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Technische Universität München - Lehrstuhl für Verkehrstechnik
Univ.-Prof. Dr.-Ing. Fritz Busch
Arcisstraße 21 80333 München, www.vt.bgu.tum.de
MASTER’S THESIS
Mobility Apps – Users’ Needs, Data Requirements
Author:
Gabriel Hernandez Valdivia
Mentoring:
Dr.-Ing. Matthias Spangler (TUM)
M.Sc. Maximilian Schreieck (TUM)
M.Sc. Sabine Krause (TUM)
Date of Submission: 2016-07-10
Technische Universität München - Lehrstuhl für Verkehrstechnik
Univ.-Prof. Dr.-Ing. Fritz Busch
Arcisstraße 21 80333 München, www.vt.bgu.tum.de
MASTER’S THESIS
of Gabriel Hernandez Valdivia
Date of Issue: 2015-12-15
Date of Submission: 2016-07-10
Topic: Mobility Apps – User Needs’, Data Requirements
Smartphone applications have become one of the main sources of information in the
daily life of many people. Through the so called apps, information from various domains
is transferred to the broad public. Many applications exist for mobility services helping
the user to plan routes, buy tickets, get the traffic state or order a taxi, among others.
The providers of Apps can be diverse, from mobility providers offering Apps for their
provided service, to private companies gathering information from various sources and
providing them in form of an App.
Within this thesis, the various existing services in mobility Apps shall be analyze. A
particular focus within the analysis shall be put on the data sources used to offer the
service. Within a survey, the user needs shall be addressed. Here it shall be found out,
which information is most important to the user and which quality they are expecting
the data to have. Furthermore, it shall be evaluated to what extent the required
information can also be obtained from sources the public authorities collect for the
purpose of traffic management.
The following aspects have to be carried out within the Master’s thesis:
Literature Review on existing mobility apps
Analysis of the data used to provide the service.
Design and conduct of a survey to analyze the user needs.
Evaluation of the survey and analysis of the data and data quality needed to
provide a service, taking in to account the desires of the respondents from the
survey.
Feasibility study for the realization of an appropriate mobility App using data
from public authorities.
The student will present intermediate results:
1. Fifth week
2. Tenth week
3. Fifteenth week
4. Twentieth week
The student will present intermediate results to the mentor(s) (Dr.-Ing. Matthias
Spangler (TUM), M.Sc. Maximilian Schreieck (TUM), M.Sc. Sabine Krause (TUM)) in
the fifth, tenth, 15th and 20th week.
The student must hold a 20-minute presentation with a subsequent discussion at the
most two months after the submission of the thesis. The presentation will be
considered in the final grade in cases where the thesis itself cannot be clearly
evaluated.
____________________________
Univ.-Prof. Dr.-Ing. Fritz Busch
Abstract
Abstract
This study identify which are the services and data used by smart infrastructure and mobility
Apps. It researches which goals should be addressed by the Apps for mobility. A survey is
applied to identify the satisfaction of the people of the current services and their needs. As a
result, propose of a platform of modular services to address the right goals, needs, preferences
using several data sources and the capabilities of the IT developers.
The market for intelligent mobility is growing intensively. The data collected by Smart
Infrastructure is highly precise but not widely used. The mobility Apps provide a high quality
and deregulated variety of services but struggle to get the data they need. The users expect
an even larger variety of services and better quality. The smart infrastructure is capable to
collect and provide the data the Apps needed. The Apps developers can provide the quality of
services the users want. A Platform offering modular services will convey the capabilities of
both. This will enable the industry to develop solutions closer to both, an individual and a global
optimum.
Contents
Contents
Abstract ................................................................................................................................ 4
Contents ............................................................................................................................... 5
Introduction .......................................................................................................................... 1
1 Methodology ................................................................................................................. 3
2 Literature Review .......................................................................................................... 6
b. Smart Infrastructure ................................................................................................ 9
c. Technologies Based on Smartphones .................................................................. 11
d. Evaluation of Smartphone based Technologies vs Smart Infrastructure .......... 12
e. Societal Implications ............................................................................................. 13
3 Mobility Services and Data ........................................................................................ 16
f. Smart Infrastructure ................................................................................................... 17
3.1.1 Services Provided by Smart Infrastructure ........................................................ 17
3.1.2 Data from Smart Infrastructure .......................................................................... 24
g. Technologies Based on Smartphones .................................................................. 28
3.1.3 Smartphone based Services .............................................................................. 30
3.1.4 Smartphone based Services Data Sources ....................................................... 38
4 Goals of Services for Mobility .................................................................................... 42
h. Goals of Organizations .......................................................................................... 42
i. User Preferences ........................................................................................................ 51
4.1.1 Theory for Statistical Analysis ............................................................................ 52
4.1.2 Survey Design ................................................................................................... 58
4.1.3 Application of Survey......................................................................................... 62
4.1.4 Results of Survey ............................................................................................... 65
5 Services and Data. Smart Infrastructure vs Smartphone Apps ............................... 76
6 Solution: A Platform of Modular Services ................................................................. 81
Conclusions ....................................................................................................................... 89
List of References .............................................................................................................. 92
List of Abbreviations .......................................................................................................... 99
List of Figures .................................................................................................................. 101
Appendix A: App Service Analysis .................................................................................. 103
Contents
Appendix B: Survey Templates ....................................................................................... 105
Appendix C: Survey Website .......................................................................................... 112
Appendix D: Programming Code in R for Linear Regressions ..................................... 115
Appendix E: Results of Linear Regressions ................................................................... 116
a. Service: Journey planner: Reg_1a ....................................................................... 116
b. Service: Navigation: Reg_1b ................................................................................ 116
c. Service: Purchase of tickets: Reg_1c ................................................................. 117
d. Service: Charging stations: Reg_1d .................................................................... 117
e. Service: Parking assistance: Reg_1e .................................................................. 118
f. Service: Vehicle/ride share: Reg_1f ......................................................................... 118
g. Service: Additional information: Reg_1g ............................................................ 119
h. Service: Taxi hailing: Reg_1h ............................................................................... 119
7 Appendix F: Goals addressed by declared service responses .............................. 121
8 Declaration concerning the Master’s Thesis / Bachelor’s Thesis ......................... 123
Introduction
1
Introduction
The contemporary world is rapidly changing; the mega trends of urbanization, climate change,
globalization and digitalization shape the world and our lives today. Near to seven billion people
live on our planet, 54% of them live in cities and this share will rise to 66% by 2050 in a world
of 9.7 billion Humans (United Nations 2014). Urban Mobility becomes a key aspect to ensure
life quality and efficiency of the forthcoming urban agglomerations. The rise in the average
temperatures between 2.2 °C and 8 °C is estimated by then (IPCC 2013). Big challenges are
ahead for all the world and mobility solutions are on the rise joining both, the real and the digital
world. The rise of the market size for Intelligent Mobility is calculated from its current annual
value of € 167 billion to € 1.07 trillion in 2025, six times bigger. This indicates a vast space for
opportunities in this field (Catapult Transport Systems 2015).
Before 1960’s, construction based solutions were the most popular way to manage with traffic
issues within cities and roads. Then in the 1970’s traffic control and traffic management
initiatives were implemented by the cities, called Intelligent Transportation Systems (ITS).
Recently, the automatization of the vehicles is on the rise. However, the improvements of traffic
flow in most cities can reach around 10% of improvement based on current ITS approaches
(Larson and Chin 2016). Therefore, it is needed to apply more integral strategies from different
natures to improve the mobility.
Information Technologies have emerged as new tools to develop innovative mobility services,
providing services closer to the users while facilitating the data collection and analysis. The
age for the Smart City is now, in which cities are connected by means of digital technologies,
their physical and human resources to augment their capabilities and meet stablished goals
(European Laboratory for Urban Innovation 2016). Sadly, the goals are not often stated and
met. A new age of mobility services is happening, a large, diverse and unorganized variety of
them. The specialists have to assure that these are oriented to optimize Mobility concerning to
both a global and individual optimum.
Smartphone applications (Apps) have become one of the main sources of information in the
daily life, nowadays, around 65% of the population in industrialized countries and 45% for
developing countries own a smartphone (PEW Research Center 2015, World Economic Forum
2016). Now, many Apps exist for mobility services. The existing service system categories and
the service modules of the most popular Apps are identified in this research. Then It has been
found that the Apps offer similar services than the ITS but rarely use the data collected with
Smart Infrastructure and prefer to generate and use their own data. The goals of the parties
involved in these services are reviewed, showing that the primary needs are out of their focus.
This leads to the development of a platform for modular services where all the parties can offer
and find modules or data sets to generate more digital solutions for mobility.
This research starts explaining the methodology to be followed which is under the framework
of Design Science Research (Hevner, March et al. 2004). Then reviewing what is being said
Introduction
2
in the literature about smart infrastructure, smartphone Apps for mobility services and social
conditions for both. Afterwards it goes deep in identifying which services offered by the current
ITS and which data they are using. Then the attention is paid to the already existing Apps.
Their services, growth and economics are to be compared. This puts lights onto what kind of
data is used and how often the data generated is used by smart cites. The data that is being
used by both, the smartphones and “in Situ” technologies is to be reviewed in terms of type of
data, collection method, ownership of data and costs.
With a top down approach, reviewing the goals for mobility and the goals for development of
different organizations. Then it identifies the user’s needs for smart mobility services and
understand the goals to follow from the bottom to the top. Then it contrasts both to identify
relevant goals. How these goals are being met by the current services is to be reviewed. The
generation of Apps and digital services in general has been a very dynamic and plural scene.
Therefore, this work proposes how to develop more solutions and reach a bigger success in
the sphere of mobility.
The outline of this work is as follows: In Chapter 1 the methods used to structure the whole
study and each chapter are to be explained. Design Science Research, Qualitative and
Quantitative Literature Reviews, framework for service systems, Descriptive Statistics and
Statistical models. Then, Chapter 2 presents the literature review. Chapter 3 contains the
analysis of services and data used for both Smart Infrastructure and Smartphones. Chapter 4
reviews what goals, organizations related to mobility declare to follow and what the people’s
satisfaction of Apps is. In chapter 5 Both data and services environments, Smart infrastructure
and Smartphones are evaluated. In chapter 6 a solution on how to connect the goals to the
Apps and the Apps to the data is to be presented, with a platform to trade data and digital
mobility services.
Methodology
3
1 Methodology
This research is written following the design science research approach. The task set is to
understand, explain and invent. The focus is also to provide guideline material for further
innovations evading random inventions.
To begin with, the design science approach used differs from the behavioral science. The
behavioral science develops and verifies theories or makes predictions. Its goal is to find the
truth. Design science research goes one step further; it finds the utility of the knowledge in the
real world. It seeks to extend the boundaries of human and organizational capabilities by
creating new and innovative artifacts. The result of the research in design science can be
presented in the form of a process (set of activities) or a product (an artifact), either physical
or digital (Hevner, March et al. 2004).
Peffers, Tuunanen et al. (2007) suggest a six steps procedure to follow design science
research. In the first one, the motivation and the problems are outlined. The second one
defines the objectives of a possible solution. The third step, designs and develops the artifact
or process. In the fourth one, the ability of the artefact to solve the addressed problem is
demonstrated. In the fifth step, the developed artefact is evaluated. Additionally, it is possible
to go back to the second and third steps to improve the artefact. The last step presents the
developed artefact an interested/target audience.
The methodologies used in each chapter are mentioned in their general terms and described
in this section. This work follows the first step, identifying the objectives for a solution in Chapter
2 Literature Review. For the second step, to define the objectives of a possible solution the
Chapters 3 Mobility Services and Data, 4 Goals of Services for Mobility
and 5 Services and Data. Smart Infrastructure vs Smartphone The work goes to the third
step, proposing solutions in Chapters 6 Solution. The evaluation of the artifact and the
presentation of the artifact to the audience, steps five and six, are out of the scope of this work
and can be covered by future research.
A detailed description and how they are applied is presented in each chapter of the thesis, in
this section these are mentioned to work as a roadmap. For Chapter 2 Literature Review, the
method by Webster and Watson (2002) is to be followed. Chapter 3 contains two sections.
The first one dedicated to Smart Infrastructure technologies using the procedure followed by
Leduc (2008) and the second Technologies Based on Smartphones follows the methods to
identify services and categorizes modules proposed by Dörbecker and Böhmann (2015) as
well as by Lacity, Khan et al. (2010).
Chapter 4, reviews different sources of goals for mobility services and summarizes relevant
concepts in to the Maslow’s Pyramid of human needs (Kellingley 2016) to set priorities.
Chapter 5 uses the method presented in the publication of the Leduc (2008) to compare
smartphone based and smart infrastructure services.
Methodology
4
Chapter 3, Literature Review follows the method proposed by Webster and Watson (2002),
popular in the field of Information Sciences. Their method aims to create a bold understanding
of the existing literature in a quantitative and qualitative way. It helps to identify the upsides,
drawbacks and gaps of the current literature in a structured way. It indicates how to select
relevant papers, guides the review in a concept-centric way instead of an author centric one,
and synthetizes quantitative and qualitative results (Webster and Watson 2002).
To identify which papers are to be considered in the review, Webster and Watson (2002) firstly
suggest searching for the keywords in the most significant contemporary journals and
conference proceedings. Then they suggest identifying the most relevant articles and tracking
their citations. From these citations, the papers are taken to a second selection. To organize
the review in a concept-centric way the researcher builds a concept matrix listing the key
concepts of their work (Figure 1). After reading each work, the concepts are identified and
marked in the matrix. The concepts can be grouped or cross-categorized, so different terms or
focuses can be included in broader meanings.
Figure 1: Concept Matrix augmented with units of analysis (Webster and Watson 2002).
In section f the analysis of the services and data sources for Smart Infrastructure is developed
under the structure proposed by Leduc (2008). It identifies the capacities, limitations and costs
as well as its market development.
For section g, three methods are applied to analyze both the Smartphone based services and
the Smartphone based Services Data. Firstly, relevant Apps are identified. Secondly, their
services and modular services, and then they are coded in to existing categories. Finally, the
data sources that Apps use are identified following the same procedure. The key service
modules of service systems for urban transportation are identified, according to (Balzert 2009)
whose work states that module elements are strongly interrelated but only weakly interrelated
with elements outside the module. Then the service’s modules considered are the part of
services that generate a distinct value for the user. For the coding, the iterative coding process
by (Lacity, Khan et al. 2010) is followed and the services’ modules are categorized according
to the existing mobility services framework of Dörbecker and Böhmann (2015). The same
process is followed to identify the data source, checking license files and general terms and
conditions of the Apps.
Methodology
5
Chapter 4 focuses on what the mobility services are addressing. The goals of the Apps are
reviewed in a two sided way, bottom-up and top-down approaches. Firstly, checking what are
the relevant organizations and how they orient their efforts to mobility using a top-down
approach. Then, the user’s preferences are checked by a bottom-up approach. Concepts
taken from both will be summarized as (Kellingley 2016) indicates and the match of both
viewpoints is to be found.
To identify the service satisfaction degree of the users, an online questionnaire of stated
preference of discrete choice is applied. This considers the frequency of usage and the
smartphone Apps knowledge of the users. For the user’s further needs and preferences, the
questionnaire asks for a revealed preference of services. The application followed the
procedure of the evaluation criteria of the Committee on the Use of Humans as Experimental
Subjects (COUHES 2015) of the Massachusetts Institute of Technology (MIT). In which, the
biasing, organizational formalities and data privacy are controlled. The data collected is
analyzed with descriptive statistics and statistical modeling using linear regression for
categorical values (Bruin 2006).
Chapter 5 conveys end evaluates the evidence collected in the previous chapters, similarly as
in the publication of Leduc (2008). The findings of these investigations are conveyed in to
Chapter 6 to the definition of Solution. The methods used assure the scientific approach of this
work.
Literature Review
6
2 Literature Review
This chapter deals with the literature used to develop this work, it dwells on the topics covered
by the sources, provides the results of quantitative and qualitative analysis made on the bases
of articles and papers that convey information about recent progresses in the area of mobility
Apps.
The literature review uses the procedure of Webster and Watson (2002) to identify the open
issues of future research in the fields of mobility Apps. The scope of this research is illustrated
in Figure 2. There are many smartphone applications on the market; this work focuses on
those for mobility services. There are also many Apps for mobility services, i.e. freight
transport, oil prices, float managers. This focuses only on mobility for people, not on their
technical details but rather on their services from the outside, identifying modules of a service
system, coding and comparing them with the services provided by the traditional Intelligent
Transportation Systems (ITS). The data sources are identified for both Apps and ITS services.
The data can be collected with the smartphone, with Smart Infrastructure or with other
technologies such as Floating Car Data (FCD). This research focuses on only smartphone
based vs Smart Infrastructure based services.
Figure 2: Scope of this research marked as the arrows
An all-field-search (title, abstract, keyword, references) was executed with the two queries on
the libraries of the web of science, science direct and google scholar in the fields of
Engineering, Computer Sciences and Environmental Science from 2005 until February 2016.
In the first query” Apps AND (Mobility OR Commuting OR Traffic)” were searched for, resulting
in 27 hits. After reviewing these publications only four were considered relevant, therefore a
second query was necessary. The second query was “apps OR smartphone AND (Mobility OR
Commuting OR Traffic)” resulting in 125 hits; in total from both searches 152 papers were
found. After scanning them, 49 papers were selected. There were 43 scientific papers and six
white papers produced by governments, consultants or think tanks. No additional search for
their references was performed since 49 articles were extensive enough literature search.
Literature Review
7
Another source of literature was that of the mentors of this study, suggested seven additional
papers, adding up to the total of 56 sources. Additionally, other articles were used as sources
to enrich the scientific argumentation. These proved to be relevant for this research in punctual
arguments rather than to orientate the research line. Therefore, these are integrated in the
content rather than in the literature review.
The most relevant literature items were produced after 2008, surely after the invention of the
iPhone and the “app store” by Apple in summer of 2008. Research started in 2005, focusing
on the location awareness estimated with the phone network signals, but it will not be
considered in this study since it was not an App. The number of publications has grown
significantly in the last years. At the beginning of 2016, January and February, almost the same
amount of papers were produced as in the year 2014, as it can be seen in Figure 3. According
to Edmondson and McManus (2007) who evaluate the maturity of research fields, this increase
in the studies of Apps for mobility shows that the topic is rapidly evolving from a nascent to an
intermediate field of research.
Figure 3: Growth of publications in the topics mobility and Smartphone Apps.
The main relevant topics in the scope of this study are:
a. Technologies based on data from infrastructure sensors
b. Technologies based on data from Smartphones
c. An evaluation among the two of them
d. Societal aspects
An article was considered relevant when it covered widely one of these four dimensions and
had a strong connection with another two. 16 works were considered relevant, none of the
papers contained all the concepts searched. From this 16 articles, this study reviewed how
they covered the aspects of kinds of data, data collection, services for users and a market
outlook of technologies based on both sensor’s data and smartphone data. Then for the
evaluation it was reviewed the coverage, quality/accuracy, modularization, costs and the
services they provide. For the social aspects, it was reviewed the social expectations, how the
people reacts to the services, the goals and data ownership. In average the relevant papers
covered the 39% of the topics, the best matches had 59% of correspondence with the target
topics and none of the papers covered all of the studied topics. These numbers show how new
the research in this field is and how spread the literature is. Nearly one third of them spoke
0
5
10
15
20
25
2008 2010 2011 2012 2013 2014 2015 2016
Am
ou
nt
of
pu
blic
atio
ns
Literature Review
8
mainly about the systems using the data from the infrastructure sensors, half of them described
the smartphone based services, one third discussed a comparison of both systems and less
than the half of them emphasized social aspects (Figure 4).
Figure 4: Topic coverage of the literature review
The least covered topics can be seen as opportunities for future research. In this research,
more comparisons and evaluations between both groups of services based on smartphone
and Infrastructure data, arise as topics with vast opportunities.
Figure 5: Coverage of relevant topics per author
DimensionResearches
analyzingSub-Dimension
Researches
analyzing
1.- Kinds of data 5
2.- Data collection 5
3.- Services for users 5
4.- Market outlook 4
5.- Kinds of data 9
6.- Data collection 7
7.- Services for users 11
8.- Market outlook 7
9.- Coverage 5
10.- Quality/Accuracy 5
11.- Modularization 5
12.- Economics 3
14.- Services 8
15.- People reacting to the services 6
16.- Social expectations 4
17.- Goals 6
18.- Data ownership 11
8
11
Evaluation
Social
Topic coverage by the relevant literature
5
11
Smart
infrastructure
Technology
based on
smartphone
data
AuthorTotal
coverage
Kin
d o
f D
ata
Dat
a
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ect
ion
Se
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es
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use
rsIn
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/
Mar
ket
Kin
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f D
ata
Dat
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ion
A
pp
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ns
and
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Ind
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Mar
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Qu
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Acc
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Serv
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Pe
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Soci
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exp
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Go
als
Dat
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ipLeduc, 2008 59%Barbaresso et al., 2014 29%Yujuico, 2015 47%Do et al., 2014 24%Lee and Gerla, 2010 53%Herrera et al., 2010 35%Khoo and Asitha, 2016 24%European Committee for
Standarization, 200224%
CATAPULT Transport
Systems, 201559%
Antoniou et al., 2011 59%Zegras et al., 2015 24%Marchetta et al., 2015 41%Sassi et al., 2014 47%Pflügler et al. 2016 24%Krcmar and Schreieck, 2016 47%Baxandhall et al., 2013 29%
Social aspectsEvaluationData from
infrastructure
Data from
smartphones
Literature Review
9
None of the sources covers all of the topics at once and, none of them applies a quantitative
literature review; these facts prove the need of more quantitative approaches for research in
ITS than the traditional scientific methods and the integration of market- and technology
oriented approaches. The content of the reviewed literature was overviewed and recorded in
Figure 4, Figure 5 and Figure 6 and is explicated in the following section.
Figure 6: Coverage of topics per author aggregated
b. Smart Infrastructure
The topic of “Smart Infrastructure” is also mentioned in the literature as Intelligent
Transportation Systems, ITS, Smart Infrastructure, Advanced Traveler Information Systems
(ATIS), Infrastructure Sensors, “In sitú” technologies, and Data for Variable Messages (VMS).
It covers the kind of data, the technology used to collect it and the applications and services
provided to the commuters. Only five of the relevant works reviewed referred directly to it. The
difference in coverage of literature on smartphone data than on “in sitú” technologies can be
caused by the search procedure. The queried keyword was Mobility Apps, and the most of the
Literature Review
10
results were researches in computer sciences and very few in ITS, traditional ITS rarely
focuses on Smartphone apps, arising the question if they should, and if the Apps can become
part of the ITS.
The kind of data collected by means of the infrastructure sensors is discussed in five articles
of the literature review. The study of Leduc (2008) performed a full overview of the different
sensors embedded in the infrastructure by 2008. Different data formats and the ways it is used
and collected is also presented in the work. Lee and Gerla (2010) investigated how the location
of relevant data collected from infrastructure sensors can be shared using the communication
devices on cars. It developed a Vehicular Sensor Network (VSN) Platform to sense events
from infrastructure sensors, save them in the car’s local storage, process them and transmit
them either to other cars or to their infrastructure items. Zegras, K. Butts et al. (2015) identifies
the data relevant for transport and the barriers of its usage in development of new technologies.
Antoniou, Balakrishna et al. (2011) review the capabilities of the data related to transport
collected by sensors and the possible merges of it for improving services. Krcmar and
Schreieck (2016) points out all the Apps in their reviews that use the data collected from
sensors in the infrastructure. The different kinds of data and their capabilities are researched,
but only for a few articles, five out of 16 already selected as relevant works out of 159. More
research is needed for them, within the scope of this research.
The data collection technologies and techniques were mentioned by five of the selected
publications. A full review of the technologies already available was performed by Leduc
(2008), Barbaresso, Cordahi et al. (2014), Antoniou, Balakrishna et al. (2011) and Herrera,
Work et al. (2010), while Lee and Gerla (2010) focused only on Vehicular Sensing Networks,
combining data collected and processed from cars with the data collected by embedded
sensors. Similarly, with the previous sub-dimension, from 159 articles filtered in to 16, only five
of them in this topic, denote a separation between Apps research and ITS.
Only five of the authors reviewed the applications and Services created with the sensors
embedded in the infrastructure. The Leduc (2008) present a state of the art of what was being
provided in 2008 while the US ITS Plan by Barbaresso, Cordahi et al. (2014) suggest what the
future of ITS will look like for the next five years in the USA. Yujuico (2015) did a historical
overview of the traffic management initiatives in Manila, such as Radio broadcasting and
Variable Message Signs and compared them with a new App they developed. Lee and Gerla
(2010) reviewed trends of vehicular sensing applications of traffic flow estimation, urban
surveillance, vehicular safety warning services, road quality monitoring and location aware
micro blogging (threads as comments in websites). European Committee for Standarization
(2002) focuses on establishing various goals and objectives for mobility services as such
considering both of the data sources. Antoniou, Balakrishna et al. (2011) reviews the
technologies that can be provided for traffic management and public transport travelers and
classify them according to the type of data used. This research uses this knowledge to build
upon it.
Literature Review
11
An important aspect of the technology life is the industry and market outlook, but only four of
the authors drew their attention to it, 25 of the total reviewed. Leduc (2008) analyzes the
development of the market of such technologies and estimates how they will be influenced by
the emerging of technologies for smartphones. Barbaresso, Cordahi et al. (2014) also worked
on the trends in research and development of ITS to switch the orientation of the Government
of the United States into the implementation of ITS. Antoniou, Balakrishna et al. (2011) even
evaluates the level of maturity of the programs. The low coverage of this field might the
technical background of the authors or novelty of the area in technology. Therefore, this
research will also focus on the attempts to connect industry and market concepts.
c. Technologies Based on Smartphones
11 of the publications observed technologies based on the data from smartphones considered
the services provided with data from the smartphones and they also mentioned in the literature
as Floating Phone Data, Floating Cellular Data, or even sometimes Floating Car Data, outlining
that it comes from smartphones.
Nine of the publications addressed the kind of data collected by smartphones. Leduc (2008)
reviews the kind of data a smartphone can collect and its applications. Lee and Gerla (2010)
researches how to collect and share data for specific applications as traffic flow prediction,
urban surveillance, warning services, road quality monitoring and location aware micro-
blogging. (Sassi, Marco Mamei et al. 2014) Traces the path from smart mobility services to the
data needed indicating the different types of them. Zegras, K. Butts et al. (2015) makes a full
review of the data needed for transportation purposes which is collected by smartphones.
Antoniou, Balakrishna et al. (2011) addresses mainly traffic data collected by any mean,
smartphones included, while Zegras, K. Butts et al. (2015) are more innovative creating an
application to collect data of different natures by means of questionnaires spatio-temporally
referenced. Marchetta, Natale et al. (2015) propose an architectural design able to collect,
update and process heterogeneous data from sources as smartphones or probe vehicles to
measure different actors of the public space like public and private vehicles, pedestrians and
infrastructure. Krcmar and Schreieck (2016) investigates the variety of data sources needed
for smartphone based services and their origin.
Smartphones are only one device full of sensors, so they can collect a wide variety of data.
Besides, a full arrange of collection techniques increase the kind of data they can collect,
summing up to a wide variety of variables to measure. Therefore, they are essential for this
study. The technologies and techniques of data collection have been addressed in the
literature, seven of the reviewed researches spoke about them. Leduc (2008) and Antoniou,
Balakrishna et al. (2011) consider smartphones as a simply another kind of sensor, which can
collect a wider variety of data and also reviews the different technologies to transmit them as
CDMA, GSM, UMTS and GPRS. The report Zegras, K. Butts et al. (2015) names five primary
mechanisms for data creation with smartphones: manual collection, open and closed crowd-
sourcing, sensor derived and generated by a service provider, reviewing them. Many of the
authors concentrated on spatio-temporal information only and traffic-related issues like
Literature Review
12
Herrera, Work et al. (2010) and Lee and Gerla (2010). The research of Marchetta, Natale et
al. (2015) is wider, incorporating data infrastructure, pedestrian and public services, when
Zegras, K. Butts et al. (2015) evaluates a new data collection technique called “Flock sourcing”,
in which, not a crowd but a trained staff has collected specialized data. Data collection
techniques are beneficial for the smartphone and will be taken into consideration as a provided
service.
The data takes a long way from the collection to the customer usage as a service, about two
thirds of the reviewed literature focuses on this path as well. The other focus only on one part
of it, either on the data or on the data analysis or on the services. In the research of Yujuico
(2015) an App for the public transport of Manila was developed. Do, Dousseb et al. (2014)
developed a location predictor for the users; Lee and Gerla (2010) also describe traffic
services. In their traffic application, Khoo and Asitha (2016) analyze how the users perceive
the current traffic and which data is needed for that. Antoniou, Balakrishna et al. (2011) look
for the generation of more traffic services from different kinds of data and collection techniques
that can possibly be used. European Committee for Standarization (2002) controls the
provision of these services with regulations. Zegras, K. Butts et al. (2015) and Krcmar and
Schreieck (2016) review which are the existing services provided through smartphone Apps.
Marchetta, Natale et al. (2015) provide different transport services through a Map while
Baxandhall, Dutzik et al. (2013) and Krcmar and Schreieck (2016) look for the future of the
services provided, proposing new options and digital architectures.
Smartphone based technologies might play a very significant role in the current industry and
market of mobility, nevertheless, only seven, less than the half of the authors paid attention to
this situation. Leduc (2008), Zegras, K. Butts et al. (2015), Baxandhall, Dutzik et al. (2013),
Krcmar and Schreieck (2016) and Antoniou, Balakrishna et al. (2011) reviewed the current role
and the future of these technologies, market players and barriers for development. Barbaresso,
Cordahi et al. (2014) declare how they will be fostered and incorporated in governmental plans,
when Yujuico (2015) has analyzed the market conditions of the traffic App they develop.
d. Evaluation of Smartphone based Technologies vs Smart
Infrastructure
A comparison between the technologies based on Smartphone and the technologies based
on in sitú sensors was performed by 34 % of the references in terms of coverage of sensors
needed, costs, quality and accuracy of the information, the capabilities to provide services as
well as their modularization and public reaction to these services.
The most of the literature shows awareness of the popularity of the smartphones or the sensors
in the infrastructure, nevertheless only nine authors, roughly the half, focus on a comparison
of the amount of sensors or smartphone needed to provide a service. Yujuico (2015), Do,
Dousseb et al. (2014) and Lee and Gerla (2010) focused only on the coverage needed to
provide the information of their initiative, and they agree that it is cheaper to use smartphones
Literature Review
13
and they need less devices than the infrastructure sensors. Antoniou, Balakrishna et al. (2011)
make a wider review, comparing the sensors necessary for traffic information with the
smartphones needed for those aims and propose data merging methods.
The coverage is useful only if it is equal in the quality of the service through the accuracy of
the data but even less authors mentioned it, five in total. A wider perspective was viewed by
Antoniou, Balakrishna et al. (2011), Lee and Gerla (2010), Marchetta, Natale et al. (2015) and
Herrera, Work et al. (2010) who also evaluated the quality of the services they developed.
To offer a service is good, to offer many services is even better, but to provide full services
and partial services is much better, therefore the modularization of the services is a valuable
strategy. From the selected literature only five articles reviewed its modularization. This is a
new trend in digital services and therefore only the newest researches addressed it. They
identified the main service and their service modules provided via smartphone Apps.
Marchetta, Natale et al. (2015), Sassi, Marco Mamei et al. (2014) to develop their own service,
a mobility map and a rideshare system; Pflügler, Schreieck et al. (2016) proposed a modular
platform of data and services and Krcmar and Schreieck (2016) identified existing modular
services in the App world.
The economic aspects of the technologies will keep it alive in the market and develop further
but only three of the selected works compared the costs of the technologies based on
smartphones and the traditional methods. Only Herrera, Work et al. (2010), Antoniou,
Balakrishna et al. (2011) and Leduc (2008) provided a comprehensive check. They agree with
the argument that smart infrastructure is very expensive and that this data can be collected via
smartphones. Nevertheless, the infrastructure was already acquired in many cities and should
be used.
The services provided by technologies based smartphones or by traditional technologies were
compared by eight selected articles. The researches who developed technology, Yujuico
(2015), Lee and Gerla (2010), Khoo and Asitha (2016) and Marchetta, Natale et al. (2015)
compared their smartphone based services with the analogue ITS one. And only the newest
wide reviews by Pflügler, Schreieck et al. (2016) and Krcmar and Schreieck (2016) looked at
both, services provided using smartphone data and using data collected by sensors and
evaluating them. Baxandhall, Dutzik et al. (2013) compares both and assess their effects on
the behavior of the population in the USA while driving.
e. Societal Implications
The social implications reviewed in this work is the reaction of people to the services, their
expectations, if the work addressed specific goals related to social benefit and owners of the
data. Only 11 out of 16 authors addressed topics of: People reacting to the service, social
expectations, Goals and Data Ownership.
Literature Review
14
Although this topic is strongly related to the mobility of people, only six articles of the literature
depth in the people’s reactions to the services. Khoo and Asitha (2016) applied a questionnaire
and checked the people’s preferences for services on their traffic App, Yujuico (2015) as well
as Sassi, Marco Mamei et al. (2014) monitored the implementation and diffusion of his App to
evaluate its acceptance to show its benefits over traditional technologies. Krcmar and
Schreieck (2016) and Baxandhall, Dutzik et al. (2013) focused on the most popular Apps to
analyze them, considering frequency of usage.
The social expectations, what people wait from the services, were the least covered topic, only
four out of all the authors wrote about them. Khoo and Asitha (2016) asked the public which
features they would prefer for a new driving navigator. Sassi, Marco Mamei et al. (2014) also
addresses the individual mobility needs with urban-scale mobility issues. European Committee
for Standarization (2002) encourages the transport planners to create indicators addressed to
the people’s preferences. Zegras, K. Butts et al. (2015) made a research on expectations as
well, but only regarding the security. Therefore, this literature will reach the customers and
inquire their demands.
The discussion at the highest level is on which goals to address. It enables the research to
develop the solutions that are more robust. It should be presented in every research but it has
been presented only in five of them. The (European Committee for Standarization 2002) is a
guideline that explains how to measure the quality of a transport network, stablishing a goal,
target, indicator framework. The researches based on locations like the Barbaresso, Cordahi
et al. (2014) and Baxandhall, Dutzik et al. (2013) reviewed the technologies needed for the
development of Digital Mobility services in the USA. Yujuico (2015) developed his solution
towards economy, effectiveness, efficiency and equity. Zegras, K. Butts et al. (2015) also
reviews the questions of what the technologies are solving, and what they should be solving.
Sassi, Marco Mamei et al. (2014) understands mobility as a socio-technical system and orients
the solution of the research towards specific goals.
Within the social aspects, the possession of the data collection is an
aspect related to governance and privacy rights, therefore, how the
data will be owned is a critical aspect. 11 articles out of the all literature
overviewed addressed this problem. The wide reviews of Leduc
(2008), Barbaresso, Cordahi et al. (2014), Zegras, K. Butts et al.
(2015) and Krcmar and Schreieck (2016) discuss how the data is
stored, shared and governed. Pflügler, Schreieck et al. (2016)
indicates how to store and regulate the access to the data their
platform offers. Zegras, K. Butts et al. (2015), Marchetta, Natale et al.
(2015) Sassi, Marco Mamei et al. (2014), Herrera, Work et al. (2010),
Do, Dousseb et al. (2014) and Yujuico (2015) reported where their
technology stores the data and under which legal terms.
Figure 7: Boxplot measuring how many articles (16) address the target topics
Literature Review
15
The 18 different topics were addressed by few authors, the half of the research covered
between five and 7 topics. As it can be seen in Figure 7, the most complete works covered 11
topics and the least complete covered only three of them. This shows still a low coverage of
the selected topics in the literature and they are mostly split and heterogeneous, indicating the
opportunities for this research to provide new knowledge.
The literature documented has proven to be vast and highly specialized that it has lost its
connection from other natures of knowledge. The pursue of improvement has often overseen
other discussions like if their approach is the best way to pursue goals of mobility or what does
its customers think of it. It has focused on the improvement of the technology, losing its goals.
Besides, the limits between services using the data from sensors and smartphone data are
blurry and unclear. Therefore, this study reviews the connection of Information Technologies
with ITS and Mobility Management. This research will explain and understand what is the
environment where mobility Apps are developed.
Mobility Services and Data
16
3 Mobility Services and Data
The digitalization trend has strongly influenced the human mobility. Technology has always
been oriented to improve mobility; at the beginning with mechanic machines then with
construction solutions, after that electronic devices were applied. Now is the century of the
digital revolution and this Chapter explore these changes. At the beginning, the relevant terms
are introduced before presenting smart infrastructure and Smartphone based services.
Products are solutions where the user and provider have only a one-time interaction, that can
be either an object or an action. Services encompass a set of interactions within users and
providers. For the case of digital mobility services, the services enable a person to get from A
to B most efficiently.
Services and service systems have emerged as key concepts as technology enables service
systems in different industries as transportation, manufacturing and healthcare i.e. Service
systems enable co-creation of value and therefore leverage the capability the transportation
systems and their different applications. Böhmann, Leimeister et al. (2014) call for future
research on service systems engineering, mentioning mobility as one promising area where
services can generate significant benefits.
Before 1970’s, construction based solutions like overpasses, bridges and new roads, were the
most popular way to manage with traffic issues within cities and roads. Then in the 1970’s ITS
started to be implemented by city administrators to improve safety, efficiency and convenience
of surface transportation. ITS monitor and analyze traffic data and influence the traffic flow
using a variety of measures such as dynamic traffic signs, lights and automatized vehicle
moves. ITS have a positive impact on energy, safety and environmental benefits as well as
construction based measures. However, the improvements of traffic flow in most cities can be
only around 10% based on current ITS approaches such as sensing the road network,
predicting the demand and controlling traffic signaling (Larson and Chin 2016), therefore it is
needed to apply different strategies from different natures to improve the mobility in cities.
Information Technologies have emerged as new tools to influence traffic and improve mobility
(Wolter 2012, Khoo and Asitha 2016). An App is a piece of software that can be run on the
internet, on a computer, on a smartphone or on another electronic device, basically, it is a short
computer program (Karch 2016). ITS and IT are therefore suitable to optimize the Mobility in
cities with regards to both a global and individual optimum.
Mobility Services and Data
17
f. Smart Infrastructure
3.1.1 Services Provided by Smart Infrastructure
The interest on improving the traffic situation has been growing and the technology to improve
traffic has significantly evolved. Traffic data collection devices improved astonishingly so as
the techniques to collect the data. And after the data is collected and analyzed, what happens
next? How are the users reached? The services provided are explained in the following
section.
The “in situ” technologies are also called “smart infrastructure”, “Advanced traffic management
systems (ATMS)”, “Advanced traveler information systems (ATIS)” and “Advanced Public
Transportation Systems (APTS)”. They are a part of the ITS that are strictly embedded in the
infrastructure to provide a wide variety of services. A full presentation of them is shown in
(Department of Transportation 2009), which organizes the ITS services in the following
categories: Arterial Management, Transit Management, Freeway Management, Traffic
Incident Management, Information Management, Crash Prevention and Safety, Emergency
Management, Commercial Vehicle Operations, Road Weather Management, Electronic
Payment & Pricing, Intermodal Freight, Roadway Operations and Maintenance and Traveler
Information (Figure 8).
Figure 8: Applications overview website. (Department of Transportation 2009)
For the scope of this work only the intelligent Infrastructure methods will be taken in account,
not considering intelligent vehicles or ITS for public transport which are not dealing with the
Mobility Services and Data
18
infrastructure. The services are enlisted according to the categories as shown in Figure
9.These services require different data sources, for some of them they combine different data
sets. Others analyze the data in different ways to provide different services for instance, the
variable speed limits service use the data to monitor the traffic flow, density and speed. These
measurements are taken and considering the conditions of the road and weather data, a speed
limit can be calculated and set. These services are applied in the VAMOS project in Dresden
and will be explained in the next section.
Figure 9: Services provided by ITS. Adapted from: (Department of Transportation 2009)
Video
SurveillanceTraffic Control
Lane
management
Parking
Management
Information
DisseminationEnforcement
TrafficAdaptive Signal
ControlHOV Facilities Data Collection
Dynamic Message
Signs (DMS)
Speed
Enforcement
Infrastructure Advanced Signal
Systems
Reversible Flow
Lanes
Information
Dissemination
In-vehicle Systems
(IVS)
Traffic Signal
Enforcement
Variable Speed
LimitsPricing
Highway Advisory
Radio (HAR)
Bicycle and
pedestrianLane Control
Special EventsVariable Speed
Limits
Emergency
Evacuation
Operations
and Fleet
Management
Information
Dissemination
Transport
Demand
Management
Safe &
Security
AVL/CAD In-vehicle SystemsRide-share
Matching
In-vehicle
surveillance
Transit Signal
Priority
In Terminal /
Wayside
Dynamic Routing
scheduling
Facility
Surveillance
PlanningInternet /
Wireless/Phone
Employee
Credentialing
Service
Coordination
Remote Disabling
Systems
Video
SurveillanceRamp Control
Lane
Management
Special Events
Transport
Management
Enforcement
Traffic Ramp Metering
HOV (High
Occupation
Vehicles) Facilities
Occasional EventsSpeed
Enforcement
Infrastructure Ramp ClosureReversible Flow
LanesFrequency Events HOV Facilities
Priority access Pricing Other EventsRamp Meter
enforcement
Lane Control
Temporary Traffic
Management
Center
Variable Speed
Limits
Emergency
Evacuation
Art
eri
al M
anag
em
en
tTr
ansi
t M
anag
em
en
tFr
ee
way
Man
age
me
nt
Video
SurveillanceTraffic Control
Lane
management
Parking
Management
Information
DisseminationEnforcement
TrafficAdaptive Signal
ControlHOV Facilities Data Collection
Dynamic Message
Signs (DMS)
Speed
Enforcement
Infrastructure Advanced Signal
Systems
Reversible Flow
Lanes
Information
Dissemination
In-vehicle Systems
(IVS)
Traffic Signal
Enforcement
Variable Speed
LimitsPricing
Highway Advisory
Radio (HAR)
Bicycle and
pedestrianLane Control
Special EventsVariable Speed
Limits
Emergency
Evacuation
Operations
and Fleet
Management
Information
Dissemination
Transport
Demand
Management
Safe &
Security
AVL/CAD In-vehicle SystemsRide-share
Matching
In-vehicle
surveillance
Transit Signal
Priority
In Terminal /
Wayside
Dynamic Routing
scheduling
Facility
Surveillance
PlanningInternet /
Wireless/Phone
Employee
Credentialing
Service
Coordination
Remote Disabling
Systems
Video
SurveillanceRamp Control
Lane
Management
Special Events
Transport
Management
Enforcement
Traffic Ramp Metering
HOV (High
Occupation
Vehicles) Facilities
Occasional EventsSpeed
Enforcement
Infrastructure Ramp ClosureReversible Flow
LanesFrequency Events HOV Facilities
Priority access Pricing Other EventsRamp Meter
enforcement
Lane Control
Temporary Traffic
Management
Center
Variable Speed
Limits
Emergency
Evacuation
Art
eri
al M
anag
em
en
tTr
ansi
t M
anag
em
en
tFr
ee
way
Man
age
me
nt
Video
SurveillanceTraffic Control
Lane
management
Parking
Management
Information
DisseminationEnforcement
TrafficAdaptive Signal
ControlHOV Facilities Data Collection
Dynamic Message
Signs (DMS)
Speed
Enforcement
Infrastructure Advanced Signal
Systems
Reversible Flow
Lanes
Information
Dissemination
In-vehicle Systems
(IVS)
Traffic Signal
Enforcement
Variable Speed
LimitsPricing
Highway Advisory
Radio (HAR)
Bicycle and
pedestrianLane Control
Special EventsVariable Speed
Limits
Emergency
Evacuation
Operations
and Fleet
Management
Information
Dissemination
Transport
Demand
Management
Safe &
Security
AVL/CAD In-vehicle SystemsRide-share
Matching
In-vehicle
surveillance
Transit Signal
Priority
In Terminal /
Wayside
Dynamic Routing
scheduling
Facility
Surveillance
PlanningInternet /
Wireless/Phone
Employee
Credentialing
Service
Coordination
Remote Disabling
Systems
Video
SurveillanceRamp Control
Lane
Management
Special Events
Transport
Management
Enforcement
Traffic Ramp Metering
HOV (High
Occupation
Vehicles) Facilities
Occasional EventsSpeed
Enforcement
Infrastructure Ramp ClosureReversible Flow
LanesFrequency Events HOV Facilities
Priority access Pricing Other EventsRamp Meter
enforcement
Lane Control
Temporary Traffic
Management
Center
Variable Speed
Limits
Emergency
Evacuation
Art
eri
al M
anag
em
en
tTr
ansi
t M
anag
em
en
tFr
ee
way
Man
age
me
nt
Mobility Services and Data
19
Figure 9 (Continues): Taxonomy for classification of services provided by ITS for intelligent infrastructure.
Hazardous Material
Management
Emergency Medical
ServicesResponse & Recovery
TrackingAdvanced Automated Collision
Notification (ACN)
Early warning system for big scale
disasters
Detection Ambulances with TelemedicineResponse Management (Tracking of
emergency fleets)
Driver Authentication Emergency Vehicle Traffic light
preference
Route PlanningEvacuation and Re-Entry
Management
Emergency traveler information
Credentials administration Safety assurance Electronic screeningCarrier Operations & Fleet
ManagementElectronic funds Safety information exchange Safety screening Automatic Vehicle Location
Electronic registration / Permitting Automated inspection Clearance at national borders On-board monitoring
Weight Screening Traveler Information
Credential checking
Surveillance, Monitoring
and prediction
Information Dissemination
for AdvisorsTraffic Control Strategies
Response and Treatment
Strategies
Pavement Conditions Dynamic Message Signs (DMS) Variable Speed Limits Fixed Winter Maintenance
Atmospheric conditions Internet / Wireless / Phone traffic Signal Control Mobile Winter Maintenance
Water Level Highway Advisory Radio (HAR) Lane Use / Road Closure
Vehicle Restrictions
Toll collection
Transit fare payment
Multi use payment
Pricing
Freight tracking Asset tracking
Freight terminal processes
Drayage Operations
Freight-Highway connector system
International border Crossing
Process
Information Dissemination
for AdvisorsAsset Management Work Zone Management
Dynamic Message Signs (DMS) Fleet Management Temporary Traffic Management
Internet / Wireless / Phone Infrastructure Management Temporary Incident Management
Highway Advisory Radio (HAR) Lane Control
Variable Speed Limits
Speed Enforcement
Intrusion Detection
Road Closure Management
Pre-trip and end-route
informationTourism & events
Internet/Wireless Travel services
Information for traveling Advanced parking
Phone services
Tv/radio
Kiosks
Inte
rmo
dal
Fre
igh
tR
oad
way
Op
era
tio
ns
and
Mai
nte
nan
ce
Trav
ele
r
Info
rmat
ion
Eme
rge
ncy
Man
age
me
nt
Co
mm
erc
ial
Ve
hic
le
Ro
ad W
eat
he
r
Man
age
me
nt
E-P
aym
en
t
& P
rici
ng
Hazardous Material
Management
Emergency Medical
ServicesResponse & Recovery
TrackingAdvanced Automated Collision
Notification (ACN)
Early warning system for big scale
disasters
Detection Ambulances with TelemedicineResponse Management (Tracking of
emergency fleets)
Driver Authentication Emergency Vehicle Traffic light
preference
Route PlanningEvacuation and Re-Entry
Management
Emergency traveler information
Credentials administration Safety assurance Electronic screeningCarrier Operations & Fleet
ManagementElectronic funds Safety information exchange Safety screening Automatic Vehicle Location
Electronic registration / Permitting Automated inspection Clearance at national borders On-board monitoring
Weight Screening Traveler Information
Credential checking
Surveillance, Monitoring
and prediction
Information Dissemination
for AdvisorsTraffic Control Strategies
Response and Treatment
Strategies
Pavement Conditions Dynamic Message Signs (DMS) Variable Speed Limits Fixed Winter Maintenance
Atmospheric conditions Internet / Wireless / Phone traffic Signal Control Mobile Winter Maintenance
Water Level Highway Advisory Radio (HAR) Lane Use / Road Closure
Vehicle Restrictions
Toll collection
Transit fare payment
Multi use payment
Pricing
Freight tracking Asset tracking
Freight terminal processes
Drayage Operations
Freight-Highway connector system
International border Crossing
Process
Information Dissemination
for AdvisorsAsset Management Work Zone Management
Dynamic Message Signs (DMS) Fleet Management Temporary Traffic Management
Internet / Wireless / Phone Infrastructure Management Temporary Incident Management
Highway Advisory Radio (HAR) Lane Control
Variable Speed Limits
Speed Enforcement
Intrusion Detection
Road Closure Management
Pre-trip and end-route
informationTourism & events
Internet/Wireless Travel services
Information for traveling Advanced parking
Phone services
Tv/radio
Kiosks
Inte
rmo
dal
Fre
igh
tR
oad
way
Op
era
tio
ns
and
Mai
nte
nan
ce
Trav
ele
r
Info
rmat
ion
Eme
rge
ncy
Man
age
me
nt
Co
mm
erc
ial
Ve
hic
le
Ro
ad W
eat
he
r
Man
age
me
nt
E-P
aym
en
t
& P
rici
ng
Hazardous Material
Management
Emergency Medical
ServicesResponse & Recovery
TrackingAdvanced Automated Collision
Notification (ACN)
Early warning system for big scale
disasters
Detection Ambulances with TelemedicineResponse Management (Tracking of
emergency fleets)
Driver Authentication Emergency Vehicle Traffic light
preference
Route PlanningEvacuation and Re-Entry
Management
Emergency traveler information
Credentials administration Safety assurance Electronic screeningCarrier Operations & Fleet
ManagementElectronic funds Safety information exchange Safety screening Automatic Vehicle Location
Electronic registration / Permitting Automated inspection Clearance at national borders On-board monitoring
Weight Screening Traveler Information
Credential checking
Surveillance, Monitoring
and prediction
Information Dissemination
for AdvisorsTraffic Control Strategies
Response and Treatment
Strategies
Pavement Conditions Dynamic Message Signs (DMS) Variable Speed Limits Fixed Winter Maintenance
Atmospheric conditions Internet / Wireless / Phone traffic Signal Control Mobile Winter Maintenance
Water Level Highway Advisory Radio (HAR) Lane Use / Road Closure
Vehicle Restrictions
Toll collection
Transit fare payment
Multi use payment
Pricing
Freight tracking Asset tracking
Freight terminal processes
Drayage Operations
Freight-Highway connector system
International border Crossing
Process
Information Dissemination
for AdvisorsAsset Management Work Zone Management
Dynamic Message Signs (DMS) Fleet Management Temporary Traffic Management
Internet / Wireless / Phone Infrastructure Management Temporary Incident Management
Highway Advisory Radio (HAR) Lane Control
Variable Speed Limits
Speed Enforcement
Intrusion Detection
Road Closure Management
Pre-trip and end-route
informationTourism & events
Internet/Wireless Travel services
Information for traveling Advanced parking
Phone services
Tv/radio
Kiosks
Inte
rmo
dal
Fre
igh
tR
oad
way
Op
era
tio
ns
and
Mai
nte
nan
ce
Trav
ele
r
Info
rmat
ion
Eme
rge
ncy
Man
age
me
nt
Co
mm
erc
ial
Ve
hic
le
Ro
ad W
eat
he
r
Man
age
me
nt
E-P
aym
en
t
& P
rici
ng
Hazardous Material
Management
Emergency Medical
ServicesResponse & Recovery
TrackingAdvanced Automated Collision
Notification (ACN)
Early warning system for big scale
disasters
Detection Ambulances with TelemedicineResponse Management (Tracking of
emergency fleets)
Driver Authentication Emergency Vehicle Traffic light
preference
Route PlanningEvacuation and Re-Entry
Management
Emergency traveler information
Credentials administration Safety assurance Electronic screeningCarrier Operations & Fleet
ManagementElectronic funds Safety information exchange Safety screening Automatic Vehicle Location
Electronic registration / Permitting Automated inspection Clearance at national borders On-board monitoring
Weight Screening Traveler Information
Credential checking
Surveillance, Monitoring
and prediction
Information Dissemination
for AdvisorsTraffic Control Strategies
Response and Treatment
Strategies
Pavement Conditions Dynamic Message Signs (DMS) Variable Speed Limits Fixed Winter Maintenance
Atmospheric conditions Internet / Wireless / Phone traffic Signal Control Mobile Winter Maintenance
Water Level Highway Advisory Radio (HAR) Lane Use / Road Closure
Vehicle Restrictions
Toll collection
Transit fare payment
Multi use payment
Pricing
Freight tracking Asset tracking
Freight terminal processes
Drayage Operations
Freight-Highway connector system
International border Crossing
Process
Information Dissemination
for AdvisorsAsset Management Work Zone Management
Dynamic Message Signs (DMS) Fleet Management Temporary Traffic Management
Internet / Wireless / Phone Infrastructure Management Temporary Incident Management
Highway Advisory Radio (HAR) Lane Control
Variable Speed Limits
Speed Enforcement
Intrusion Detection
Road Closure Management
Pre-trip and end-route
informationTourism & events
Internet/Wireless Travel services
Information for traveling Advanced parking
Phone services
Tv/radio
Kiosks
Inte
rmo
dal
Fre
igh
tR
oad
way
Op
era
tio
ns
and
Mai
nte
nan
ce
Trav
ele
r
Info
rmat
ion
Eme
rge
ncy
Man
age
me
nt
Co
mm
erc
ial
Ve
hic
le
Ro
ad W
eat
he
r
Man
age
me
nt
E-P
aym
en
t
& P
rici
ng
Mobility Services and Data
20
These ITS services are used in many cities. One of them is Dresden, in Germany. The City of
Dresden developed an advanced traffic management system and implemented it in the
framework of the project: “Traffic Management System for Dresden” –
Verkehrsmanagementsystem für Dresden (VAMOS Projekt 2012). The services implemented
are: dynamic parking systems, dynamic signaling in highways, radio reports of traffic situation,
dynamic routing with signals, traffic information systems and green waves depending on the
traffic situation.
Dynamic Parking System
The dynamic parking system provides information about the capacity and occupancy of
parking garages, facilities (buildings) and parking squares/zones in the Dresden Downtown
and Park and Ride (P+R) Stations. This initiative aims to decrease the traffic when searching
for a parking spot, since the system can deliver information of the parking options nearest to
the destinations. According to Giuffrè, Siniscalchi et al. (2012) the seeking for parking space
is responsible for up to 40% of the total traffic. Parking seekers often drive slowly and cause
the traffic flow behind them to slow down (Inci 2014). The system collects the information at
the facility with sensors and provides it through dynamic signals. It also cooperates with the
dynamic routing, explained in the following section (Figure 10).
Many solutions like these ones exist on the market (General Electric 2015, Siemens Mobility
2015, SmartParking 2015). In the scientific literature, they are overviewed by Sujith, Yacine et
al. (2014), McNeal (2013) and Seong-Eun, Poh Kit et al. (2008). The advantage of this
infrastructure based parking systems is that the parking information is very precise (Nawaz,
Efstratiou et al. 2013). The main disadvantage is their expensive costs, what is demonstrated
by Nandugudi, Ki et al. (2014): the equipment necessary for one single parking lot costs around
US$ 2500. There are also additional costs for cabling, interfaces and communication systems.
Mobility Services and Data
21
Figure 10: Parking information and routing display on a dynamic signal. Taken from (VAMOS Projekt 2012)
The system collects the information with either
vehicle barriers or with induction loops, the
vehicles entering and leaving the facilities are
identified and a digital signal is sent to the
system to provide information of the availability
of parking spaces. The information is mainly
provided with dynamic traffic lights deployed at
intersections and on streets in the route to the
targeted facility. The closer to the parking
facility, the more accurate the information gets.
At the same time, the system distributes the
demand of the parking seekers in to the
different parking facilities, in order to avoid the
traffic generated by cars waiting for other cars
to park. (VAMOS Projekt 2012) This is a useful
initiative however; it is unclear because the
system cannot identify the destination of the
people to provide accurate information for their
parking.
Dynamic Signals on Highways to Influence the Traffic Flow
The dynamic signals aim to influence the drivers to ensure the traffic safety and optimize the
traffic flow. The impacts of accidents, construction sites, irregularities and weather conditions
in the traffic can be minimized. These conditions are monitored and combined with the
information of the traffic flow, to influence the traffic flow positively. The traffic flow is influenced
by means of the dynamic signals showing information about the weather conditions, directions,
irregularities on the road, driving behavior and speed limits (
Figure 11). Moreover, the system can calculate the speed of the traffic flow, identify the best
speed for all the motorists and establish the maximal speed to benefit all the motorists in that
section.
Signaling systems are being widely used in Germany and gained popularity in developed
countries from the years 2000 – 2010, however, these dynamic signals require also expensive
costs of implementation, operation and maintenance for the investors in infrastructure (often
governmental administrations) but none for the user.
Mobility Services and Data
22
Figure 11: Dynamic signals to influence the traffic flow used in VAMOS Dresden (VAMOS Projekt 2012)
The information of the urban traffic state in the local radio stations
The traffic information streamed through the local radio stations influences the traffic behavior
with information of the traffic irregularities and risks. Furthermore, the current technology can
connect to the GPS device of every car and reroute the journey or show notifications in real-
time. It can transmit the cause, magnitudes and dimension of the traffic incident. The VAMOS
project reported that this service is only available for non-urban roads due to technological
reasons.
The data feeder for this service is a group of sensors built aside the road and the inputs of the
police crew on those road sections. Currently, the sensors can be installed only within sections
length from 25 km to 100km. This service includes a notifications generator which works 24
hours a day. The notifications generator reads the data and translates it in to a notification for
users and for the local radio stations.
The dynamic routing in the metropolitan area of Dresden allows drivers to calculate their trips
according to the current traffic conditions. Its display consists of a static frame with plates called
“Prismenwendern” that rotate to show different messages.
One disadvantage is that signals can show only a few predefined messages, and they are
unprepared for other unexpected conditions. Another challenge is that the drivers who already
passed the signal will not be able to see the message and their situation will not be improved.
The data used is mostly collected at the highways; the city of Dresden is also covered with 50
signals at 22 intersections. The data flows from its detection at the streets through the data
analysis based on filters and criteria, to the delivery to the user by means of the signal. The
critical street-sections are already identified and their alternative route is already known and
prepared in the movable pieces of dynamic signal.
Speed
Driving behavior
Irregularities
Directions
Weather conditions
Mobility Services and Data
23
Figure 12: Dynamic signal - Prismenwendern changing from normal conditions to traffic jam. Taken from (VAMOS Projekt 2012)
Traffic information system
The traffic information system is an addition to the signaling system previously presented. It
can deliver information about traffic jams in different streets, saturated parking areas, train
arrivals and departures etc. These signals can be adapted to show different messages, as
urgent urban or traffic issues, high air pollution levels or demonstrations happening on the
roads i.e.
Figure 13: Display of traffic information. Taken from (VAMOS Projekt 2012)
Adaptive traffic light programming:
The green waves proved improvements on the traffic flow, nevertheless the traffic conditions
are far from being regular and predictable, therefore adaptive traffic lights are a bolder solution.
The green waves can be manually activated to set in which direction the traffic will be eased
or the system can automatically detect and solve it. It can also be adapted, applying partial
green waves to priority sections.
There are also services provided not only to the end users, but to the administration of the
infrastructure. These are the measurements using the infrastructure that allow the authorities
to plan the new infrastructure. These are noise levels, air pollution, traffic flow, Occupation
rate, Vehicles categories, Speed, Travel time, Origin-Destination information and Incident
detection among others. More complex services are the estimations of the Average Annual
Daily Traffic (AADT) and the Vehicle Kilometers Travelled (VKT).
Mobility Services and Data
24
These traffic variables assist the traffic engineering analysis for Model calibration,
determination of traffic exposer functions, infrastructure design, policy decisions, risks of
accidents, i.e.
The first one, The Average Annual Daily Traffic (AADT) is the average calculated over a year
of the amount of vehicles passing a point in a given section every day. It is usually expressed
in vehicles per day. This measurement can be taken straight forward from the road. The
guidelines to take these measurements can be found in the publication of Ehlert, Bell et al.
(2006).
The second one, the Vehicle Kilometers Travelled (VKT) refers to the distance travelled by
vehicles on the roads. It is an indicator of traffic demand and generally is used to indicate
mobility patterns and travel trends. VKT can be presented as a value to reflect community
behavior or can be broken down in VKT per capita to report about individual contributions. This
value is harder to measure. Several methods can be used to estimate it. Four methods are
widely applied in Europe: Odometer readings (vehicle –based method), Traffic counts (road-
based method), Driver survey (people-based method) and an estimation based on fuel
consumption. Only one of them is relevant for this study, the Traffic counts (road-based
method). This method uses the previously explained AADT and multiplies it by the length of a
link (in km) which is in focus. A full review on its calculation is presented in the work of Fricker
and Kumapley (2002).
Once that it is clear, which services are provided by Smart Infrastructure, then, what is the data
they are using? These services require different data sources, some of them use only one.
Some others combine different data sets and others analyze the data in different ways to
provide different services. The data they collect and use will be reviewed in the next section.
3.1.2 Data from Smart Infrastructure
The data is the main source for ITS services. This section reviews what data is used to provide
the services and how is this data collected. It includes also descriptions of devices, formats
and examples that currently exist. In the last years, such alternative data sources have
emerged as vehicle location (Floating Car Data) and Vehicle to infrastructure (V2X), which are
out of the scope of this work.
The work of Leduc (2008) and Antoniou, Balakrishna et al. (2011) illustrate the kinds of
technologies in this field, separating them into two categories intrusive and Non-intrusive,
depending on whether the measures were directly taken or not. The intrusive method as the
matter of fact is fulfilled by a data recorder and a sensor placed on or in the road. They are the
most commonly used and the most important ones. This equipment can be briefly described
as following:
Pneumatic road tubes: Rubber tubes placed across the road lanes to detect when a vehicle
passes over the tube. The pressure changes by the weight of the vehicle, creating a pulse of
Mobility Services and Data
25
air which is processed by a counter located on the side of the road. This technology can provide
various information such as the number of vehicle, speeds, weight etc. however, the main
drawback of the technology is that it is limited to one lane and its efficiency is highly influenced
by the weather, temperature and traffic conditions. The system is not efficient for low speed
flows.
Piezoelectric sensors: The sensors are placed in a line along the road surface of the lane
monitored. The prefix piezo- is Greek and stands for 'press' or 'squeeze'. These sensors
convert the mechanical energy into electrical energy. The mechanical deformation of the
piezoelectric material generates a difference in the electric potential between the electrodes.
The amplitude and frequency of this signal is proportional to the degree of deformation and it
is recorded as the measurement. These sensors can measure weight, speed and traffic flow
and are more reliable than pneumatic road tubes.
Magnetic loops: The most conventional technology to collect traffic data is the use of
magnetic loops. They are coiled wires into loops and embedded in roadways in a square
formation that generates a magnetic field. When a vehicle (as a big metallic body) passes over
them, it changes the magnetic field, generating a pulse of electricity. This pulse is transmitted
as information to a counting device placed on the side of the road. This technology has been
widely deployed in Europe in the last years; nevertheless, it has proved to have a short life
expectancy, since it can be easily damaged by heavy vehicles. Its implementation and
maintenance costs are expensive but it is not affected by bad weather conditions.
Non-intrusive techniques are based on a remote observation that does not have direct contact
with the vehicle. They have recently emerged with using different kinds of technologies and
the digital revolution is boosting them. The most important ones are the following:
Manual counts: It is the most popular method since it involves only people, pens and papers.
Observers are trained to gather traffic data that cannot be efficiently obtained using technology
e.g. pedestrian counts, vehicle classifications, irregular behavior at intersections and vehicle
occupancy. The most common equipment necessary is the tally sheet, mechanical count
boards, and electronic count board systems.
Passive and active infrared: The presence, speed and type of vehicles can be detected by
the infrared energy reflecting after an infrared beam is projected to the detection area. This
method is heavily affected by the weather conditions and has a limited lane coverage.
Passive magnetic: Magnetic sensors are built in the pavement. They can count the amount
of vehicles passing, their type and speed. In high traffic flows they might fail differentiating
measurements of vehicles passing close by.
Microwave radar: The microwave radars are devices throwing microwave beams to the roads
and detecting moving vehicles. It can record count data, speed and simple vehicle
Mobility Services and Data
26
classification. Speed can be recorded us4ing a second radar and measuring the (Doppler
effect).
Ultrasonic and passive acoustic: These devices throw a wave of sound and record the signal
returning to the device. Basically, they measure the time the signal takes to return. Their
frequency is out of the human hear but they can be easily affected by weather conditions. The
ultrasonic sensor is placed alongside the road and can record vehicle counts, speed and
classification data.
Video image detection: These devices are video cameras capable to identify image patterns
and characteristics through Optical Character Recognition (OCR) Software. They can identify
plate numbers, trip lane, vehicle occupation and tracking i.e. These devices have expensive
costs and are sensitive to weather conditions.
The kind of data these sensors collect is presented in Figure 14 and compared according to
its capabilities. The most of them are focused on counting vehicles. The second most popular
categories are to measure the speed, the type of vehicle and its occupancy. Some of them can
detect an accident and very few can provide travel times or Origin/Destination information,
which is the most relevant for transport planning. These comparisons are based on the
researches of Martin, Feng et al. (2003), Schmidt, Giorgi et al. (2005) and the U.S. Department
of Transportation (2006).
Figure 14: Type of data provided by different data collection technologies. Adapted from (Schmidt et al., 2005) and (U.S. Department of Transportation, 2006) and (Peter Martin, 2003).
How precisely does the data look like? The data collected for the (VAMOS Projekt 2012) is an
illustrative example. While looking at it, it the degree of complexity of raw data can be better
understood, it is highly complex. At the course “Mobility Services Lab” the programmers got
this data to produce visualizations and explore the possibility to generate a service with it. They
Volume / Count SpeedVehicle
categoryOccupancy Travel time
O/D
information
Incident
detection
Doppler
True Presence
I
n
t
r
u
s
i
v
e
Video Image processing with
ANPR
Radar
Ultrasonic
Detector type
Inductive Loop
Passive Infrared
Pneumatic Road Tube
N
o
n
I
n
t
r
u
s
i
v
e
Active Infrared
Microwave
Radar
Passive acoustic
Video Image Processing
Manual Counts
Piezoelectric cable
Mobility Services and Data
27
have found it very hard to understand, manage and build in a solution from the provided data.
In conclusion the data was not used and they preferred to generate the data by their own with
the smartphones. A similar situation happened during the BMW Automotive Hackdays at the
Technische Universität München in March 2016. Out of the ten proposals developed by the
teams, all of them were defined by the available data. An example of the data collected by
different sensors can be seen in the following Figure 15:
Sensor type Kind of data / German
Kind of data Exemplary value
All of them Information Sensor ID, Camera ID, Lane, Position, Port, IP, Direction, Sensor ID code, Timestamp, Kind of
parking, Description, Capacity, Opening hours, prices
Double induction loop
Geschwindigkeit Speed 23
Belegung Occupancy 22
Belegung_SV Occupancy freight 0
Traffic camera (KPD) LOS (1 - 6) LOS 1
Traffic Observer Laser Measuring System (LMS)
LOS (1 - 6) LOS 1
Parking Guidance System (PLS)
Belegung Occupancy 22
Status Status (Open 1 /closed 0) 1
Tendenz Tendency (1=decrease, 2= no flow, 3 increase)
3
"Verkehrspegel by Induction Loops
Belegung Occupancy 22
Belegung_SV Occupancy freight 0
Geschwindigkeit Speed 17
Nettozeitlucke Net time gap 35534 ms
Belegzeit Time of vehicle passing 65534 ms
Length of the vehicle Length of the vehicle 7
Street Management Belegung Occupancy 22
Street lamp with a TEU, Traffic Eye Universal
Belegung Occupancy 24
Geschwindigkeit Speed 22
Wartezeit Waiting time 4
LOS (1 - 6) LOS 1
Figure 15: Data collected by different sensors Provided by (VAMOS Projekt 2012).
One of the main aspects in the decision to apply these technologies in cities is their influence
in the users and their costs. The effectivity of smart infrastructure measuring have been
research by (Khoo and Asitha 2016). They argument how the travel related information can be
displayed. With VMS, the information can be shown only in hotspot locations and only provide
localized traffic information. The drivers have limited response choice in their alternatives to
divert and are uncertain on the traffic conditions these alternatives offer.
The costs of this infrastructure are fully covered by the governments or infrastructure operators.
These are internalized with different instruments and are rarely paid directly by its users. The
Mobility Services and Data
28
costs are published yearly by the (Department of Transportation 2009). An overview of them
for different sensors is shown below:
Figure 16: Costs of smart infrastructure sensors. (Department of Transportation 2009)
In summary, the sensors of smart infrastructure are a mature technology, well recognized and
efficient. New technologies in this direction do not offer great advantages over the previous
ones. I.e. Video image processing over Radars. These technologies are highly precise and
resilient, but expensive to install and operate. Governments rely commonly in these
technologies, absorbing the associated costs. Governments use these technologies to monitor
the infrastructure, support its users and regulate them. The data gathered with this technology
is very detailed but hard to understand and use in different applications. They have limited
coverage, mainly only in major highways and key intersections, with low precision for urban
areas at a mesoscopic scale.
g. Technologies Based on Smartphones
This section focuses on the Digital Mobility services, also called Apps for mobility or Smart
mobility services. It understands and explains which services are offered braking them down
in their most important parts. It reviews its relevance in the current market and its development.
The impact of digital services on mobility has increased significantly over the last years. The
spread of smartphone and the invention of the Apps intensify this. When Apple. Inc. launched
their App store, solutions to small and different problems of life were created. An App is a piece
of software that can be run on the internet, on a computer, on a smartphone or on another
electronic device. Basically, it is a short computer program (Karch 2016)
Google trends reports the interest of a certain topic monitored through its search machine.
Since google is one of the most widely used search machine in the world, their results are
significant. When searching for the term “Smartphone apps” it is evident how the interest in
Apps has grown since the invention of the App stores in 2008. The searches for Apps through
google were recorded since years earlier, probably made by developers or the specialized
population and starts to grow exponentially from 2009 to 2012 and stayed regular for one year.
Unit Cost ElementLifetime
(years)
Capital Cost
(US$ 1000)Cost Date
Operation &
Maintenance
Cost (US$ 1000)
Cost Date
Inductive Loop Surveilance on Corridor 5 3-8 2001 0,4-0,6 2005
Inductive Loop Surveilance at Intersection 5 8,6-15,3 2005 0,9-1,4 2005
Machine Vision Sensor on Corridor 10 21,7-29 2003 0,2-0,4 2003
Machine Vision Sensor at Intersection 10 16-25,5 2005 0,2-1 2005
Passive Acoustic Sensor on Corridor 3,7-8 2002 0,2-0,4 1998
Passive Acoustic Sensor at Intersection 5-15 2001 0,2-0,4 2002
Remote Traffic Microwave Sensor on Corridor 10 9-13 2005 0,1-0,58 2005
Remote Traffic Microwave Sensor at Intersection 10 18 2001 0.1 2001
Infrared Sensor Active 6-7,5 2000
Infrared Sensor Passive 0,7-12 2002
CCTV Video Camera 10 9-19 2005 1-2,3 2004
CCTV Video Camera Tower 20 4-10 2005
Mobility Services and Data
29
Then the interest has been decreasing 55% since 2013 to 2016. This might indicate that the
peak has already been reached in 2013 and now it is in the process of plateau, confirming the
Gartner’s hype cycle, which describe the intensity of trends as growing curve that reaches a
peak, then decreases again and then stabilizes for the long term, growing at a slow pace
(O'Leary 2008).
Figure 17: Interest over time of the concepts of: "Apps", Adapted from Google trends (2015)
Figure 18: Gartner Hype cycle of emerging technology. Adapted from (O'Leary 2008).
Since the early 2000s, it has become possible to track mobile devices and to gather location
data of users, firstly with the phone networks (CDMA, GSM) and recently using a GPS device
in the phone. While before, this data has been exclusively gathered by city administrations via
on-street sensors, the data is now available for anyone who have access to mobile devices’
database (Zhao 2000). Subsequently, mobility and location-based services emerged on
smartphones as well as on cars. For example, Google introduced Google Maps in 2005,
offering digital maps of the whole world to smartphone users. Over the years, Google gathered
more and more data from the users to provide more specific information, location’s reviews,
Public Transport, information on the traffic flow i.e. This is possible as it only takes the phones
of 2-3% of the drivers on the road to provide an accurate report of the speeds of traffic (Herrera,
Work et al. 2010). Similar crowdsourcing approaches emerged to gather information on traffic
incidents, radar controls or pavement conditions (Yi, Chuang et al. 2015). With the increasing
spread of smartphones, further services such as car sharing and ride sharing (Teubner and
Flath 2015) or parking (Caicedo, Blazquez et al. 2012) emerged. This leads to a situation
where smartphone based services are an important influencing factor on individual mobility
Peak of inflated
expectations
Technology trigger
Plateau of Productivity
Slope of enlightment
Trhough of Disullusionment
Vis
ibili
ty
Time
Mobility Services and Data
30
(Wolter 2012) (Forrester 2013). As these services strive for the user’s optimal mobility, they
have a conflicting goal to ITS, which strive for a macro system optimum for example for one
city or one road (Jonkers and Gorris 2015).
When looking for granulated searches for mobility apps, some services are far more popular
than other ones. The maps and navigation Apps are more popular than the parking and traffic
information Apps Figure 19. This raises the question, which are the services the Apps provide?
How often are they used?, what are their effects in mobility?. Questions to be solved in the
forthcoming sections 3.1.3 and 3.1.4 .
Figure 19: Interest over time of the concepts of: "App transport”, "App navigation", "App traffic", "App map". Adapted from Google trends (2015)
3.1.3 Smartphone based Services
This study uses the framework for service systems to identify the services and their granularity.
This framework allows the understanding of the services systems as groups of service
modules. This section analyzes existing digital mobility services to identify their modules and
data sources. In doing so, the second step of modularization within the framework by
Dörbecker and Böhmann (2015) is applied. This step comprises the identification and analysis
of the service system’s modules. Subsequently, the data and method used for the analysis are
described, then the results are the modules and data sources of digital services for urban
transportation.
City administrations solve traffic congestion problems by operating ITS, meanwhile individuals
try to optimize their travel patterns with digital mobility services. Both groups aim for different
goals with specific resources as time, money, comfort, reliability, etc. Therefore, a large
number of heterogeneous digital mobility services have emerged for usage on smartphones.
Mobility Services and Data
31
Some services provide information on traffic and mobility options, such as the trip planners
Quixxit and Allyapp, others directly support the user in reaching his destination by matching
the user with bike-shares, car-shares or taxi drivers. All these services are part of a service
system for digital mobility services, which solve the complex scenario of the transportation
systems. However, the services are hardly related, the landscape of digital solutions is vast
and unstructured.
Alter (2008) defines services as “[…] acts performed for others, including the provision of
resources that others will use." In the case of digital mobility services, an “act” could be the
provision of information how an individual can get from A to B within a city most efficiently. A
service system is “a work system that produces services”, while a work system is “a system in
which human participants and/or machines perform work using information, technology, and
other resources to produce products and/or services for internal or external customers” (Alter
2011). In the case of digital mobility services, traffic data, algorithms and software applications
such as mobile Apps build a work system that produces services related to mobility.
The modularization of systems is a concept supporting the desired characteristics for product
as well as service systems, breaking down the concepts composing the system or product
(Baldwin and Clark 2000). In particular, modularization fosters co-creation of value and
innovation (Böhmann, Junginger et al. 2003). Service modularization can be defined as “(a set
of) activities being part of interactions between the components of service systems” (Leimeister
2012). Consequently, a modular architecture consists of modules and decoupled interfaces
between modules (Ulrich 1995). The interfaces facilitate co-creation of value and form the
basis of the service system. Based on service systems engineering theory, Dörbecker and
Böhmann (2015) propose a methodology framework for the design modular service systems
to followed in this work. For example, the house building industry followed this evolution. Firstly,
there were whole organizations building the whole houses. After many years, specialists in
different activities and products bloomed and the industry got modularized. This way the
industry gained quality and efficiency.
3.1.3.1 Digital Mobility Service Systems
In the vast landscape of digital solutions, modular services belonging to one service system
can add value for another service system as well (Böhmann, Junginger et al. 2003, Böhmann,
Leimeister et al. 2014). Böhmann, Leimeister et al. (2014) in their call for future research,
describe sustainable transportation as one area in which services can deliver greater value
when they are part of a service system. Then it is needed to understand the service systems
and service modules currently available. Following the framework by Dörbecker and Böhmann
(2015), the perspective of modularization of service systems is applied to digital mobility
services.
To structure the landscape of mobility services and to identify the most important modules of
digital mobility services, the modules and data sources of 54 digital mobility services (Apps)
relevant for urban transportation were evaluated as part of the scope of this study. During the
Mobility Services and Data
32
service analysis, it was found that different Apps provide one main service, and many different
service components. Some of the service components were directly related to mobility, some
others were not. This study helps practitioners to develop more components of mobility
services. This is also relevant for public administrations which use the traffic data they gather
to operate ITS in order to provide digital mobility services for individuals.
The heterogeneity jeopardizes deeper analysis on how these services impact mobility and how
future mobility services can be designed. Moreover, the data availability defines how far an
App can reach. Therefore, the first step is to identify the most important modules o digital
services as part of a service system. The second step is to identify their data sources.
The Apps (understood as service systems) selected for the review come from the list of 50
Apps more downloaded in the German Android Play Store’s App category “traffic” and Apple
App Store’s category “navigation”. The search was enhanced by a keyword search within a
database of more than 80,000 tech blog articles gathered from October 2015 to February 2016;
the keywords used were “traffic”, “mobility” and “navigation” to identify suitable articles. By
including tech blog articles, new services that are not yet in the top 50 Apps were included.
Altogether, 54 mobility services were analyzed in more detail. The research compared the
sample with the overview provided by Motta, Sacco et al. (2015) to identify missing services.
To identify the data sources used by the services the license files and the general terms and
conditions were revised. With an iterative coding process as described by Lacity et al. (2010),
the services were grouped into categories and grouped similar modules and data sources. The
categories of mobility services defined are trip planners, traveling analytics, car/ride sharing
services, navigation services, charging stations, location-based services and parking services.
Hereafter the understanding taken for this work is explained for each service.
Trip planners Apps provide information for the users to help them plan their itineraries within
and between cities. One example is Allyapp, it is a platform to calculate routes by several
means of transport as long as they are available on the streets. It focuses on Public Transport,
creating a partnership with the local public transport associations to get high quality and real
time data.
Car sharing services Offers cars to share and drive individually as for example with Drivy
(2016). Drivy, a French startup founded in 2010 provides a platform for individuals to rent their
cars to others whenever they do not need it. It is also called Customer to Customer (C2C) car
sharing. Drivy is little by little acquiring its competitors, after buying its German competitor
“autonetzer” in 2015 and has become Europe’s largest platform for individual car sharing
(Jacqué 2015).
Ride sharing services provide users with a platform to share rides as for example with Flinc.
Flinc is a ride sharing service that facilitates dynamic ride sharing. Instead of prearranging
rides as in traditional ride sharing, drivers get notified in real-time whenever a request of a
passenger fits their current route. While traditional ride sharing is mostly used for long-haul
Mobility Services and Data
33
inter-city travelling, dynamic ride sharing as offered by Flinc is also suitable for inner city ride
shares. From January 2016 on, Flinc integrates information on public transportation to
enhance seamless intermodal travelling (Schumacher 2016).
Parking services offer information on free parking lots, either of their exact position or of the
probability to find one within a specific are. Years ago they offered information of parking
facilities, nowadays these services are extended for the public streets curbside. For example,
the service parknav which is available in the US and Germany shows the probability that in a
given road there will be available parking lots (Wagner 2016).
Navigation services support the user in following a route by giving directions. For example,
Google Maps navigation feature is the most used navigation service on Android devices.
Navigation services are mostly used in-car or recently, they are incorporating navigation for
pedestrians and cyclists, and only a few provide directions in public transportation or by
different transport modes.
Location-based information services provide relevant information for the users based on
their current location. For example, dedicated services for radar controls or charging stations
exist. Apps like this can be Blitzer.de or Waze.
Charging stations services offer the information about fuel charging station either for gas or
electric based vehicles. More than their location and main services, some Apps offer additional
services, i.e. Tanktaler even offers discounts for charging in certain stations.
Traveling analytics services Monitors the behavior of the user and delivers a report of their
mobility, often analyzing it. i.e. Fleet board monitors the driving style and managing the time of
the user and reports it to one central user.
The most common services in the sample are the trip planners and the ride sharing Apps.
Followed by car-sharing, navigation and location-based information Apps. The least common
are the traveling analytics and charging stations Apps. Non-classifiable Apps were labeled as
“others”. The amount of Apps per service system is illustrated in Figure 20.
Figure 20: Categories of digital mobility services (Source: own analysis)
Mobility Services and Data
34
3.1.3.2 Digital Mobility Service Modules
To understand the service systems, this study goes deep in its parts, called service modules.
According to Balzert (2009), modules comprise elements that are strongly interrelated among
each other, but only weakly interrelated with elements outside of the module. Therefore,
service modules are parts of the services that generate a distinct value for the user. Service
systems are composed by many service modules. This work does not describe insights of the
technical details of the service modules since this is an analyse from the outside, it only
identifies them and their functions. Consequently, the analysis relates to the second step of
the modularization framework by Dörbecker and Böhmann (2015), covering the identification
and analysis of modules of a service system. By iteratively coding the modules within existing
mobility services.
A service module directly related to mobility addresses spatio-temporal dynamics, like routing,
dynamic map view or traffic information. A non-directly related service module is like fare
splitting, connection to calendar, service rating and trips report i.e. The services not related to
mobility are considered but not analyzed further due to its plural nature and low relevance for
mobility. Nevertheless, they seem to mark the difference on the popularity of the App.
The digital mobility service modules with an spatio-temporal nature found in the analysis are
explained:
Map view to show locations, nodes and links. In most of the cases the map is the main view,
for example, the car sharing application Drive Now shows the user’s position and all available
cars on a map on the first screen after log in, even when the users only need one car.
Route planner module provides suggestions on which route should the users follow in any
transportation mode.
Real time navigation module provides information on which path should the users take
according to their current position as a part of an already calculated route. There are unimodal
services, like Waze or google maps by foot, or multimodal, like google maps by public
transportation or Moovit.
Points of Interests (POI) show the locations of relevant activities or places in the map. They
can be thematic classified and different attributes can be added. i.e. the App Tanktaler
publishes the offers in real time in certain gas stations.
Location sharing module allows to share either the current position of the user or of any other
relevant location in the map. In both cases it is static, since it does not matter if it changes in
time or space.
Dynamic location sharing allows to share the location of a user in real time while it moves.
This is often used in taxi services as Uber or Mytaxi app, to share the position of the taxi
vehicles but it does not share yet the position of private users.
Mobility Services and Data
35
Parking information has become a popular service module, Pflügler, Köhn et al. (2016)
analyses the different parking information services existing and proposes a new one, based
on publicly available data. The parking information services are aided by sensors on the
infrastructure, activated by the users or predicted based on publicly available datasets.
Infrastructure Information notifies about the state of the infrastructure and any change, it can
be officially deployed through official apps, i.e. The Apps of the public transport association
notify of any maintenance or closure, besides through Waze, the users can notify of street
closures, construction sites or problems.
Traffic information service module shows the aggregated data of the traffic state for cars or
public transportation vehicles. Some of the most popular services are the one provided by
Google maps, reading the position of the users or Waze, doing the same and confirming with
reports made by the users.
Matching user to user allows the market function, connection offers to its demand for a certain
mobility service. For example, Drivy, on one side, allows private people to offer their car for
rent and on the other side, allows other people to search for an available car and rent it.
Figure 21: Services modules in Digital mobility services systems
Figure 21 shows service modules that are more often included in a specific service system
category. The map view is the most popular service module, the most of the Apps categories
include it without big variations. The Route planning, real time navigation, location sharing and
Category
Ro
ute
pla
nn
ing
Real ti
me
navig
ati
on
Dyn
am
ic
Lo
cati
on
sh
ari
ng
locati
on
sh
ari
ng
Map
vie
w
Po
ints
of
Inte
rest
(PO
I)
Park
ing
info
rmati
on
Tra
ffic
info
rmati
on
Infr
astr
uctu
re
info
rmati
on
Matc
hin
g u
ser
to u
ser
Trip planner
Car-sharing
Ride-sharing
Parking service
Navigation
Location-based
information
Charging stations
Traveling
analytics
Average
Variance 0.15 0.15 0.14 0.17 0.01 0.10 0.11 0.04 0.16 0.05
Symbols 100% 75% 50% 25% 0%
Mobility Services and Data
36
POI’s are the second most popular modules. Many Apps categories include them with a higher
variation. The parking information is less popular with a higher variation as well, some
categories always have them, many others do not. The infrastructure information module is
very popular in Location-based information and navigation but not in the other service systems.
The traffic information and the matching user to user modules are rarely used without higher
variations.
Other service modules identified are listed below. These are harder to analyze and less
relevant individually due to the heterogeneity and plurality. Nevertheless, they make the
difference on the popularity of an app, i.e. (Khoo and Asitha 2016) found that one of the main
factors to prefer a car navigation App is the voice guidance, above the quality of the traffic
information or displayed map.
Cancellation policy In case the user wants to cancel the inquired service
Chain of custody written evidence of service for legal purposes
Guarantee Delivery of the expected result guaranteed
Support Real time customer support
Trips report Does the App offer a report of the behavior of the user?
Save requests of the
users?
Is the App storing information of the requests of the user?
Only girls service Does the App offer a differentiated service according to gender?
Order via website Does the service works from a website / PC?
Offline mode Does the service work offline?
reminders does the App send reminders to the user and carrier?
Real time order order now?
Schedule/ Program
Order
Can users book / make orders in advance?
Messaging / call communicate with the messenger/transporter
Rating / feedback for
transporter
Key partner (carriers) rating
Client rating Can the transporter rate the user?
Personal registration do the users of both sides have to register in person at an office?
Recruiting process carriers pre-screened?
Training for drivers Do the App provide training to its key partners?
Only officially
registered vehicles
Do the providers require additional official affiliations?
App for Partners Is there a specialized Key partner App different than the one for
users?
Customize order (filter) increase fare, according to area, min. Fare, etc.
Re-routing planner Do the App can adjust the route to real time conditions?
ETA Sharing Share the "Estimated Travel Time” to arrival
Mobility Services and Data
37
Visual communication Provide visual information to the users
Audiovisual
communication
Provide audio visual information to the users. i.e. Map + voice,
videos.
Activity Recognition Can the App recognize the activity performed by the user?
Indoor information Does the App provide indoor maps?
Customize save your places, see what you want, etc.
Customized link to
Mobility Services
User can decide to which mobility services is the App connected
(DB Call-a-Bike or "Norisbike" or MetropolRadRuhr i.e.)
Connection with
calendars
Can the App export/Import data from the calendar of the user?
Price estimator Does the App provide a price estimator for its services?
Transfers the value Transfers the value to transport operators
Electronic ticket Can users buy an electronic ticket?
Spit fare Can users share the costs, differentiating the charge on their Credit
Cards?
Flat rates for long
distances
Does the App offer flat fares for certain routes?
Dynamic fares Flat fares for the most of the routes
Mediator in disputes Does the App official act as mediators in case of disputes
Make reports of other
kind of info
can users input reports about different issues than the App services
(infrastructure, crime, environmental issues)?
Connects with social
media
Can the App export/Import data from different digital social
networks?
Internal social network Does the App have an internal social network?
Mutes the cellphone Does the App mute the cellphone while moving to avoid dangerous
distractions?
Notification of
abnormal movement
Does the App notify you in case it detects an unexpected
movement? i.e. in case your car moves / is stolen
Provides vehicle Does the App provide the vehicle?
Different vehicles
available
Can users choose the vehicle from different options?
Float management Locate your vehicles, get reports of their trips and statistics.
Parking alert The App notifies you when your parking ticket is about to expire.
Fitness coach the App sends suggestions to keep fit the users
Pictogram Evaluate the state of the vehicle only taking a picture
Extra device needed Wearable? vehicle? other device?
Accident sensor &
Notification
Automatic Detection of accidents
Panic Button Direct connection with authorities in case of emergency
Mobility Services and Data
38
3.1.4 Smartphone based Services Data Sources
After identifying the service systems and their service modules, the feeding of data sources for
the digital services is analyzed. As many mobility services are data-driven (Wolter 2012). The
data sources used by the services help to understand the interdependencies of services and
their modules as well as to provide an indication for practitioners of which data sources are
necessary for which targeted service.
The most popular data source used by the reviewed Apps are the device sensors collecting
information in the way a crowdsourcing. Mostly the sensors used is the GPS to locate the user
and enable the routing as well as analyze the spatiotemporal information of the user and
generate the services needed. The Google ecosystem emerged as one of the most important
data sources. Firstly, the map view provided via an API (Application Programmer Interface) is
widely used in mobility services of all categories as seen in the previous section. In addition,
google provides the routing and traffic information through an API and is widely used in
navigation and trip planner service systems. Overall, 35 out of the 54 of the reviewed Apps
use at least one of the Google API’s as a data source.
In the data set market, many players besides Google are being used by the Apps.
Nevertheless, Google is the main source for 26 / 54 service systems. Many use other private
providers, but none of them is as dominant as google is. For example, TomTom, monitors
and builds the basis for in-car navigation services. They sell the datasets and it is not
commonly built in apps, Google in comparison provides the API for this service module for
free. Another important data source are the public transportation providers. These providers
are often the Public Transport associations or parastatal companies that partner with App
companies to build their own App for the users. The datasets they can provide can be the
timetables, routes and even real time data as incidents or sudden adjustments in the network.
For instance, the App Moovit partner with the public transport providers of every city where
they are active, to offer service up-to-date in real time. Public administrations offer the data
of the traffic situation or construction works in very rare situations. The most common case is
for Public Transport changes or data sets about the urban structure of the city.
Mobility Services and Data
39
Figure 22: Data source popularity of usage
The different data sources used by different categories of mobility service systems are shown
in Figure 23. It can be seen that all the categories use the sensors built in the smartphone as
a data stream. The usage of information from the public administration is not widely used, and
its usage is spreader in to the different system categories. While the majority of the charging
stations services use it. None of the car or ride sharing Apps use this data source.
Figure 23: Data sources feeding digital mobility service systems
The data coming from the public administration is the focus of this work, often collected by
means of smart infrastructure. Then its review is more elaborated. The key transport-related
datasets for intelligent mobility are extracted from the data catalogue provided by the Catapult
Transport Systems (2015), only considering those owned by the public administrations. This
confirms which datasets should be shared by public administrations and build in Apps.
1. Place and space
a. Points of interest and places (POI)
b. Public Transport stops and interchange locations
c. Vehicle parking locations
d. Vehicle fueling / charging stations
e. Streets, roads and railway lines used to move people and goods
2. Environment
a. Weather data
b. Air quality
3. People, things and movement
a. Personal location data anonymously
b. Aggregated people locations data
4. Event-related data
a. Operation of transport networks (Traffic, incidents, disruptions, etc.)
b. Events as cultural, leisure, mass gatherings, etc.
5. Public Transport Services
Category GoogleOther private
provider
Public
transport
provider
Public
administration
Smartphone
sensorsCrowdsourced
Trip planner
Car-sharing
Ride-sharing
Parking
service
Navigation
Location-
based Charging
stationsTraveling
analytics
Average
Variance 0.10 0.15 0.09 0.13 0.00 0.07
Symbology 100% 75% 50% 25% 0%
Mobility Services and Data
40
a. Transport network capacities for every transport mode
b. Normal service’s operations as timetables and routes
6. Freight connections
a. Network wide capacity for all transport modes
7. International connections
a. Network wide capacity for all transport modes
b. Normal service’s operations as timetables and routes
8. Consumption and transaction data
a. Fare and pricing for all modes of transport
b. Opinions from social networks
c. Usage of data from different digital mobility services providers
d. Payments and purchases of goods and services related to mobility
The datasets were presented; how does this data get collected by means of a Smartphone?
The data collection techniques started using the sensors and signals of the smartphone and
have been evolving in to new techniques to measure different parameters. The transport
catapult illustrates it as shown in Figure 24 taking in account five different techniques, Manual
collection, Overt crowdsourcing (activated by the user) and Covert crowd sourcing (indirectly
taken), sensor derived (sensors inside the phone measure parameters) and service provider
generated (Network provider measure the data transfers).
Figure 24: Data collection techniques with smartphones. (Catapult Transport Systems 2015)
These different techniques make use of the sensors available in smartphones, the more
sensors the more data collection techniques and the more data that can be collected. An
Mobility Services and Data
41
overview of the sensors technology currently available in Smartphones is presented in Figure
25.
Figure 25: ten of the major sensors an average smartphone may be equipped with. (Matzen 2015)
Services from both technologies experience similar issues. On one side, Smart Infrastructure
offers parking information, rerouting services and traffic information, nevertheless due to its
nature, it cannot offer too many services directly to the end users and offer more to the mobility
authorities, mainly, data collection services. On the other side, smartphone services are
offered in a big variety and offer different kind of service modules. Their main data source is
the own smartphone sensors and crowdsourcing collected data but not the data collected by
the infrastructure. Both environments try to improve mobility, but in which way? Many different
institutions related to both have declared many goals and objectives, are they addressing the
global and individual optimum? Or what is their aim? That will be the focus of the next chapter.
Goals of Services for Mobility
42
4 Goals of Services for Mobility
This Chapter finds the goals for smart mobility services matching the top-bottom approach with
a bottom-up approach. Firstly, checking what are the relevant organizations taking into
consideration? how they orientate their efforts. Then, the user’s needs are reviewed as a
bottom-up approach. Both will be conveyed in a structure closer to the human needs. Following
its mobility needs, uses and preferences will be reviewed with the application of a survey and
will be compared.
h. Goals of Organizations
Then, what are the goals the technology should pursue? How to prioritize the future actions?
Firstly, a global perspective to analyze, where the world should go is reviewed. In order to do
this, the Sustainable Development Goals of the United Nations (2015) were checked. Then in
a more technical perspective, what orientates the industry of the ITS and of the automotive
industry was checked. Also, governmental goals were looked through, the European
Committee for Standarization (2002) and the ITS Strategic Plan of the USA 2015, produced
by Barbaresso, Cordahi et al. (2014). A summary of the goals from different literature is
presented in Figure 26.
Figure 26: Goals mentioned by relevant sources
The Sustainable Development Goals (SDG) have a very global perspective, mostly oriented
towards environmental and health benefits. They promote initiatives and recommend policies
at different organizational levels. They promote initiatives to prevent and cure diseases related
to traffic pollution. The SDG support sustainable transportation that can address rising
Goals of Services for Mobility
43
congestion and pollution issues, particularly in urban areas, and are applicable at global,
national, local and sector levels. The policy recommendations are to be published in a global
sustainable transport outlook report that will be released on the first international conference
on sustainable transport in 2016.
The goals of the SDG standing alone do not relate directly to mobility Apps. Neither their
targets do. Nevertheless, concepts related to mobility are mentioned across different targets,
in those related to health, energy, infrastructure, cities and human settlements. The relevant
targets will be explained followed by a presentation of an extract of relevant concepts for
mobility Apps.
From the relevant goals for transport, only the 9th and 11th address Apps for mobility in some
of their targets. The 9th goal aims to build resilient infrastructure, promote inclusive and
sustainable industrialization as well as to foster innovation. The 11th goal refers to making the
cities and human settlements inclusive, safe, and sustainable. From such targets, the relevant
concepts for the goals of Apps for mobility can be identified.
The relevant goals for Apps for mobility are cited in the targets 9.1, 11.1, 11.2 and 11.a. The
target 9.1 addresses the economic development and human wellbeing, the target 11.1.
considers transport as one of the basic services to be available in households. The target 11.2
clearly regards to transport through safety, accessibility and affordability. The target 11.a takes
the path for regional planning towards economic and social development and sustainability
(United Nations 2015).
From these relevant targets for mobility apps, the main concepts of economic development,
human wellbeing, availability, accessibility, safety, affordability, sustainability and social
development can be identified. These targets do not provide specific indicators, leaving the
performance measurement open to the public. Nevertheless, these general concepts can be
considered as the goals that the United Nations (2015) recommends to follow in the
development of Apps for Mobility.
Not only the intergovernmental organization promoting international cooperation cares about
guiding the development but also the industry and industry clusters. The article written by
Catapult Transport Systems (2015) state that all transport companies are expected to be data
companies, exploiting the digital byproducts generated from their operations. Aside, many of
the major global data and technology companies are already investing in transportation
systems to explore whether they can provide enhanced services. i.e. SAP rideshare, Google
autonomous car, Apple maps. The amount of data, its degree of detail and the data analysis
capabilities of these companies make of them strong market competitors in the understanding
and offering of user-focused transportation systems.
At the Cosmos Conference on smart mobility services in Ingolstadt on March 8, 2016, the goals
to orientate the efforts of the automotive industry defined the headliners of the discussion. This
study considers what has been said on this conference as representative of the German car
Goals of Services for Mobility
44
industry. Prof. Wagner (2016) presented three strategies for mobility services, not goals
themselves, clustered in to “Product and Technology”, “Services and organization” and
“Society and communication” as shown in Figure 27. These concepts are relevant for mobility
Apps; the first cluster named technological innovation and optimization of traffic flow is included
in the “Product and Technology” strategy. The activities to add value to automobiles,
intermodality, car-free zones and digitalization are mentioned in this section of analysis. For
the “Society and Communication” strategy, the professor pointed out that the research was
meant to understand the changes of lifestyle, regional development and how the mobility
concepts became part of the created environment. These factors seemed to be current trends
to consider while developing mobility services, but not as strict goals that can be sectioned into
objectives, targets and indicators.
Figure 27: Three -column strategy of Mobility / Technische Hochschule Ingolstadt (Wagner 2016)
After mentioning the perspective of the automotive industry, what is the perspective of the
industry in ITS? The ITS are defined according to the ITS-EduNet (2005) as the following
points:
ITS integrate telecommunications, electronics and information technologies, in short,
“telematics” – with transport engineering in order to plan, design, operate, maintain
and manage transport systems.
This integration aims to improve safety, security, quality and efficiency of the
transport systems for passengers and freight optimizing the use of natural resources
and respecting the environment.
To achieve such aims, ITS require procedures, systems and devices to allow the
collection, communication, analysis and distribution of information and data among
moving subjects, the transport infrastructure and information technology applications.
It has not always been that the ITS covered such a broad part of the transportation field. They
were established in the 60’s for urban traffic and motorway control. During the 80’s, they
evolved to network approach, distributed intelligence and the inclusion of vehicles. The ITS of
today integrate different transport modes, including mobility management measures, providing
Goals of Services for Mobility
45
personalized services and increasing automation. Nowadays, the digitalization trend is pulling
them towards digitalization of services and big data analytics.
The Traffic control and Traffic Management notes of Prof. Busch (2014) at the Technische
Universität München present the objectives of Transport Management and Control under one
main goal: “Sustainable Transportation”; the general objectives were defined as following:
1. Environmentally friendly traffic
2. Safe transportation
3. Efficient transport systems
4. Comfortable travel
5. Economic traffic management
These objectives define sustainable development as the one that “meets the needs of the
present without compromising the ability of future generations to meet their own needs” (World
Commission on Environment and Development 1987). Additionally, the experts in
transportation split sustainability in three different domains, Economical, Societal and
Environmental. Such domains ponder available resources in the present and the future, but
also they do not compromise the resources of other domains without equivalent recovery or
exchange.
Governmental agencies have also made big efforts in guiding mobility services, but not yet
Apps. In this work the approaches of the European Union and the USA are reviewed, as global
economic development poles. One as a highly regulated and social market and the other as a
free market economy. The regulation norms to orientate mobility services in Europe are
European Committee for Standarization (2002) and in the ITS Strategic Plan of Barbaresso,
Cordahi et al. (2014).
The main purpose of the DIN 13816 (European Committee for Standarization 2002) is to
promote the quality of public transport operations as well as to focus interests on customers’
needs and expectations. This study expands and analyses all the different services provided
by Apps. This standard is based on a service quality loop, which compares both perspectives,
the customer’s perspective and the service provider’s perspective, measuring the difference
between expectations and reality for both, called satisfaction and performance respectively
Figure 28. The way to measure is based on a recommended criterion.
The criteria recommended is the perspective of the customer. Here it is presented as an
overview, from which the concepts relevant for Mobility Apps will be extracted. The criteria are
divided into eight categories: Availability, Accessibility, Information, Time, Customer care,
Comfort, Security and Environmental Impact.
Goals of Services for Mobility
46
Figure 28: Service quality loop of the European Committee for Standarization (2002)
The means of compliance of criteria can be also supported by App that measures the
performance or validates results. European Committee for Standarization (2002) also provide
guidance on how to measure the satisfaction and performance levels. It guides to the
application of Customer Satisfaction Surveys (CSS) for satisfaction measures as well as
Mystery Shopping Surveys (MSS) and Direct Performance Measures (DPM) for performance
levels. These data collection techniques apply stated preference and revealed preference
questionnaires as well as direct observations. Mobility Apps can be applied for any of these
data collection techniques and provide any kind of information to the user about the transport
network. The goals and conditions for a transport network that the norm measures are enlisted
here:
1. Availability: extent of the service offered in terms of geography, time frequency and
transport mode.
a. Modes
b. Network: Range and extent of the mobility services on offer by reference to
time, geography and mode.
i. Distance to b/a-point: distance to boarding and alighting (b/a) point,
need for transfers, area covered
c. Operation
i. Operating hours, frequency, vehicle load factor: ratio of passengers
carried against total capacity of the vehicle
d. Suitability: Degree on how the services fit to the transport needs of the
individual customer.
e. Dependability: Degree to which the customer may be certain that the
services will be provided as published.
2. Accessibility: Access to the public transport system including interface with other
transport modes.
a. External interface
i. To pedestrians, to cyclists, to taxi users and to private car users
b. Internal interface
i. Entrances/exits, Internal movement, transfer to other transport modes
c. Ticketing availability
i. Acquisition on network, acquisition off network, validation
Customer View Service Provider view
Goals of Services for Mobility
47
3. Information: Systematic provision of knowledge about a public transport system to
assist the planning and execution of journeys.
a. General information
i. About availability, accessibility, sources of information, travel time,
customer care, comfort, security, environmental impact
b. Travel Information normal conditions
i. Street directions, b/a point identification (boarding and alighting),
vehicle direction signs, about route, about time, about fare, about type
of ticket
4. Time: aspects of time relevant to the planning and execution of the journeys.
a. Length of trip time
i. trip planning, access/egress, at b/-a points and transfer points, in
vehicle
b. Adherence to schedule
i. Punctuality: Adherence to published schedules by time
ii. Regularity: Adherence to published schedules by frequency/ interval
5. Customer care: Service elements introduced to effect the closest practicable match
between service and the requirements of any individual customer.
a. Commitment
i. Customer orientation, innovation and initiative
b. Customer interface
i. Enquires, complains, redress
c. Staff
i. Availability, commercial attitude, skills, appearance
d. Assistance
i. At service interruptions, for customers needing help
e. Ticketing options
i. Flexibility, concessionary tariffs, through ticketing, payment options,
consistent price calculations
6. Comfort: service elements introduced for the purpose of making public transport
journeys relaxing and pleasurable.
a. Usability of passenger facilities
i. At b/a points, On vehicles
b. Seating and personal space
i. In vehicle, At b/a-points
c. Ride comfort
i. Driving, Starting/stopping, External factors
d. Ambient conditions
i. Atmosphere, weather protection, cleanliness, brightness, congestion,
noise, other undesired activity
e. Complementary facilities
i. Toilets/washing, luggage & other objects, communication,
refreshments, commercial services, entertainment
f. Ergonomy
i. Ease of movement, furniture design
7. Security: Sense of personal protection experienced by customers.
a. Freedom from crime
i. Preventive design, lighting, visible monitoring, staff/police presence,
identified help points
Goals of Services for Mobility
48
b. Freedom from accident
i. Presence/visibility of supports e.g. handrails, avoidance/visibility of
hazards, active safeguarding by staff
c. Emergency management
i. Facilities and plans
8. Environmental Impact: Effect on the environment resulting from the provision of a
Public Transport service.
a. Pollution
i. Exhaust, noise, visual pollution, vibration, dust & dirt, odor, waste,
electromagnetic interference
b. Natural resources
i. energy, space
c. Infrastructure
i. Effects of vibration, wear on paths/road/rail, etc., demands on available
resources, disruption by other activities
Mobility Apps can provide information of these fields, nevertheless they can directly act in the
fields of information, customer care, comfort and security. Within these terms, the concepts
considered are availability, accessibility, travel time, customer care, comfort, security, safety,
sustainability, complementary services and ergonomy. This is a very complete norm, that can
consider many aspects of mobility, but it is only addressed to public transport and not to other
mobility services, although the ability of application exists. Apps can not only influence all the
aspects of mobility services, but they can be significantly useful for many important aspects,
such as security, ticketing or even triggering on demand in services like Uber or Mytaxi App.
The European ITS strategic plan is called “EC ITS Action Plan” for deployment and use of ITS
suggests measures and proposals on how ITS can contribute to a cleaner, safer and more
efficient transportation system. Its goal is to create the necessary momentum to speed up
market penetration of rather mature ITS applications and services in Europe. The initiative is
supported by five co-operating Directorates-General: DG Energy and Transport, DG
Information Society and Media, DG Research, DG-Enterprise and Industry and DG
environment, approved in April 2009 (ITS-EduNet 2005).
The Strategic Plan for ITS, outlines the goals related to safety, efficiency, sustainability,
innovation and interconnection. They break the goals down into vision, mission, priorities,
topics and categories. In the latter 3, they present plans and specific objectives (Barbaresso,
Cordahi et al. 2014).
Yujuico (2015) mentions the orientation of the App developed in their research in an official
way. The government of Manila has the App development policy towards economic
development, equity, efficiency and effectiveness of the mobility. They address effectiveness
in an organizational way neither clear enough nor relevant for this research. Zegras, K. Butts
et al. (2015) reports that the digital mobility services are and should be orientated towards
economic development, sustainability, innovation optimization of mobility and digitalization of
the current industry.
Goals of Services for Mobility
49
The proposal of a general infrastructure for location-based smart mobility services by Sassi,
Marco Mamei et al. (2014) is orientated towards the concept of the citizens happiness and
well-being in any transport mode, optimizing the network in a sustainable way. Their general
infrastructure is oriented to generate more innovation in App services.
These findings are visually demonstrated in Figure 29; the most popular goal is to optimize the
mobility network and sustainability, referring to recent climate change. Some authors did not
mention essential goals like economic development, safety and quality. Other goals like
intermodality, customer care, ergonomy and interconnection are rarely considered.
Heterogeneity and inconsistency dominates in the orientation of solutions, but, should all the
authors mention all the goals? Do all of them have to follow the same orientation? Is it possible
to highlight goals that are more important or graduate them? These questions will be answered
on the bases of the match with the individual needs and preferences in the next section.
Figure 29: Mentions of goals aggregated
In order to find a match between organizational and individual goals, they must be studied as
separated groups. More than expectations, costumer needs are the drivers for mobility
behavioral decisions. These needs can be external or internal. The internal conditions do not
depend on the person, and describe more his preferences, they can be explained by the
research analysis applied to mobility by Sußmann and Roemer (2011) based on the Maslow’s
Hierarchy of Needs (Kellingley 2016). The external conditions are based on the environment
surrounding the person.
For the internal conditions, Sußmann and Roemer (2011) has made an application of Maslow’s
hierarchy of human needs applied to mobility, it has been a good approach but it still needs
more accuracy. For instance, to what Maslow calls “physiological needs”, he assigns
Availability, Affordability and Safety, which is logic, but to what Maslow calls “Self-actualization”
Goals of Services for Mobility
50
he assigns luxury needs, prestige and entertainment which do not directly corresponds.
Therefore, this work re-organizes the hierarchical structure of customer needs in mobility.
From the organizational goals reviewed, some of them can be internalized in to human needs.
Others are strictly external since they are addressed to the systems or the environment and
not to the individuals or they are merely trends, such as intermodality, reduction of car usage,
digitalization, customer care, complementary services, and interconnection.
The Maslow’s hierarchical structure of human needs, shown in Figure 30, shows the most
essential needs at the bottom and the less essential needs at the top. It classifies and
prioritizes the human needs. This varies from person to person and from organization to
organization and some give more importance to less essential needs than to others. This
depends from region, style, personal goals and experiences (Huitt 2007).
Figure 30 Maslow’s hierarchical structure of customer needs for urban mobility
The most basic needs are physiological, at the base of the pyramid. At this level, the mobility
goals into consideration are availability, accessibility and the optimization of the Mobility
network. Availability means that the mobility service will be there for that person. The
accessibility is that the service is proper so the people can use it and the optimization of the
mobility network addresses the operations of the network. In the next level of priority, the safety
needs are situated. Here the goals of economic development, safety, security and affordability
can be allocated. The economic development allows the person and organizations to earn
money and be able to exchange products. Safety is related to the avoidance of accidents and
security is related to avoid crime. Affordability addresses the capacity of the people to reach
the mobility service by monetary means.
The needs of belonging and love can be addressed by the goals of social development in
which the mobility services foster the positive social interactions of the communities. The goal
of customer care reinforces the relation customer – mobility service with trust and
communication. The needs of esteem seek to personal achievement, competency, gain
approval and recognition. The goals addressing these needs are oriented to the human well-
Goals of Services for Mobility
51
being, in which the people feel respected and satisfied, the comfort in which the people is
happy using the services, ergonomics in which the services are fitted to the people, equity in
which the people feels respected and shows respect to others.
The needs of self-actualization are oriented to the growth of the person, once they have solved
their deficiency needs. In this frame the goals of Innovation, Life-style and Sustainability can
be located. Innovation is to find new solutions to reach beyond the average. Life-style connects
with the people’s playfulness and individuality while sustainability make the people think
beyond the ego and respect other natures that surround them.
The Apps for mobility address the goals of the mobility, nevertheless in a wide spectrum as
shown in Figure 31. Different Apps address different goals, there are Apps addressing
sustainability and Apps addressing the optimization of the network, i.e. CO2go and Uber, they
cannot be integrated in to other App to create more robust solution.
Figure 31: Organizational goals matching with the Apps roles
i. User Preferences
To confirm the users’ preferences, a survey was conducted. The process to design and apply
the survey are explained with the statistical concepts. The responses are analyzed with
descriptive and advanced statistical tools to better understand the sample taken and the
relations among the responses. In this section, firstly the theory is described, then the results
of the survey are presented and explained.
Goals of Services for Mobility
52
4.1.1 Theory for Statistical Analysis
As Merriam-Webster (1986) dictionary explains, predict and forecast are synonyms: “Predict,
commonly implies inference from facts or accepted laws of nature; Forecast adds the
implication of anticipating eventualities and differs from Predict in being usually concerned with
probabilities rather than certainties.” It is very important to know how to analyze the data before
collecting it, so the surveys’ responses can be adjusted to the planned analysis. There are two
main ways to analyze them, with basic descriptive statistics and with statistical models. Since
both will be applied in this study.
4.1.1.1 Descriptive Statistics
Descriptive Statistics simply describes the obtained data. The description of the measures to
convey information about just one variable in a dataset were taken from Kuhnimhof (2014) and
include:
Xth Percentile: X% of the observations are smaller than this value.
1st Quartile: Equals to the 25th percentile, which means that a quarter of the observations are
smaller than this value.
3rd Quartile: Equals to the 75th percentile; three quarters of the observations are smaller than
this vale.
Mode: most common value among the observations.
Median: Equals to the 50th percentile; half of the observations are smaller than this value.
Arithmetic mean: summarizes the information in the data in one numeric measure. It is the
sum of all observations divided by the number of observations. Data with a very high variance
or many outliers undermine the usefulness of the mean.
Harmonic mean: summarizes the information in the data in one numeric measure. This is
especially good to represent ratios. It is calculated by taking the sample size divided by the
sum of the multiplicative inverses of each observation.
Geometric mean: This mean is especially good to represent growth rates. The quotient of
every measure divided by the previous measure (growth/change) is multiplied and then, the
nth root is applied where “n” is the number of observations.
Variance: the average squared deviation of the individual observations from the mean.
Standard Deviation: the square root of the variance. It is easier to understand since its values
are more similar to the observations than the values of the variance.
Coefficient of variation: represents the data spread as a proportion of the mean.
Goals of Services for Mobility
53
Skewness: a property to describe on which end of the range the observations are more
concentrated.
The methods to provide information about the relationship of, more than one, different
variables are the Covariance and the Correlation ρ.
Covariance: measures how two variables change together. The covariance can take values
between 1 and -1. If both grow, it is positive, if one decreases as the other grows, then it is
negative. If there is no connection between the two, it is zero. A large covariance (close to 1
or -1 means that there is a strong association in which the values of the variables spread out
strongly.
Correlation ρ: improves the information given by the covariance, calculating the degree of
association between the two variables, dividing it by the two standard deviations. This division
transforms the values in to values between -1 and 1.
There are different ways to present the data, Kuhnimhof (2014) present the Histograms, Ogive,
Pie charts, Box-plots, Pyramids, Lorenz curves. In this research only Histograms, Pie charts
and Boxplots will be used. Histograms and Pie charts are self-explanatory. A graphic
explanation of the Boxplots is presented in Figure 32, using the corresponding concepts
previously explained.
Figure 32: Box Plots explanation, adapted from Intechopen (2016)
4.1.1.2 Sample Size
The appropriate sample size should be chosen so that the amount of responses provides of
confident information about the population it represents. It represents part of the group of
people (or population) whose opinions or behavior the research cares about. To understand
sample sizes, the information provided by the survey web-tools Survey Monkey (2016),
GreatBrook (2016) and Fritz Scheuer (2016) is presented:
Population size: The total number of people in the group the research encompasses is called
the population size. It is represented by the sample size. For instance, if the researcher is
interested in the population of Mexico, the population size would be about 110 million. Similarly,
Goals of Services for Mobility
54
if the research is about a specific company, the size of the population is the total number of
employees.
Margin of error: A result from a survey is unlikely to be exactly equal the true population
quantity of interest. The margin error is a percentage that describes how closely the answer
the sample is giving to the “true value” of the population. The smaller the margin of error is, the
closer the research is to having the precise answer at a given confidence level. If the margin
of error seems to be too big, the sample size should be increased.
Confidence level: A measure of how certain the analyst is that his sample accurately reflects
the population, within its margin of error. Common standards used by researchers are 90%,
95%, and 99%.
As an example, say it is needed to decide between two different names of the product, by the
population there are 100 000 000 potential customers in the target market. It is decided that
the industry standard of 3% margin error at a 95% confidence level is appropriate, then 1,068
surveys are needed. Given that 55% of the respondents chose name A and 45% chose name
B, it is very likely that the costumers would chose name A would be in a range from 52 000
000 to 58 000 000 (55% +/-3%).
The margin error and confidence levels have a stronger influence on the choice of the sample
size than the population size. This is illustrated in Figure 33.The smaller the margin error, the
larger the sample size is needed. The higher the confidence level is, the larger the sample size
is needed. The larger the population size, the sample size is not intensively influenced.
Figure 33: Statistical accuracy of a survey. Population size vs Sample size (as percentage of population) Adapted
from GreatBrook (2016)
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The mathematic equation of the graph above is explained here:
Where: population size = N
Margin of error = e (percentage into decimal form -for example, 3% = 0.03 -)
z-score = z
The z-score is the number of standard deviations a given proportion is away from the mean.
The right z-score to use can be obtained from several tables that look like Figure 34: Sample
size calculation - z-scores shows.
Desired confidence level
Z - score
80% 1.28
85% 1.44
90% 1.65
95% 1.96
99% 2.58 Figure 34: Sample size calculation - z-scores
4.1.1.3 Statistical Models
A statistical model is a probability distribution constructed to enable inferences to be drawn or
decisions made from data. A statistical model represents the variability of the sample using
probability distributions. Typically, it must accommodate both random and systematic variation.
The complexity of the model will depend on the problem at hand and the answer required, so
different models and analyses may be appropriate for a single set of data. The data has
variables, these can be taken as the explanatory variables, also called independent as well as
the explained variables, also called dependent or response variable. (Buechler 2007).
The selection of a statistical model depends on the question to answer and the types of values
of contained in the dataset for both, the explanatory and the explained variables as shown in
the Figure 35. Many more statistical models exist and will be created in the upcoming years
however, all of them indicate how strong is the relationship of one variable with the other ones.
Univariate analysis typically deals with the relationship between a single, dependent variable
and one or more independent variables The Multivariate analysis is different because it deals
with groups of both dependent and independent variables. The Multinomial analysis deals with
non-continuous and not ordered as well as non-numerical options (Bruin 2006)
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Figure 35: Type of model to be used according to the kind of data. Based on (Buechler 2007) and (Bruin 2006).
Figure 35 shows three main categories, the univariate, the nominal and the multivariate
analyses. These categories will be explained in the following section to illustrate the theory of
the procedure of this work. Specifically, only the linear regression, how the multivariate differs
from the univariates, and the multinomial modelling are explained. They are also widely used
models in econometrics for transport and science.
Linear Regression:
The theoretical explanation of the Linear regression follows the description of Verbeek (2004).
Regression analysis, a statistical technique which analyses how responses to questions vary
by specific characteristics and circumstances of individuals while holding all other
characteristics equal. The key benefit of regression analysis is that it provides a better method
of identifying those factors which matter most rather than an analysis looking at the relationship
between only two characteristics at a time. By understanding this relationship, the researcher
can estimate the value of the explained variable for a given set of explanatory variables, even
if this specific configuration has not been observed in the sample. An example for this could
be the prediction of an individual’s wage given the individual’s level of schooling, experience
in the workplace, test scores or other metrics.
These variables are connected through a linear relationship of the form:
𝑦 = 𝑓(𝑥1, 𝑥2, 𝑥3, … , 𝑥𝐾) + 𝜀
= 𝑥1𝛽1 + 𝑥2𝛽2 + 𝑥3𝛽3 … + 𝑥𝐾𝛽𝐾 + 𝜀
In this model, y is the dependent variable, x1 through xK are the independent variables, also
called regressors or covariates and ε is the error term. The error term captures any influences
on y that are not explained by the set of regressors. There are several potential effects that will
influence y, such as omitted variables, measurement error or random, non-systematic noise in
the data. The β1 to βK are parameters that inform us about the size and direction of the effect
of the associated regressor. The aim in regression analysis is to estimate these parameters
Type Model Type Model
All
continuousRegression continuous
Normal regression,
Anova, Ancova
All
categorical
Analysis of variance
(Anova)Binary Binary logistic analysis
Continuous
and
categorical
Analysis of covariance
(Ancova)Proportion Logistic regression
Count Log linear models
Nominal Multi-nomial
Many
explained
variables
Multivariate analysis
Explanatory variables One explained variable (Univariate)
Goals of Services for Mobility
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reliably in order to obtain information about the nature of the relationship between the
dependant and the independent variables.
The standard method to estimate a linear regression model is the estimation with ordinary least
squares (OLS). OLS estimation specifies a regression model that gives the best approximation
of y given a linear combination of covariates x1, x2, …, xK and a constant. For that the sample
coefficients b1, bK need to be chosen. So as to minimize the difference between the observed
value yi and its linear approximation b1x1, bKxK. Since the objective is to find the best
approximation for all observations from the sample, it can be written as
𝑆(𝑏) = ∑(
𝑁
𝑖=1
𝑦𝑖 − 𝑥𝑖′𝑏)²
By taking squares it is assure that positive and negative deviations do not cancel each other
out. This minimization problem solved in order to obtain the best linear unbiased estimator for
β, b.
Multivariate Analyses:
A multivariate statistical model is a model in which multiple explained variables are modeled
jointly. This can be explained based on the description of SAS (2016) The following equation
separate regressions represent two univariate models. So the relationships of one variable
with all the other variables is not influence by the relation of other variable for every subject.
Ç
In a multivariate setting the vectors and collect the responses and errors for the two
observation that belong to the same subject. This way a correlation can be identified. This
simple example shows only one approach to modeling multivariate data, through the use of
covariance structures. Other techniques involve seemingly unrelated regressions, systems of
linear equations, among others
Multinomial Choice Models:
Multinomial choice models can be used to analyse the choices individuals make between
options that are non-continuous and not ordered. This choice could for example be between
Goals of Services for Mobility
58
different car models and brands or a choice on commuting modes. Multinomial models belong
to a broader category called discrete choice models. These can be: Binary Logit, Binary Probit,
Multinomial Logit, Multinomial Probit and Conditional Logit among others.
The question for the researcher is then how model the probability of an individual i choosing
option j among a set of options. The individual’s decision will dependent on individual-specific
characteristics, xi, as well as characteristics that reflect the specific relation between the
individual and the option, wij.
4.1.2 Survey Design
The user’s needs can be identified according to the customer story of mobility, which every
person who wants to move from one place to other experiences. The user starts his activity at
one place at a certain time, he knows what his next activity is, either school or work, and where
it is located. How to get there? He needs information about the fastest, most reliable,
sustainable and cost effective way to get there and these aspects will shape his mobility
behavior. The trip can be divided into four parts: before the trip, at the start, transport, at the
end of the trip. In every stage of this trips, different services can be provided, they are:
Customer story Services needed
Before the trip
1. At the first part the user needs to
decide which is his next activity,
time and place.
2. Then he needs the information of
where and when a transport mode
can take him there, it can be either
car, Public Transport Unit, Bike, etc.
How much will it cost, how long will it take
Information for different transport modes (walking time, cycling stations, bus directions etc.)
Weather information
Schedule a trip
The start of the trip
1. He needs to get to his transport
mode from his current position.
2. He needs to get the key or ticket or
code or any requirement to allow
him enter the system and get the
service.
Information about where and at what time is the vehicle available
Best route to get from the current location to the position of the vehicle
Navigation to the station A
Real time Navigation
Transportation
Once in the system he needs to get to his destination, either automatically or by himself. For this he might need directions and hints.
Share dynamic location temporarily with specific persons
Panic button // fast connection in case of an emergency
Performance monitoring
Parking information
At the arrival
1. He needs to know he arrived. Navigation vehicle – destination
Trip rating
Goals of Services for Mobility
59
2. He needs knowledge on how to
move again to his next activity.
3. He needs to keep a record of the
trips.
Vehicle supervision / Panic button
Trip documentation
Figure 36: Services needed for four stages of the trip
Following the organization along the moments of the trip, the services to be provided can have
an infinity of solutions. This variety makes hard to propose and chose a specific solution.
Furthermore, the plurality of conditions in different cities or countries makes it harder even to
define the guidelines for an optimal solution. For instance, while at the end of the trip by car,
parking information is needed. There are a variety of ways to provide solutions for parking in
the market. These are mainly of three categories: prediction with public information, activated
by sensors or activated by the users (Pflügler, Schreieck et al. 2016). Therefore, it is needed
to check what is available in the market and how is it perceived by the users. A survey is the
most useful tool to evaluate user’s expectations and aid in the conception of new Apps for
mobility closer the user’s needs and global goals.
The aim of the survey is to explore the usage of the mobility services provided through
smartphone applications and how they are fulfilling the expectations of the people. To be
specific, it has the following objectives:
1. Personal modal share
2. Which is the priority of the needs to choose mobility services?
3. Which are the mobility services available in the App market?
4. Which is the frequency of usage of a service?
5. Which are the factors influencing the usage of a service?
6. Which is the degree of satisfaction of each service?
7. Which are the factors influencing the satisfaction of each service?
8. Which are the declared desires and needs of the users of mobility apps?
The objectives can be revealed when applying the following questions respectively, the
complete survey is shown in Appendix B and C.
How often do you use the following transport modes?
What are the reasons why you choose this transport mode?
The review of Krcmar and Schreieck (2016) was used as a guideline to define this.
How often do you use smartphone mobility services?
Descriptive Statistics were used to find this out.
How these mobility services address your expectations?
Statistical modeling based on a linear regression was used to find this out.
Is there a mobility App or function you want to have and have not found yet?
It also addresses the socioeconomic conditions of the respondents and served as a guide and
inspiration for this section. The World Bank used this information to create a sensitivity analysis
of the mobility situation of certain cities. From the approach of Prof. Zegras and Cottrill, Pereira
et al. (2013), the socio-demographic characteristics inspired this work. They collected this
information with an app, “the Future Mobility Survey” to generate a digital Origin-Destination
Goals of Services for Mobility
60
database for transport planning. The socio-demographic variables selected were: in which
human agglomeration lives the respondent; The age group of the respondent, these groups
were based on the categories Gender and commuting time. Additionally, smartphone
technologies’ savvy was measured as it is highly relevant for this study.
Sociodemographic questions:
1. In which city or village do you live?
2. In which age group you are?
3. Which is your gender?
4. How long is your daily commuting time (All-round-trip)?
5. How familiar to smartphone technologies do you consider yourself?
The answers correspond to every question, following a Likert scale. Each concept-question-
answer-machine langue is explained in the following section and are illustrated in Figure 37.
The first objective is to identify the “personal modal share”, it was asked in the survey how
often does every person use a certain transport mode. From the approach made by the World
Bank, the transport modes mentioned were chosen to be asked in this research. Walking,
Cycling, Public Transport, Car, Car-share, Taxi, Rideshare and Motorcycle. with a frequency
categorized as never, rarely (yearly), occasionally (monthly), Frequently (weekly) and very
frequently (daily). This is illustrated in Figure 37.
The next objective is to find the priority people set about the variables to make a mobility
behavior decision. The different approaches in the literature were analyzed and the relevant
features were applied to these questions. These approaches are the estimation of Mobility
behavior by Kopp (2015), presented at the course of Dr. Andreas Kopp at the TUM. The other
approach is the one followed by Future Mobility survey developed by MIT Prof. Christopher
Zegras (Zegras, K. Butts et al. 2015). The first one defined and fixed these variables and asked
the people for them. The other one focused on creating an Origin-Destination survey aided
with the smartphone data collection techniques. One defined and fixed these variables and
asked the people for them. The other one focused on creating an Origin-Destination survey
aided with the smartphone data collection techniques. Both examples of survey were
combined with the results of Chapter 4: Goals of Services for Mobility
, to present a list of the needs and goals to address. This list was presented to the respondents
and they were asked to prioritize them, the three most important, the three less important and
the rest six will be organized according to the multiple results received from the respondents.
To find out which Apps are currently available on the market, this research collaborated with
Krcmar and Schreieck (2016). It was more useful to use a Service-oriented taxonomy, than an
app-oriented one because of the heterogeneity of Apps providing different modular services
and not being directly related to the specific category. For instance, the journey planner by
Google Maps offers a web-based Map at the beginning, while the journey planner by MVG
offers text entries to calculate the journey. These features make them to belong to the category
Goals of Services for Mobility
61
of journey planner but not in the category of map services. These categories are fully explicated
in the section 3.1.3.
Figure 37: Variables and values explanation
In order to identify the factors influencing the usage of a service the respondents were asked,
how often they used a certain service. This question has discrete categorical answers ordered
in the way they are to be interpreted and understood by public and turned for the statistical
software into machine language.
Variable Question Subquestion
Name for
statistical
analysis
Concept For human languageMachine
language
0 - 18 younger than 18 1
19 - 24 19 - 24 2
25 - 30 25 - 30 3
31 - 40 31 - 40 4
41 - 60 41 - 60 5
61 + 61 or more 6
Male Male 1
Female Female 0
Commuting
time
How long is your
daily commuting
time?
Minutes (All-round-trip) 2_comm Commuting timeMinutes invested in mobility
dailyNumber
1 I do not use 1
2 I use it sometimes with few apps 2
3 I use it daily with few apps 3
4 I use it daily with many apps 4
5 I am an App developer 5
Location
In which city or
village do you
live?
Location city TextLocation. Aided by the geocoding
tool by Awesome Table
Coordinates
Lon, Lat
Walking
Cycling
Public Transport Never Never 0
Car share Rarely Sometimes every year 1
Taxi Occasionally Sometimes every month 2
Rideshare Frequently Sometimes every week 3
Motorbike Very frequently Sometimes every day 4
Journey planner
Navigation
Purchase Never 0
Charging station Rarely Sometimes every year 1
Parking assistance Occasionally Sometimes every month 2
Additional Info. Frequently Sometimes every week 3
Taxi Very frequently Sometimes every day 4
Journey planner
Navigation
Purchase Unknown I do not use the service Blank
Charging station Dissatisfaction It does not fulfill my expectation 0
Parking assistance Low satisfaction It fulfills a bit my expectations 1
Additional Info. Satisfaction It fulfills well my expectations 2
Taxi Over expectative Overpasses my expectations 3
GenderWhich is your
gender?gender_m
Variable / Question Values / Answers
AgeIn which age
group you are? age Selection
Selection
Frequency of
usage of
Smartphone
mobility
services
How often do you
use smartphone
mobility services?
5a_plan,
5b_nav,
5c_purch,
5d_charge,
5e_park,
5f_share,
5g_info,
5h_taxi
Satisfaction
degree with
mobility
services
through
smartphones
How these
mobility services
address your
expectations?
6a_plan,
6b_nav,
6c_purch,
6d_charge,
6e_park,
6f_share,
6g_info,
6h_taxi
Selection
Selection:
Selection:
Selection:
Smartphone
Savvy
How familiar to
smartphone
technologies do
you consider
yourself?
Smartp
Frequency of
usage of
Transport
mode
How often do you
use the following
transport modes?
3a_walk,
3b_bike,
3c_pubtra,
3d_car,
3e_cshare,
3f_taxi,
3g_rsha,
3h_mbike
Goals of Services for Mobility
62
To identify which are the factors influencing the usage of a smart mobility service, statistical
modeling based on linear regression analysis was applied. The theoretical analysis on which
statistical model to choose, is explained in section 4.1.1 Statistical testing. The dependent
variable, the frequency of App service usage, was tested with the socio-demographic variables
as independent ones: Smartphone savvy, commuting time and transport mode usage.
To identify whether a smart mobility service meets the expectations of the public, the direct
question “How these mobility services address your expectations?” was included in the survey.
Following the Likert scale (Select Statistical Services 2016), the possible answers are that the
respondents do not know the service or do not use it, no value, “Blank”, were used for the
statistical analysis of this value. Then, to indicate dissatisfaction, the value 1 is used for low
satisfaction. The value 2 is used to indicate a positive satisfaction. The value 3 is to indicate a
satisfaction above expectations of the public.
To identify which are the factors influencing the satisfaction about a smart mobility service,
statistical modeling based on linear regression analysis was applied. The theory is explained
is in section 4.1.1 of this work. The dependent variable was the question: “How these mobility
services address your expectations?” and the independent variables: Smartphone savvy,
frequency of usage of smart mobility services and commuting time.
Finally, and most important, to find out the declared needs and desires of the user, the open
question “Is there a mobility App or function you want to have and have not found yet? “was
proposed to answer, in which the user entered needs, desires, ideas and suggestions of
services to be developed.
4.1.3 Application of Survey
The survey was applied in a four weeks’ period from April 15th to May 15th 2016. Its distribution
channels were mainly online services, since the target population is people who use Apps and
the online channels are an efficient mean to reach them. The chosen channels were
professional associations (Mexican Society of Civil Engineers, Mexican Transportation
Engineers, CONACyT Mexican Scientists in Europe1), Alumni associations (Masters of
Science in Transportation Systems of the TUM2, Alumni of the Engineering Faculty of the
UNAM3, DAAD Scholarship holders4), Websites of Non-Governmental Institutions (Planeacion
y Desarrollo, Laboratorio para la Ciudad de Mexico) among other advocacy groups.
1 : CONACyT Stands for the Mexican Minister of Science and Technology, “Consejo Nacional de Ciencia y Tecnología”. 2 TUM stands for Technische Universität München 3 UNAM stands for National Autonomous University of Mexico 4 DAAD stands for the German Agency of Academic Exchange. “Deutsche Akademische Austausch Dienst“
Goals of Services for Mobility
63
The survey is an online questionnaire composed on nine questions and lasted about four
minutes. During its application it was monitored with a map and dynamic graphs of the
information collected Figure 50.
The whole website can be found in Appendix C. The Evaluation criteria of the Committee on
the Use of Humans as Experimental Subjects (COUHES 2015) of the Massachusetts Institute
of Technology (MIT) is used to perform this survey. Although the COUHES has not been
involved in this procedure, it has been taken and applied as a reference due to the following
reasons. Firstly, The COUHES framework is considered a fair procedure for the interviewer,
its institution and the respondents. All of them can be sure that their data will be accurate and
safe, the data will be collected in an unbiased way for the aims of the institution and the
respondents won’t be annoyed as well as their data will be kept safe. it is a well-known
procedure for the author, since he has applied it successfully on the study Zegras, K. Butts et
al. (2015). Thirdly, the COUHES procedure has been used to produce vast high quality
research for the Massachusetts Institute of Technology, with successful results. Based on the
COUHES guidelines, the following survey will be performed. In such a way 390 responses
were collected.
Before its application, the mentors of this work, peers of the author and a trail in the field tested
the questionnaire on different sectors of the population (young, old, people in early career,
adults, women and men). It has been found that the first objective of the survey, the priority of
goals for the people, was too complex, they did not understand it well and they have to invest
more than two or three minutes into it, skip it or leave it partially answered. To guarantee the
completion of the survey by respondents, it was decided to leave it out of the questionnaire.
4.1.3.1 Preparing Statistic Values
The population focus of this research is people using the internet, smartphones and
smartphone based mobility services. It was applied all over the world, since the smartphone
based mobility services are not strictly location based.
Let’s estimate the number of the population in focus. It can be said that every smart mobility
service user has a smartphone since they are needed to use these services. Also that every
smartphone user is an internet user since the most of the smartphone services are based on
internet services. Then the focused population for this research is smaller than the people
using smartphones and the people using the internet. The world has currently around 3.4 billion
people using the internet. Its geographic distribution per continent can be seen in
Goals of Services for Mobility
64
Figure 38: Internet users per continent. Since the most of the responses came from Europe
and south America, this research can focus in these regions. From the Figure 18 it can be
concluded that these regions account for the 21.8% and 19% of the internet users respectively,
summing 40.8% of the total of internet users. 3.4 billion * 40.8% = 1.38 billion people (Internet
Live Stats 2016).
Figure 38: Internet users per continent (Internet Live Stats 2016).
The smartphone users are also an important number for this research. According to Statista
(2014) there is a smartphone use penetration in percentage of the total global population of
25.3% in 2015. GSMA reports a market penetration of 78.9% for Europe, 70% for North
America and 52.3 for Latin America in 2014 (GSMA 2015). Since the population of these
regions is 7.35 billion for the globe, 0.739 for Europe, 0.641 billion for Latin America and 0.360
billion for North America. The smartphone users’ population in focus can be estimated as in
Figure 39, reaching a total of: 1.22 billion people. This is less than reported as the estimated
amount of internet users and is considered valid for this research.
Region Smartphone penetration
ratio
Total Population
Smartphone users
[%] [billion People] [billion People]
Europe 79% 0.739 0.583071
North America
70% 0.641 0.4487
Latin America
52% 0.36 0.18828
Sum for Population Size: 1.22
Goals of Services for Mobility
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Figure 39: Estimation of population size
The sample size for the population focus can be calculated following the procedure explained
in Sample Size. For a population of 1.2 billion people and a confidence level of 95% and a
margin of error of 5%, the sample size needed is 385 responses. For a population of 1.38
billion people, the same sample size is reported. In both cases a normal distribution of 50% is
used, this means, that the optimum sample size is the calculated number or even less, since
this normal distribution is the worst case and 385 responses is enough.
4.1.3.2 Monitoring of Survey Application
The data collected was dynamically analyzed and presented at the website shown in A. The
geographical progress of the survey’s collection can be seen in Figure 40: Progress of survey's
collection. Descriptive statistics of the gender distribution, age distribution, commuting travel
time and date of response for both versions, English and Spanish, was collected. The dynamic
monitoring of the data collection was applied to encourage people to take an active part in the
survey. When they see that not so many people from their region have answered the survey
yet, they will be more willing to take it. This was reported in emails and messages sent to the
author during the data collection period. The amount of responses collected per day can be
seen in Figure 40.
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Figure 40: Progress of survey's collection from April 15th to May 15th 2016.
4.1.4 Results of Survey
4.1.4.1 Descriptive Statistics
From the responses obtained, the sample of 390 is statistically valid with a confidence level of
90% and a marginal error of +/- 5% to represent a population of 1.3 billion people. The
descriptive statistics of the sample show that slightly more men than women answered the
survey, with 53% and 47% respectively. The age distribution of the sample is pretty similar to
the distribution of the population of the global internet users provided by Statista (2014). The
most of the users were between 15 and 34 years old, the second biggest group is of people
between 30 and 44. The least of the people belong to the groups older than 40 years. The
people of the sample reported in average a daily usage of smartphone with some Apps (more
than few and less than many); nevertheless, the three first quartiles report a daily usage, from
few Apps to many Apps. Developers are out of the three first quartiles but they are more than
people who do not use a Smartphone. See
Figure 42: Smartphone savvy level reported.
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Figure 41: Gender distribution of respondents
Figure 42: Smartphone savvy level reported
Figure 43: Boxplot of the reported commuting time
Figure 44: Age distribution of the sample
Figure 45: Distribution of internet users worldwide as of November 2014, by age group (Statista 2014)
Goals of Services for Mobility
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Figure 46: Average daily travel time as a function of GDP per capita (Schäfer 1998)
Figure 47: Frequency of usage of transport mode.
From the sample, the reported transport mode usage provides useful knowledge. The results
can be found in Figure 47. The most widely used transport mode is the public transport with a
weekly frequency from some times per month to everyday mainly. The next more popular
transport mode is the car, with an average of usage every month between weekly and yearly.
This finding denotes two things, firstly people use the Public Transport more often than the
car, and the car is not being used frequently.
The respondents reported to walk with a frequency from some days per week to never although
people walk every day. Only the percentile between the 75th and 95th of people understood
this phenomenon. Such an answer denotes how wrong the concept of walking as a transport
mode, pedestrian behaviour and non-motorized infrastructure is, and therefore it is often
Goals of Services for Mobility
69
overlooked. Unless there is a disability to walk, the frequency of walking should be 95% with
some outliers as people in wheelchairs. Cycling is also present as a transport mode growing
in importance, the sample reports cycling with a monthly usage between never and weekly.
Cycling has almost caught up the car usage, outstanding its importance as one of the main
transport modes and not an “alternative transport mode” anymore.
The least used transport modes are Taxis, Car-share, Ride-share and motorbikes. People
chose the Taxi on a yearly basis in average, between never and monthly. Nevertheless, the
responses between the 75th and 95th percentile reported a more frequent usage up to a daily
usage.
Car sharing is being used on a low yearly basis in average, between sometimes per year and
never. The responses reach a monthly usage in the percentile 95th and outliers reported to use
it on a weekly basis. Ride sharing is reported in average usage less frequent than yearly,
between never and sometimes per year. Its 95th percentile reach only a monthly usage,
nevertheless some outliers use it on a daily basis.
Motorcycle is rarely used. It is reported in average between never and sometimes per year
with the 95 percentiles in never. Some outliers use it on a yearly, monthly, weekly and daily
basis. This denotes the motorcycle as a very specific and uncommon transport mode for the
sample.
Then the idea of the car as an everyday transport mode is questioned towards a more
multimodal behaviour in a week scope, in which people use certain transport modes at some
days, and other modes in other times depending on their activities. Instead of different transport
modes for the same trip. Furthermore, few of the respondents associated walking with the
usage of different transport modes. This can be a big explanation to the general overlook of
the pedestrian infrastructure, regulations and general attention.
The frequency of smart mobility services usage was also evaluated; its results are presented
in Figure 48. The journey planners were indicated as the service of planning before the trip
(which stations, lines, transfers, costs, etc.) - e.g. MVG, BVG, Google maps -. The journey
planners were the most widely used services, with an average on a weekly basis. Some
outliers use them yearly or do not use them at all. The navigation service, indicated as real-
time directions - e.g. Google Maps, Tom-tom, Waze -, is being used on a weekly basis by the
sample. Its usage ranges from monthly to weekly, with some people using it daily or yearly
(Figure 48).
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Figure 48: Frequency of usage of Smartphone based mobility service
The service to purchase tickets, MVG, Deutsche Bahn, Moovel, etc. present a yearly average
usage, from the 1st to the 75th percentile the range is from never to on a monthly basis. To the
95th percentile a daily frequency is reached. Both the service of information of charging stations
- e.g. Chargepoint, Tanktaler, BMW Charge now- and the parking assistance services
(location, reservation, prediction, etc.) - e.g. Park pocket, Parknav, Parknow – have a similar
frequency of usage. They represent a low usage of nearly never. Some outliers reported to
use them on a yearly, monthly, weekly and daily basis. Ride-, Car-, and bike sharing Apps E.g.
Carpooling, Blablacar, Nextbike, Car2Go, Ecobici – are reported as not often used. Their
usage ranges from never to sometimes per month with outliers using them also weekly and
daily. Apps that provide additional information for transport (traffic, infrastructure, locations,
etc.) - e.g. Waze, Blitzer, Stau info – are still rarely used, with a monthly frequency or fewer,
but still the 75th percentile reach a daily usage. A similar frequency is observed for taxis ( Figure
48).
The service satisfaction presented a more similar distribution among the services (Figure 49).
The Journey planner and navigation presented an intense concentration of the values in
satisfaction, with the three first quartiles and the mean value set at “satisfied”. They were the
highest evaluated services together with taxi services. Taxi services were symmetrically
distributed with the mean value on “satisfied”. The services to purchase tickets and taxi
services represent a slightly higher satisfaction than the services of charging stations, parking
assistance, Ride-, Car-, Bike- Sharing and additional information. Nevertheless, all of them
reach values between satisfaction fulfilled and low satisfaction. The fact that none of the
service reached a satisfaction level closer to low satisfaction than to above expectations can
be attributed to the feeling of play the “hard to get” of the people, where they express being
less satisfied than they really are. Another explanation can be that they got used to the services
after a long time using them and now they are not surprised anymore (Figure 49).
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Figure 49: Satisfaction with smartphone based mobility services
The high usage of route planners and navigation Apps is significantly higher than the usage of
the other services. This situation might be due to three factors, the frequency of the necessity
to use the services and the maturity of the technology. Route planners have been in the market
for already more than five years while parking Apps are still in development. Besides, the
availability of such services might be more common in cities with well-developed public
transport system than in cities without them. Since the Apps can easily permeate through
countries, the degree of connectivity the mobility services of city have with the internet, or how
“smart” the mobility of a city is seeming to be more relevant.
The taxi as a transport mode is not widely used but their Apps present a high degree of
satisfaction. This can also be reflected in the rapid grow of companies like Uber, Lyft and
Cabify. In the following section, the services they provide will be analyzed to identify if the
additional services play an important role.
Services like trip planning and real-time navigation are highly popular and highly satisfactory,
but there is still work to do. People’s expectations are not overpassed yet and in the comment
section the battery demand of the GPS, preciseness of GPS and transport routes or the
existence of the service is pointed out, which seems to be available only in well-developed
cities.
4.1.4.2 Geography
The geographical positions of the responses were also collected in the survey. The spatial
distribution of the responses is concentrated mainly in Germany and Mexico, many of the
responses came from other countries such as France, Spain, Italy, Chile and the United States.
Few responses came from other places in the world. Therefore, this research concentrated in
the population of Europe and the Americas (Figure 50). Since the smartphone-based mobility
services are not strictly location based Apps can be found and used independently of the
Goals of Services for Mobility
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country, or city, their spatial distribution is taken only as an incentive to get more answers from
different places of the world.
A dynamic map that tracks the location of the responses in real time was built to make the
research more interesting. The respondents answered the survey in google forms. The google
form has an “Add-in” to geocode the keywords in the location column, in this case location.
The Add-in transforms keywords in to coordinates for Longitude and Latitude. It is provided by
the company called “Awesome Table”. The form is connected to a google fusion table that is
updating it in real time. The google fusion table is being read continuously as a database by
CartoDB. CartoDB creates the map. The map’s HTML code has been embedded in the website
of the survey. See Appendix C: Survey Website.
Figure 50: Geographic Distribution of the responses
4.1.4.3 Linear Regression
To test what people are more likely to use which services, a linear regression was used through
the software R. The linear regression is a tool considered strong enough to identify the
relationship within variables even if they not continuous but categorical. The experiment is
mathematically explained as:
Frequency of use of each service ~ Smartphone Savvy + Commuting time + Modal choices
Different variables influence the usage of different services. It has been found also that not all
the correlations meant causation; therefore, it is needed to rethink their relationships. In all the
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cases, the linear regression presented a good fit. In none of the cases, the commuting time
influenced the usage of an App.
For each experiment, two conditions are checked. Firstly, how well the regression fitted to the
experiment, also called goodness of fit. Secondly, how strong the relationship of the dependent
variable with the independents is. To check the goodness of fit, two conditions were checked,
firstly how well distributed the residuals around cero are and the R-squared or overall model
fit, the smaller the better. Then to check the relationship within the variables, the “Estimated
coefficients”, “t values”, Standard error and “Pr(>|t|)” – calculated probability- of every variable
were considered. The estimates define the equation of the regression line and show how
strong the relationship is.
When evaluating the factors influencing the usage of a journey planner (X5a_plan) it was found
that the variables of smartphone savvy and the frequent usage of public transport had a strong
influence on it. To check for a good fit, the residuals and the R-squared value were checked.
The residuals (Min, 1Q, Median, 3Q, Max) proved to be symmetrical around cero. The R-
squared value is 0.2922 as an overall fit, which means that the linear regression presents a
good overall fit for these variables. When focusing on the relationship within the variables, the
biggest estimated coefficients were Smartphone savvy (Smartp) and Usage of Public
Transport (X3c_pubtra) with 0.565 and 0.218 respectively). Their t values are also far from
cero (8.81 and 4.04) and their calculated probability (Pr(>|t|)) is very small. These conditions
indicate a strong relation between these variables. Also the software R indicates a strong
relationship is with ***, **, *, ,(in a decreasing order) at the right side. The other variables do
not show a relationship with the dependent variable.
Call: lm(formula = X5a_plan ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -3.7468 -0.5490 0.1758 0.7813 2.1346 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1790773 0.3370600 -0.531 0.596 Smartp 0.5655547 0.0641892 8.811 < 2e-16 *** X2_comm 0.0009234 0.0021620 0.427 0.670 X3a_walk 0.0366868 0.0578974 0.634 0.527 X3b_bike 0.0724870 0.0411305 1.762 0.079 . X3d_car 0.0100924 0.0462439 0.218 0.827 X3c_pubtra 0.2189751 0.0541215 4.046 6.52e-05 *** X3e_cshare -0.0550128 0.0806510 -0.682 0.496 X3f_taxi -0.0155837 0.0590562 -0.264 0.792 X3g_rsha 0.0215203 0.0751882 0.286 0.775 X3h_mbike -0.1190209 0.0794651 -1.498 0.135 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Goals of Services for Mobility
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Residual standard error: 1.083 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.2922, Adjusted R-squared: 0.2704 F-statistic: 13.38 on 10 and 324 DF, p-value: < 2.2e-16
Following the similar procedure, the analysis was conducted for each smartphone based
mobility service. A summary is explained in this section. The full report of the results can be
found in Appendix D: Programming Code in R for Linear Regressions and Appendix E: Results
of Linear Regressions
For the navigation service (X5b_nav) it was found a good fit of the linear regression to the
model. Other variables have also strong coefficients but their P-value was reported too big and
therefore they are not marked with the star. Only the usage of Smartphone and the car were
strong independent variables influencing the usage of a navigation. This might be clear
because the most of the navigation services are dedicated to driving, there is no need in
navigation in Public Transport, Cycling or Walking.
For the services to purchase tickets (X5c_purch), the R squared value of (0.113) is small but
the residuals are not distributed symmetrically around cero. Then the fitness is not so good.
There was also found a strong relation between the variables, smartphone savvy (Est. Coef.
=0.23, t value=3.47) and taxi usage (Est. Coef. =0.21, t value =3.41). However, in the reality,
the usage of taxi has no relation to the preference of App usage to purchase tickets in different
transport modes. Then such kind of influence should have another explanation.
For the services of information of charging stations (x5d_charge), the R-squared value of
(0.077) is very good but the residuals are not symmetrically distributed around cero. None of
the variables presented favorable values in their coefficients, t-values nor P-values for a
correlation. The usage of taxi (X3f_taxi) presented some hints of a correlation and is marked
with two stars **. When analyzing what can be the relationship between taxi usage and
information of charging stations there is not much sense. Therefore, although there is a
correlation, there is no relation. “Correlation is no causation”.
Similar results presented the analysis for the services of Parking Assistance services
(X5e_park). A good R-squared value but no symmetry within the residual, then a poor
goodness of fit. Only favorable values for a correlation with the usage of motorcycles which
makes no logic.
The analysis of the influences of rideshare has shown a positive goodness of fit with an R-
squared value of (0.291) and residuals slightly symmetrical. The variables Smartphone Savvy
(Smartp), usage of a bicycle (X3b_bike), usage of car, usage of taxi (X3f_taxi) and usage of
rideshare (X3g_rsha) presented favorable values for a correlation, Nevertheless, only the
smartphone savvy, the usage of a car, and the usage of ride share make sense. This indicates
Goals of Services for Mobility
75
that the people practicing car-sharing and ridesharing are highly likely to use an App for these
services.
The usage of services to acquire additional information (X5g_info) present a good fit with the
linear regression (R-squared=1.30), Residuals symmetrical enough. From the independent
variables, only the Smartphone savvy and the car usage have a strong correlation with the
usage of information Apps. This can be caused because the most of extra information is for
cars, like traffic, the position of traffic control cameras or the infrastructure state. In the declared
answers people indicate an inexistent service to monitor the state of the infrastructure or the
traffic state for bicycles or pedestrians i.e.
For the Apps providing the service of taxi hailing (X5h_taxi), the R-squared value of 0.337 is
too big but the residuals present symmetry around 0, then the fit can be considered good
enough. For this analysis, the independent variable, Smartphone (Smartp) and taxi usage
(X3f_taxi) presented a correlation. It is clear that the more someone use a taxi (or Uber), the
more they are willing to use an App for that.
4.1.4.4 Declared Preferences
To be more specific for the services the people really want, they were asked to report their
preferred service through smartphone Apps. They responses have been read, conveyed and
summarized in the following statements. Figure 51 presents an aggregated list of these
responses.
The term “More cities” were taken when the people expressed they would like to have in their
city a service that already exists, or that they would like to have many cities covered with the
same service.
The bus is often considered as ignored by Apps. Their lack of information and lack of precision
can be translated in to a low reliability of the bus networks. The term Multi-modality was
assigned when people expressed they wanted to have more transport modes integrated in to
their apps, like cycling, Public bicycles or taxis. Buses were often called and therefore they will
have their own term.
This is what the people express they would like to have. The people is not an expert so their
answers are merely indicators of the possibility of success of a strategy and not precisely what
should it do. Some of these services already exist. A local platform indicating what works and
what does not, would propose the best services for each location.
Goals of Services for Mobility
76
Figure 51: Modular services as responses of declared preference.
Services are highly interconnected; therefore, it is hard to locate suggestions only to one
service while it addresses many service modules. For example, people said Bus information
and navigation. This would address the Route planning service, the map view and the
navigation services.
One of the biggest barriers or limits of Apps are the data sources. An App can reach what the
data allows it to. The people might wish it, the organizations consider it; but the conditions set
by the data will define the limits of the development of the technologies.
Analyzed
service
related to:
Response concept Freq.Already
exists?
Bus information 4
Integrate a delay alarm 1
Integrate possible delays and
warnings1
Journey planner multi-modal and
multi-city for long distance traveling1
Multimodality 5
Preciseness 4
More cities 8
Integration of energy consumption
and reliability1
Frequency based shortest paths 1
Bus information 2
Parking in critical areas 2
Multimodality 3
Preciseness for addresses 1
Proper cycle lane indicators 1
show routes of ride share offers 1
Easier carpooling apps 1
all car sharing services in one app 1
higher quality 2
Motorcycle share 1
Accidents and traffic jams 1
Location based probability to have an
accident 1
Location based probability of crime 1
Route tracker and predictor 1
Multimodal navigation 7
Multimodality 1
Bus information 1
Cycling navigation 3
Pu.T. Navigation 3
Speed recommendations 1
App versions for disabled people 1
Multi-Lingual services with local
major dialects / translations 1
Orientation for Rural villages 1
Traffic flow in real time 1
Integrate with official information of
traffic 1
Acc
ess
ibil
ity
Traf
fic
info
Car
-/B
ike
-, R
ide
-
shar
ing
Nav
igat
ion
Ro
ute
pla
nn
ing
Map
vie
w
Loca
tio
n-
bas
ed
info
rmat
ion
Analyzed service related Response concept Freq.Already
exists?
Points of Interest (POI) Preciseness for addresses 1
Parking information Parking information 3
Dynamic Location sharing Preciseness 1
location sharingDynamic locations of
public transits vehicles1
Matching user to userCycle-share and Walk-
share / companions1
Charging stationsGas stations + additional
services (Restaurants, etc)2
Traveling analytics Freight transport tracking 1
Other responses related
to:Response concept Freq.
Already
exists?
Walking Safe walking app 3
Cycling app 3
Cycling app integrating
safety1
Routes, Workshops, Shops
for cycling1
Safety and securityIntegrate safety and
security in the algorithm5
Integrate weather
information2
Connection with
emergency services 1
Autonomous car hailing 2
Cable cars in the alps 1
Cycling
Integration
Future
Comparison of local and
international bus and train costs1
Illustrated train seating-maps (with
advises) - similar as SeatGuru for
aircaft seating
1
cheapest flight tickets without
indication of destination (Like in Ryan
air)
1
multi national and multi-modal
planning/ booking/ Paying/
Navigation app
1
To enter and generate O/D
information2
Low carbon mobility 1
Apps with low Battery consumption 3
Wi-Fi in transport 1
Location more precise than GPS 1
Pre
sen
tH
ard
war
e
Services and Data. Smart Infrastructure vs Smartphone Apps
77
5 Services and Data. Smart Infrastructure vs Smartphone
Apps
This chapter conveys what is known about both service systems environments, smart
infrastructure and mobility services via smartphone. Firstly, it sets an economic framework
through the market size and the popularity of smartphones. Then it goes to the factors of
success of the market penetration of digital mobility services and the goal orientation their
users follow. A special focus is given to the privacy issues and the capabilities of influence of
each environment. Additionally, an outlook for data collection techniques and companies is
presented.
The emerging Intelligent Mobility Market is composed of activities dedicated to move people,
goods or data in an environmentally friendly and efficient way using digital technologies.
According to Catapult Transport Systems (2015) its market is about to grow from its current
annual value of € 167 billion around to € 1.07 trillion euros per year in 2025, it means it will
grow 6.5 times bigger in ten years. Therefore, research, development and innovations have
big opportunities in this field.
Transport and mobility companies are turning in to data analysis companies. Researches have
explored the application of IT within ITS’ solutions by means of Apps and different IT products
and services. Further than apps, Vishwanath, Gan et al. (2014) design a mobility model for
urban transportation that leverages developments in the IT sector. The science in ITS is
evolving, the internet of things creates the term of “Smart Cities”, developing “smart mobility”
services, new concepts are being developed focused on people, vehicles, infrastructure and
devices (Dimitrakopoulos and Demestichas 2010, Wolter 2012). Kapoor, Weerakkody et al.
(2015) suggest digital solutions to foster citizens’ involvement in transportation decisions and
(Cottrill, Pereira et al. 2013) designed a tool to monitor citizens to track their mobility behavior.
Many of the recent ITS developments are based on large amounts of data from different
sources, making ITS a more and more data-driven discipline (Zhang, Wang et al. 2011).
Continuous GPS based data offer greater than 98% correlation across different roads, traffic
and weather conditions. GPS can be highly battery demanding, regular drivers are rarely likely
to have their phones plugged in their charger, but sensor based techniques combined with
map and crowdsourcing data can achieve more than 94% of correlation. Smartphones can
offer reliable speed, location estimates and furthermore, other contextual information
(locations, SMS reads, email distractions), therefore, they can be considered as super-set of
On-Board Diagnostic Devices (Meng, Mao et al. 2015). Several pilot projects worldwide have
demonstrated the technical feasibility of FCD (from cellular phones) with globally good results
compared to traditional collection methods (Leduc 2008).
ITS started in the 80’s and they are popular in industrialized countries. ITS require large
investments and political stability for infrastructure decision. The digital mobility services
deployed through Apps started in 2008 and now they are a key aspect of any mobility service.
Services and Data. Smart Infrastructure vs Smartphone Apps
78
Their only dependence is the smartphone ownership of the public and its capitalization to keep
the Apps running, which is not high. This is a small investment taken by the users and not by
governments. Users are willing to acquire the services at low costs. This way, it can easier
penetrate in the markets.
The declared responses from the user collected through the survey were analyzed, quantified
and compared with Maslow’s pyramid of human needs. This is shown in Figure 52. This
analysis shows that they are mostly focused on the upper categories. These categories are
the less basic and essential for the human being, however, they caught a significant attention.
The full quantitative analysis is shown in Appendix F: Goals addressed by declared service
responses.
Figure 52:Goals and Needs Addressed by Declared Responses from Users.
In terms of coverage, the Smart Infrastructure sensors have proven limited local areas of use,
so that a huge number of devices must be installed to determine the traffic situation in a wide
area. This situation is eased with smartphones, since they are there where people is located,
so the most popular areas will be well monitored and the less demanded areas will have less
measurements. It only takes the phones of 2-3% of the drivers on the road to provide accurate
velocities of traffic (Herrera, Work et al. 2010). Figures by Forrester (2013) show an individual’s
mobility behavior is more and more impacted by digital services used mostly via smartphones.
For example, Google Maps was nowadays the 6th most used App in 2015, offering routing,
navigation and a variety of additional features for individuals travelling within or between cities
(Jonkers and Gorris 2015). And the popularity of the smartphone is intensively growing as
Figure 53 shows.
Services and Data. Smart Infrastructure vs Smartphone Apps
79
Figure 53: Percentage of adults who reported smartphone ownership, and its change between 2013 and 2015 in selected countries (PEW Research Center 2015, World Economic Forum 2016).
It is proven that there are more diverse data in smartphones than it is in ITS, although the
smartphones have only a few sensors, the services they provide are often combining the data
from different kinds and sources. These sources are also more fragmented, namely the owners
are from different natures (Governments, other companies, NGO’s, etc.). Also the data used
to provide services through smartphones is considered to be more sensitive, since the degree
of consciousness the users have over its source (their phones) is high as well as their
understanding of the phone’s Apps is low. They have the feeling that the Apps will take other
sensitive information more than what they need. Sadly, they often do. For instance, a map
application request access to the contact list of the user; which does not make sense for the
services they provide. The smart infrastructure collects information with different sensors and
these are a well mature technology, with highly standardized data formats that rarely changes.
They belong to the governments, collect information of the vehicles or people passing by and
nothing else; therefore, the mistrust in them is lower.
For the services provided, the Smart Infrastructure provides basic services, decided often by
a top-down approach and far from what the users want. Clearly, the most of them are provided
by the governments. On the side of the smartphones, these services are vast and
heterogeneous, they provide services very close to the customers and easy to understand.
Often they are regional, so their services adjust very well to regional conditions and can be
adapted for an international deployment but are not precisely inter-regional services. Once the
smartphone is popular all over the world, the Apps can be used in many other places.
The analysis of the service systems of ITS and Apps applied was based on Busch (2014). He
analyses main capabilities of information for transport as the scope of influence, the type of
information sent, the type of regulation and if the information is in real time. This analysis shows
Services and Data. Smart Infrastructure vs Smartphone Apps
80
that both service systems environments, the ITS and Apps, can have similar capabilities,
depending on which service systems is in focus (Figure 54).
Figure 54: Categories of services provided by ITS and Apps. Own elaboration with information from Busch (2014)
The Traffic Management and Control Systems can influence the movements of the travelers
before (pre-trip) or during the trip (on – trip) through three levels of intervention:
1. Information
2. Recommendation and guidelines
3. Regulation and control
Two main differences arise. Firstly, that the type of information sent is highly individualized
through Smartphone Apps in comparison with ITS. The reason can be simply how close to the
user’s the smartphones are. Secondly that none of the Apps is being used for regulation
functions. This can be again due to the privacy concerns of the data being transmitted through
the smartphones. The contact between the user and the smartphone is way closer than with
any transport vehicle, namely car, bus or train i.e. therefore the phone is able to measure many
other variables relevant for mobility like perceptions or opinions rather than only vehicles
passing. Besides, it can provide a communication channel in three directions instead of only
in one. Namely, the user can receive, enter and share information.
These levels of intervention are also covered with Apps and smartphones. Until now 2016,
Smartphones provide information, recommendation and guidelines but no regulation and
control App has been found. The App environment is still too young, free, un-regulated. IT has
not been seen a service trying to penalize users. Since the smartphones can access so much
information about its owner like, the places visited, conversations and interests, any
intervention with them is highly sensitive. In very critical cases the police can have access to
Services and Data. Smart Infrastructure vs Smartphone Apps
81
the cellphones, only under the permission of a judge and under strong control. This rarely
happens and is highly polemic. In Europe the terms of data protection are stated in the personal
data protection laws (European Comission 2016).
The data collection techniques are very different according to the technology of the sensors
and the aim of the data. New techniques are being developed to collect the data with
smartphones and they are merging datasets, clarifying or drawing new conclusions after
collecting different datasets. For instance, Lee and Gerla (2010) focused only on Vehicular
Sensing Networks, combining data collected and processed from cars (FCD) with the data
collected by embedded sensors as well as Leonhardt (2008) developed a machine learning
algorithm to merge data from Floating Car data and Smart Infrastructure.
Digital mobility services often start like a small start-up and then take capital from major
technology companies until they are absorbed by them. This situation makes the market harder
for small enterprises who want to grow independently. Moreover, the expectation of no-cost
from the users makes it harder for developers. Users expect the services for free or at a very
low cost. Half of iOS programmers and 64% of Android Developers operate under a “Poverty
line” of US$ 500 per App per month. This “expectation of free” has been created by the
crowdsourcing business models deployed successfully by major tech firms for the big scale
but they do not apply for smaller providers. Currently, the attitudes to personal data privacy
and concentrations of market power and legislation slow the potential threads these business
models can do to the industry (Catapult Transport Systems 2015).
In Summary, the intelligent Mobility Market is a huge market and is going to grow intensively
in the next ten years. The solutions based on Smart Infrastructure have proven to be a precise,
resilient, and trusted while the solutions based on Smartphones are unstructured, de-regulated
and variated. The latter grow and expand rapidly and at low costs with lack of trust from their
users. The cooperation of Smart Infrastructure and Smartphone based Apps can be an efficient
way to convey the successes of each one and overcome their limitations.
Solution: A Platform of Modular Services
82
6 Solution: A Platform of Modular Services
Many services are available in the market. Many are not popular but they are very useful.
They might not be practical since they are only one single modular service, instead of a service
system. Nevertheless, as shown in the service analysis, service systems are also fragmented
and spread out, namely, they provide different modular services. For instance, if the user wants
to calculate a route by public transport he might use google maps, then if they want to be
navigated in their trips he will have to change to Moovel.
Services provided by Apps are very popular but solve basic issues of mobility with complex
programming programs. They are mostly service systems offering many modular services.
More specialized services are not very popular and are not interrelated with many elements of
the service system. They are often oriented for a specific audience for example, the
Flocktracker, offering a tool for specialists in city planning to collect data. These can build
extensions for the service systems.
The development of Digital Mobility Services, Services for navigation of cars will be taken by
vehicle technologies (sensors and interfaces) more than by Apps. Apps will focus on services
closer to human behavior (walking, Public Transport, Activity planning). Every App is awesome
from the view of its creator, but there are stronger Apps needed.
Official Apps seem basic in comparison to private ones, but they have direct access to the
information and therefore they are more reliable. The App Bayern Info has all the datasets
belonging to the traffic authority of the state of Bavaria in Germany, nevertheless the interface
of it is not very user-friendly and that’s why users prefer to use Waze, the crowdsourcing based
traffic navigator. The traffic authority of the state of Bavaria is not willing to disclose their data
due to the data protection and data safety. The integration of data from public authorities, and
programming skills from motivated teams from the private sector would take the industry closer
to an optimum.
Due to the fragmentation of the services, their limited features and the concerns of many
institution (data owners) for the deployment of data. A platform where different services
systems for mobility, data analysis tools and data sets can be found and traded would boost
and increase the quality of the Apps in general as well as ease its development.
The main key resources of an App are the data and the capabilities of the developers. They
have to match to generate a successful service. But what often happens is that the raw data
can be too complex and hard to understand for the developers. Easy to use and analyze data
is often hard to find and use. These are the main barriers that generate weaker Apps than the
what was planned. Based on the response of the survey for a Walking app, a customer story
will illustrate the situation for the developers, for the users and for the data owners. Afterwards
it will be shown how the platform can deal with it.
Solution: A Platform of Modular Services
83
Explanation for the IT Developer
Example of customer story
Firstly, the idea either from
an IT developer,
entrepreneur or company
is to be developed. This
involves the creation of an
App, a data analysis tool
or a website. The concept
is defined, the final product
and its capabilities are
defined too.
For instance, an App for walking is to be developed.
Apart from the capabilities
of the IT developers,
external key resources are
need to build the solution,
for instance data and
modular services.
Services are needed such as an account manager, a user’s
GPS locator, A map, a route calculator and a navigator.
For walking comfort, the datasets needed would be noise levels
on street, sidewalks quality and location of stairs.
For walking security, the databases needed would be street
lighting, spatial distribution of crime, land uses and pedestrian
flows.
The developers find only
some key resources, not
all of them are available,
up to date or suit to the
solution to be built.
They found services in form of an API of a base map, the route
calculator, navigator and an API of a user’s GPS locator. They
did not find and will have to build the account manager.
The developers found only the following datasets: the air quality
measurements and analysis, sidewalk quality, stairs location,
street lighting, spatial distribution of crime and land uses. No
Noise levels on streets or pedestrian flows exist for this city.
They inquire to the
institutions to access to
the data sets. The
developer should contact
many different
stakeholders in different
forms and times.
The air quality measurements and analysis belong to the
environmental authority. The sidewalk quality, stairs location
and street lighting belong to the construction authority of each
municipality of the city. The Crime distribution dataset to the
federal police. The land uses to the trade chamber. The map
and the route calculator are open source. The navigator and the
GPS locator belongs to the private company.
They are granted to
access to some datasets
and services, not to all of
them and sometimes
under special conditions.
They got access to the air quality measurements and analysis
in real time. They can have the dataset of sidewalk quality but
only for one measurement taken once last year. They can
access to the stairs location and street lighting but have to wait
two months after inquiring for it. The land uses dataset has
expensive costs. The crime distribution is confidential
information and cannot be accessed. The map is available for
free. The user’s GPS can be used only if this provider can store
the data generated by the users.
Solution: A Platform of Modular Services
84
From all these data sets,
the developer can analyze
and understand only a
restricted amount of them.
They can only understand the air quality analysis and the stairs
positions. They also agree to pay for the land uses’ data set.
The stairs locations are in many different plains, impossible to
be built in the solution. He uses the map and the user’s GPS
locator.
The data that can be built
on the App is limiting the
services it was intended to
provide.
They can only build an App for walking providing comfort based
on air quality and stairs as well as security based on land uses.
The map works well but the user’s GPS locator makes the
solution non anonymous anymore.
The planned solution has been shrink, and the quality of the
planned services has decreased.
Figure 55: Schematic comparison of digital mobility solution planned vs. developed.
Solution: A Platform of Modular Services
85
The data sets and services availability is limiting the capabilities of the developers. Moreover,
in the future the IT-developers will have to check for the datasets updates with each of the
different institutions. The solutions the IT developers can provide have a potential bigger than
the one seen currently on the market.
This concept of architecture for the platform will allow the open access to modular services
and data sets to develop solutions in the smart cities. IT developers will have a common place
to find data sets, data services and end products to develop more solutions and deploy them
there. The data collected by the authorities with sensors in the infrastructure will be used more
frequently generating more services and conveniences in the city. Furthermore, data providers
can get a share of the profits of the solution developed as well as the authors of the platform.
A new governance concept will have to be developed to regulate the shares of benefits, privacy
of usage and data as well as data sets storage and transfer. It will ease the IT-developers to
produce the planned services in a regulated and standardized way.
In the users’ perspective, a customer story is presented. They only expect to have a service
that fulfills their needs and preferences. These are in the best of the cases addressing the
goals for mobility analyzed in the previous chapter, but often there are other conditions
involved. Nowadays, users get what is developed in the market and it is limited by the key
resources available to the programmers creating them. A user needs to combine several
different of these single services to get the required solution. For instance, a user has an
appointment at a crowded place in a city. As he wants to take his own car, he uses many
different solutions for finding the optimal route, checking whether there is a traffic jam and
where he could park his car. Although he is only interested in arriving at his appointment in
time, he has to use many different single services.
The customers often have an “Expectation of free service” and want the services as cheap as
possible. After using the service, there are only two channels to provide feedback. One is by
how satisfied the customer is, rating the whole App with stars or entering in to a free text space
what they think of the App. The first one is narrow and unprecise, based only in one categorical
values for a complexity of features, the other is too open where the users input whatever they
would like to address. With the architecture platform of modular services, the users will have
more opportunities to get more services; furthermore, they will be able to give feedback to any
specific service module.
In the perspective of the data owners, the development of a solution is explained. The data
owners are often governmental authorities or major tech companies as google, INRIX or
TomTom. They collect and store the data by expensive devices. Then the IT developers
inquire them for the data. Since these are often start-ups or small businesses with limited
resources, they can rarely offer big compensations for the data. The process of accepting the
usage of the data can take a long time. Each organization have specific requirements for this.
The data owners stablish the conditions of data usage and often become a partner of the App.
A standardization of this process is needed. Depending on the deal they reach, the data will
Solution: A Platform of Modular Services
86
be accessed only once, in real time or in the future. Either in raw format or aggregated. Either
complete or partial data sets.
Due to the current volatility of the conditions in the perspective of the IT developer, the user
and the data owner, the ease of the development of mobility solutions becomes a clear
necessity. To design an open platform for modular services would take the advantage of the
private sector developing Apps and the governmental sector providing the data. This platform
will offer modular services that can be integrated in Apps via open and standardized interfaces,
so called Application Programming Interfaces (API’s). This architecture allows the usage of
existing data in different services which can then be used for the development of innovative
mobility services. The platform is a key contribution and will bring together stakeholders that
offer digital services or plan to offer one.
This platform offers several modular mobility services with different levels of granularity. These
services should access different data sources and refine their information. Additionally, the
services should be hosted in a secure and safe environment. Many of these mobility services
are quite computation intensive, which leads to problems when there are too many service
calls. Finally, each user should be identifiable by the platform. The Open Mobility Services
Platform provides a direct connection between the resources and their demand, through a
legal framework, controlling who is accessing it and standardizing the resources provided.
The resources on the platform are standardized, this way similar datasets from different
sources can be presented and understood in a similar way. Moreover, the source can stablish
the requirements for the usage of the resource and get benefits from it, for instance in legal
terms or prices. For instance, the public transport data can be in different formats from city to
city. The platform will propose the public transport providers to use a standard format i.e. GTFS
(General Transit Feed Specification) or require to the sources to indicate in which format is
their data shown.
At the front end, the platform looks as a website. Users will provide their credentials and have
access to the different resources available in the form of API’s. This way, the API’s and the
data will be in a safe environment. A well-known example of this kind of websites is the
Framework for API’s “Swagger”. The proposed platform will create cooperation between the
public and the private sectors, they can create consortiums or Public Private Partnerships and
define who will operate the platform. Moreover, the cooperation with the governments ease
the compliance of the personal data protection laws (European Comission 2016).
Figure 1 shows the concept for the architecture of an open platform for modular mobility
services. It consists of the following elements:
Solution: A Platform of Modular Services
87
Figure 56: Concept for the architecture of an open platform for modular mobility services.
Data sources
The platform is based on several different data sets. One data set could be floating car data,
which can be generated through on board units within cars or through smartphone Apps of
drivers. This data can be used for approximating the flow of traffic within the city and to detect
traffic congestion. It has been shown that it is sufficient to get data from only 2-3% of all cars
to provide accurate measurements of the velocity of the traffic flow (Herrera, Work et al. 2010).
Another relevant data source is parking lot data. This data could come from parking garages,
which already have a good knowledge about their available parking spaces. Data on on-street
parking spaces is harder to get. It could either come from sensors or from the crowd. For
instance, 8.622 parking spaces have been equipped with sensors in San Francisco (McNeal
2013). Several companies offer sensors for shriveling parking spaces (General Electric 2015,
Siemens Mobility 2015, SmartParking 2015). Other solutions are based on the crowd that
reports free parking spaces with an App (ParkMünchen 2015, Parkonaut 2015).
Additionally, data from public transportation providers and taxi corporations could also bet
relevant. They often already have own solutions that show time tables or the position of the
next available car. This data could also be a basis for the platform.
Layers of modular services
The modular services form the core of the platform. There are actually several layers where
the level of granularity increases from the top to the bottom. The services at the bottom focus
on analyzing and refining the data sources, whereas services on higher levels reside on
services from lower levels. The services on higher levels, integrate the services below, using
its results. The highest level takes theses integration and matches them with the inputs from
the user to deliver end user services.
Solution: A Platform of Modular Services
88
Figure 57: Layer of modular services with example services.
Figure 2 illustrates these different levels and shows some example services. A simple modular
service could show the current traffic and parking situation on the streets in a certain area.
Another modular service could use this service, analyze the delivered information and predict
the parking situation for a specific point of time in the future.
The following services are examples for possible modular services:
Prediction of traffic situation: This service predicts the traffic situation for a certain
point of time in the future. It is based on the traffic situation service and on other data
sources such as weather data. This service processes the data with machine learning
algorithms.
Routing: The routing service calculates the best route between two points. The user
can specify whether the current traffic situation or the predicted traffic situation for a
certain point of time in the future should be considered.
Public transportation information: This service shows current and future timetables of
trains, subways and buses. It also provides information about any failures or
unforeseen situations.
Public transportation navigator: The public transportation navigator service suggests
the best public transportation route between two points for a certain point of time. It is
based on the public transportation information service.
Multimodal navigator: This service offers the optimal route within the city for car
drivers. It considers the traffic situation for selecting the optimal route, but also checks
where it is possible to find a parking space at the destination. Additionally, it checks
whether it is better to park the car near a bus station and to use public transportation.
This service is based on the previously described modular services.
Integration layer
The integration layer creates a secure and safe environment. The modular services can only
be accessed through the integration layer. The integration layer buffers service calls and acts
as a load balancer for the services. The user management and access control also reside in
this layer. As all service calls go through this layer, it can also be used for analysis of the
service calls.
Solution: A Platform of Modular Services
89
Solutions
These are the solutions that users of the platform create. It is possible, that these solutions are
end user solutions or that the services are integrated into services outside of the platform.
One example for a possible solution is a scheduling and routing service for small and medium-
sized businesses that have multiple appointments within one or several cities. By considering
the routes between appointments and the predicted traffic situation at that point in time, the
scheduling of appointments can be optimized.
A solution already on development is the crowdsourcing logistics tool, ExCELL Transport. This
allows customers to request an order to transport packages. Couriers are companies that still
have capacity in their transport operations. These capacity available result in a competitive
disadvantage for small- and medium-size businesses. With ExCELL Transport, the couriers
can see the request as work orders, select them and schedule it in their agendas with the
routing included. ExCELL Transport is currently developed by TUM students under the
supervision of the chair for information systems. The development of this prototype used code
modules and data analysis API’s from the TUMitfahrer, explained in (Schreieck, Safetli et al.
2016).
Figure 59: Web App - Creating a new request
Figure 58: iOS App – Accept request and see routing
Conclusions
90
Conclusions
This work is one of the few found in the literature that make a deep review of the existing
mobility apps, and connects them to the users’ needs. In the last years, the richness of road
traffic data collection sources has grown substantially. Nowadays the data is related to a variety
of topics as such as occupancy, speed and traffic flow, and this variety is to be growing in the
coming years. The combination of traditional on-road sensors with floating car data techniques
can provide high quality traffic data in real-time that can be utilized by all the transportation
actors.
In 2008, Leduc (2008) foresaw the potential of smartphone generated data and predicted its
contribution to traditional ITS, however this has not happened yet. Due to the market size and
popularity of smartphones, this research proves the opposite, that smart infrastructure will
complement smartphone technologies. The main difficulty present and to be found in the future
is the data privacy issues. This is handled by data collection techniques, data randomization
methods or data governance.
Considering the services they provide, the Apps are supporting the cooperation of mobility
services. In the nearest future, public transport, car-share and bike share associations (that
work together) are to be seen more often. Furthermore, inter-modal cooperation organizations
are expected with the bike and car share cooperate together with public transport services.
Some of the Apps started already considering that mobility is larger from going from A to B,
and try to connect activities of the users. This is still a field in exploration with vast opportunities.
The Apps usage also can be used as a catalyst of the development of the transport network.
The more services through Apps are offered, the more developed the network is.
It was found that the idea of the car as an everyday transport mode is questioned towards a
more multimodal behaviour in a week scope, in which people use certain transport modes at
some days, and other modes in other times depending on their activities. Instead of the same
transport mode for all the days or different transport modes for the same trip. Furthermore, few
of the respondents associated walking with the usage of different transport modes. This can
be an explanation to the general overlook of the pedestrian infrastructure, regulations and
general attention.
The fact that none of the services reached a satisfaction level closer to low satisfaction than to
above expectations can be attributed to the feeling of play the “hard to get” of the people,
where they express being less satisfied than they really are. Another explanation can be that
they got used to the services after a long time using them and now they take them for granted
(Figure 49).
The services to be provided can have an infinity of solutions. This variety makes it hard to find
and chose a specific solution. Furthermore, the plurality of conditions in different cities or
countries makes it harder even to define the guidelines for an optimal solution. Therefore, to
provide very flexible platforms comes as a proper solution. It was found that there is also a
Conclusions
91
remarkable difference between what the App provider promises and what is really delivered.
For instance, the Bayern App info promotes they have a full Public Transport journey planner
using the data from smart infrastructure, while in the App it does only calculate a few train lines.
The need of cooperation between the private App developers and the governmental owners
of data becomes evident as well as Programmers with Transport scientists.
This work is subject to the following three limitations. Firstly, it implemented new methodologies
and theories from other fields in to transport science and it might sound uncommon for the
practitioners in this field, however, multi-disciplinarity is very positive.
Secondly, the survey was designed, applied and analyzed by a master’s student with less
experience with supervision from non-specialist in these techniques. Ask for only one open-
text-question to declare needs for more Mobility services instead of one question per service
system category. More research is needed for the prioritize of goals for mobility. In the survey,
during the tests it got discarded due to technical capabilities of the surveying platforms (Google
surveys, Survey Monkey, Lime survey). Due to the deployment of the survey through social
network, the most of the respondents might be in age between 20 and 40, located in Mexico
City or Germany, experiencing specific mobility conditions of their environment, minimizing the
degree of randomness of the sample. Socioeconomic variables were not measured.
Thirdly, the concept of the modular platform is still in the development stage. Its usefulness
and capabilities have been already proved by the ExCELL Transport Application. This is one
of the first attempts for the modularization of the mobility Apps industry.
For further research, a study like this can be applied to each category of service system, to
have an overview at a different level of granularity. A survey should be applied to assess the
quality of each service module and what are these missing. Also, it should be applied in a
specific region and the socioeconomic conditions should be asked to have more control over
the predictions.
One of the functions the ITS have is to regulate and assist authorities in enforcing the
application of the law. This does not happen with mobility apps, where some of them are either
at the limits of the law as the Apps warning users of the position of the hidden speed detectors
or warning the position of the police checks. Apps are on the side of the people in a less
regulated environment which is mostly controlled by private companies without a strong
governmental influence. This situation shows vast opportunities in the fields of governance.
Regulations can be enacted based on the information collected with the Smartphones,
however, this would decrease their popularity. Governments can use the strategy to exchange
rights in to governance of the data collected for the provision of their data. However, an
initiative like this should be carefully investigated.
The gathered results may indicate that the commuting time is shortening globally, after
comparing the Figure 46 with Figure 47 (Travel times globally vs Responses of the survey). It
has two possible explanations: either the world transportation systems are achieving shorter
Conclusions
92
commuting times than those in 1998, globally or the responses were influenced by random
conditions. In both cases, the situation illustrates how useful it is to work with a more advanced
technique to collect these data with better preciseness.
The market for intelligent mobility is growing intensively. The data collected by Smart
Infrastructure is not widely used. The mobility Apps provide high quality and deregulated
variety of services but struggle to get the data they need. The users expect an even larger
variety of services. The smart infrastructure is capable to collect and provide the data the Apps
needed. The Apps developers can provide the quality of services the users want. A Platform
offering modular services will convey the capabilities of both. This will enable the industry to
develop solutions closer to both, an individual and a global optimum.
List of References
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List of Abbreviations
100
List of Abbreviations
AADT Average Annual Daily Traffic
ANPR Automatic Number Plate Recognition
API Application Programming Interface
App Mobile Application
APTS Advanced Public Transportation Systems
ATIS Advanced Traveler Information Systems
ATMS Advanced Traffic Management Systems
CONACyT
Mexican Minister of Science and Technology, “Consejo Nacional de Ciencia y
Tecnología”
COUHES Committee on the Use of Humans as Experimental Subjects
CSS Customer Satisfaction Surveys
DAAD
German Agency of Academic Exchange. “Deutsche Akademische Austausch
Dienst“
DPM Direct Performance Measures
EUR € Euro (European currency)
FCD Floating Car Data
GTFS General Transit Feed Specification
HCM Highway Capacity Manual
iOS Mobile operating system created and developed by Apple Inc.
ITS Intelligent Transportation Systems
LOS Level of Services
MIT Massachusetts Institute of Technology
MSS Mystery Shopping Surveys
NGO Non-Governmental Organization
List of Abbreviations
101
P+R Park and Ride
SDG Sustainable Development Goals
SFD Smartphone Floating Data
TUM Technische Universität München
UNAM National Autonomous University of Mexico
US $ Dollar (currency of the United States of America)
V2X Vehicle to infrastructure
VAMOS Verkehrs- Analyse-, -Management- und –Optimierungs-System
VKT Vehicle Kilometers Travelled
VSN Vehicular Sensing Networks
List of Figures
102
List of Figures Figure 1: Concept Matrix augmented with units of analysis (Webster and Watson 2002). .....................4
Figure 2: Scope of this research marked as the arrows ..........................................................................6
Figure 3: Growth of publications in the topics mobility and Smartphone Apps. .....................................7
Figure 4: Topic coverage of the literature review .....................................................................................8
Figure 5: Coverage of relevant topics per author ....................................................................................8
Figure 6: Coverage of topics per author aggregated ...............................................................................9
Figure 7: Boxplot measuring how many articles (16) address the target topics .................................. 14
Figure 8: Applications overview website. (Department of Transportation 2009) .................................. 17
Figure 9: Services provided by ITS. Adapted from: (Department of Transportation 2009) .................. 18
Figure 10: Parking information and routing display on a dynamic signal. Taken from (VAMOS Projekt
2012) ................................................................................................................................... 21
Figure 11: Dynamic signals to influence the traffic flow used in VAMOS Dresden (VAMOS Projekt
2012) ................................................................................................................................... 22
Figure 12: Dynamic signal - Prismenwendern changing from normal conditions to traffic jam. Taken
from (VAMOS Projekt 2012) ................................................................................................ 23
Figure 13: Display of traffic information. Taken from (VAMOS Projekt 2012) ...................................... 23
Figure 14: Type of data provided by different data collection technologies. Adapted from (Schmidt et
al., 2005) and (U.S. Department of Transportation, 2006) and (Peter Martin, 2003).......... 26
Figure 15: Data collected by different sensors Provided by (VAMOS Projekt 2012). .......................... 27
Figure 16: Costs of smart infrastructure sensors. (Department of Transportation 2009) ..................... 28
Figure 17: Interest over time of the concepts of: "Apps", Adapted from Google trends (2015) .......... 29
Figure 18: Gartner Hype cycle of emerging technology. Adapted from (O'Leary 2008). ..................... 29
Figure 19: Interest over time of the concepts of: "App transport”, "App navigation", "App traffic",
"App map". Adapted from Google trends (2015) ............................................................... 30
Figure 20: Categories of digital mobility services (Source: own analysis) ............................................ 33
Figure 21: Services modules in Digital mobility services systems ....................................................... 35
Figure 22: Data source popularity of usage .......................................................................................... 39
Figure 23: Data sources feeding digital mobility service systems ........................................................ 39
Figure 24: Data collection techniques with smartphones. (Catapult Transport Systems 2015) .......... 40
Figure 25: ten of the major sensors an average smartphone may be equipped with. (Matzen 2015) . 41
Figure 26: Goals mentioned by relevant sources ................................................................................. 42
Figure 27: Three -column strategy of Mobility / Technische Hochschule Ingolstadt (Wagner 2016) .. 44
Figure 28: Service quality loop of the European Committee for Standarization (2002) ....................... 46
Figure 29: Mentions of goals aggregated ............................................................................................. 49
Figure 30 Maslow’s hierarchical structure of customer needs for urban mobility ............................... 50
Figure 31: Organizational goals matching with the Apps roles ............................................................ 51
Figure 32: Box Plots explanation, adapted from Intechopen (2016) .................................................... 53
Figure 33: Statistical accuracy of a survey. Population size vs Sample size (as percentage of
population) Adapted from GreatBrook (2016) .................................................................... 54
Figure 34: Sample size calculation - z-scores ...................................................................................... 55
Figure 35: Type of model to be used according to the kind of data. Based on (Buechler 2007) and
(Bruin 2006). ........................................................................................................................ 56
Figure 36: Services needed for four stages of the trip ......................................................................... 59
List of Figures
103
Figure 37: Variables and values explanation......................................................................................... 61
Figure 38: Internet users per continent (Internet Live Stats 2016). ....................................................... 64
Figure 39: Estimation of population size ............................................................................................... 64
Figure 40: Progress of survey's collection from April 15th to May 15th 2016. ....................................... 65
Figure 41: Gender distribution of respondents ..................................................................................... 66
Figure 42: Smartphone savvy level reported ........................................................................................ 66
Figure 43: Boxplot of the reported commuting time ............................................................................. 66
Figure 44: Age distribution of the sample ............................................................................................. 66
Figure 45: Distribution of internet users worldwide as of November 2014, by age group (Statista 2014)
............................................................................................................................................ 66
Figure 46: Average daily travel time as a function of GDP per capita (Schäfer 1998) ......................... 67
Figure 47: Frequency of usage of transport mode. .............................................................................. 67
Figure 48: Frequency of usage of Smartphone based mobility service ............................................... 69
Figure 49: Satisfaction with smartphone based mobility services ....................................................... 70
Figure 50: Geographic Distribution of the responses ........................................................................... 71
Figure 51: Modular services as responses of declared preference. ..................................................... 75
Figure 52:Goals and Needs Addressed by Declared Responses from Users. ..................................... 77
Figure 53: Percentage of adults who reported smartphone ownership, and its change between 2013
and 2015 in selected countries (PEW Research Center 2015, World Economic Forum
2016). .................................................................................................................................. 78
Figure 54: Categories of services provided by ITS and Apps. Own elaboration with information from
Busch (2014) ....................................................................................................................... 79
Figure 55: Schematic comparison of digital mobility solution planned vs. developed. ....................... 83
Figure 56: Concept for the architecture of an open platform for modular mobility services. .............. 86
Figure 57: Layer of modular services with example services. .............................................................. 87
Figure 58: iOS App – Accept request and see routing ......................................................................... 88
Figure 59: Web App - Creating a new request ..................................................................................... 88
Appendix A: App Service Analysis
104
Appendix A: App Service Analysis
Nam
e
Serv
ice t
yp
e
Mark
et
sid
es
Ro
ute
pla
nn
ing
Real ti
me
navig
ati
on
Dyn
am
ic L
ocati
on
sh
ari
ng
locati
on
sh
ari
ng
Map
vie
w
Po
ints
of
Inte
rest
(PO
I)
Park
ing
info
rmati
on
Tra
ffic
info
rmati
on
Infr
astr
uctu
re
info
rmati
on
Matc
hin
g u
ser
to
user
Waze Trip planner 0
Moovit Trip planner 0
Google Maps Trip planner 0
Moovel Trip planner 0
Quixxit Trip planner 0
Drivy Car-sharing 2
BMW-Drive Now Car-sharing 1
Car2go Car-sharing 1
Bla bla car Ride-sharing 2
Lyft Ride-sharing 2
My Taxi Ride-sharing 2
Uber Ride-sharing 2
Gett Ride-sharing 2
Blacklane Ride-sharing 2 -
Flywheel Ride-sharing 2 -
Instantcab Ride-sharing 2
BMW – MyCityWay Others 2
Chargepoint Charging stations 1
BMW – ChargeNOW Charging stations 1
ParkN0w Parking service 2
Ampido Parking service 2
ParkPocket Parking service 2
Parknav Parking service 0
Zendrive Traveling analytics 2
Flinc Ride-sharing 1
MeinFernbus/Flixbus Trip planner 1
FleetBoard Traveling analytics 1
Allryder Trip planner 0
Öffi Trip planner 0
Blitzer.de Location-based information 0
HERE Maps Navigation 0
BVG Fahrinfo Trip planner 1
TomTom Blitzer Location-based information 0
Karten und Navigation GPS Navigation 0
HVV Trip planner 1
Blitzer POIbase Location-based information 0
FahrPlaner Trip planner 1
RMV Trip planner 1
Bus & Bahn Trip planner 1
easyGo Trip planner 1
München Navigator Trip planner 1
Stauinformationen Location-based information 0
Navmii Navigation 0
Citymapper Trip planner 0
ADAC Maps Navigation 1
Maps.ME Navigation 0
City Maps 2 Go Navigation 0
Urban Engines Trip planner 1
Car Jump Car-sharing 2
Nunav Navigation 0
nextbike Car-sharing 1
waymate Trip planner 1
Blitzer Radar Location-based information 0
Bayern Info Trip planner 0
Wunder Ride-sharing 2
General information Service components
Appendix A: App Service Analysis
105
Go
og
le
Oth
er
pri
vate
pro
vid
er
Pu
bli
c
tran
sp
ort
pro
vid
er
Pu
bli
c
ad
min
istr
ati
on
Sm
art
ph
on
e
sen
so
rs
Cro
wd
so
urc
ed
Wazehttps://www.waze.com/de/
https://www.waze.com/es/about/dev
Moovit http://moovitapp.com/#
http://blog-espion.fr/exemple-utilisation-open-data-moovit/
Google Mapshttps://www.google.de/maps?source=tldsi&hl=de
http://readwrite.com/2009/08/25/google_maps_gets_smarter_crowdsources_traffic_data
Moovelhttps://www.moovel.com/
https://www.moovel.com/en/ES
Quixxit https://www.qixxit.de/
Drivy https://www.drivy.de/
BMW-Drive Now https://de.drive-now.com/en/ http://www.bmwcarsharing.com/
Car2go https://www.car2go.com/
Bla bla car https://www.blablacar.es/
Lyft https://www.lyft.com/ http://www.lyftvsuber.com/
My Taxi https://us.mytaxi.com/index.html
Uber https://www.uber.com/
Gett http://gett.com
Blacklane - - - https://www.blacklane.com/ https://www.crunchbase.com/organization/blacklane
Flywheel - http://flywheel.com/ http://fortune.com/2015/04/30/flywheel-taxis-payment/
Instantcab https://www.instantcab.com/
BMW – MyCityWay https://www.cargomatic.com/, https://www.youtube.com/watch?v=vWV4MSQFulY
Chargepointhttp://www.chargepoint.com/mobile/ and
https://play.google.com/store/apps/details?id=com.coulombtech
BMW – ChargeNOW https://chargenow.com/
ParkN0w https://www.park-0w.com/
Ampido https://www.ampido.com/
ParkPocket http://parkpocket.com/
Parknav https://play.google.com/store/apps/details?id=com.faspark.android&hl=de
Zendrive https://www.zendrive.com/how-it-works/#none
Flinc https://flinc.org/
MeinFernbus/Flixbus http://parkpocket.com/
FleetBoard https://play.google.com/store/apps/details?id=com.faspark.android&hl=de
Allryder http://www.allryder.com/
Öffi https://play.google.com/store/apps/details?id=de.schildbach.oeffi, https://oeffi.schildbach.de/,
Blitzer.de https://play.google.com/store/apps/details?id=de.blitzer.plus
HERE Maps https://play.google.com/store/apps/details?id=com.here.app.maps
BVG Fahrinfo https://play.google.com/store/apps/details?id=de.eos.uptrade.android.fahrinfo.berlin
TomTom Blitzer https://www.youtube.com/watch?v=Ui8S4Hu0TLY,
https://play.google.com/store/apps/details?id=com.tomtom.speedcams.android.map
Karten und Navigation GPS https://www.youtube.com/watch?v=uG9eaeCWGbQ
HVV https://play.google.com/store/apps/details?id=de.eos.uptrade.android.fahrinfo.hamburg
Blitzer POIbase https://play.google.com/store/apps/details?id=de.navigating.poibase
FahrPlaner https://play.google.com/store/apps/details?id=de.hafas.android.vbn
RMV https://play.google.com/store/apps/details?id=com.cubic.cumo.android.rmv
Bus & Bahn https://play.google.com/store/apps/details?id=de.hafas.android.vbb
easyGo https://play.google.com/store/apps/details?id=de.easygo
München Navigator https://play.google.com/store/apps/details?id=de.hafas.android.sbm
Stauinformationen https://play.google.com/store/apps/details?id=de.knk.stauinformation
Navmii https://play.google.com/store/apps/details?id=com.navfree.android.OSM.ALL
Citymapper https://play.google.com/store/apps/details?id=com.citymapper.app.release
ADAC Maps https://play.google.com/store/apps/details?id=com.ptvag.android.adacmapformembers
Maps.ME https://play.google.com/store/apps/details?id=com.mapswithme.maps.pro
City Maps 2 Go https://play.google.com/store/apps/details?id=com.ulmon.android.citymaps2gofull&hl=de
Urban Engines https://www.youtube.com/watch?v=HFXjK67L058
Car Jumphttps://play.google.com/store/apps/details?id=com.ghm.carjump&hl=de
http://carjump.me/de/DE/home?orig=www.carjump.de/
Nunav https://play.google.com/store/apps/details?id=com.nunav.play&hl=de
nextbike https://play.google.com/store/apps/details?id=de.nextbike&hl=de
waymate https://www.waymate.de/
Blitzer Radar https://play.google.com/store/apps/details?id=com.lelic.speedcam&hl=de
Bayern Info http://www.bayerninfo.de/bi-app
Wunder https://play.google.com/store/apps/details?id=org.wundercar.android&hl=de
Data source
ReferencesName
Appendix B: Survey Templates
106
Appendix B: Survey Templates
English version
Mobility Apps
The survey identifies the users' preferences of mobility services through Smartphone
applications. It is a part of the thesis of Gabriel Hernandez, mandatory to get the Masters
degree in Transportation Systems by the Technische Universität München.
Your participation is voluntary, anonymous and can be finished at any time without
consequences. After being analyzed and presented, the answers´ database will be destroyed.
More information (and an awesome dynamic map of the answers):
https://gabrielhernandezvaldivia.wordpress.com/transport/mobility-apps-users-needs-data-
requirements/
Your input is highly valuable to provide better services. Thanks :).
Sincerely ,
Gabriel Hernandez,
In cooperation with:
TUM Mobility Services Lab: mobility-services.in.tum.de
TUM Institute for Intelligent Transportation Systems
https://www.vt.bgu.tum.de/en/research/groups/
1. In which city or village do you live?
2. How long is your daily commuting time in minutes? (one way transport to your daily
activities)
2a. How familiar to Smartphone technologies do you consider yourself?
1 2 3 4 5
Appendix B: Survey Templates
107
1. I dont use 2. Sometimes few Apps
3. Daily few Apps 4. Daily many
Apps 5. Progrmamer / Developer
Programmer.
3. How often do you use the following transport modes?
Never Rarely -
yearly-
Occasionally
-monthly-
Frequently -
weekly-
Very
frequently -
daily-
Walk
Bike
Public
Transport
Car
Taxi
Car-share
Ride-
share
Motorcycle
5. How often do you use smartphone for mobility services?:
Never Rarely -
yearly-
Occasionally
-monthly-
Frequently -
weekly-
Very
frequently -
daily-
Planning
before the trip
( which
stations, line,
transfers,
costs, etc) -
e.g. MVG,
Appendix B: Survey Templates
108
Never Rarely -
yearly-
Occasionally
-monthly-
Frequently -
weekly-
Very
frequently -
daily-
BVG, Google
maps -
Navigation (
real-time
directions) -
e.g. Google
Maps,
Tomtom,
Waze -
Purchase
tickets for
transport -
e.g. MVG,
Deutsche
Bahn, Moovel
-
Charging
stations info (
for gasoline
or electric) -
e.g.
Chargepoint,
Tanktaler,
BMW Charge
now-
Car parking
assistance
(location,
reservation,
prediction,
etc) - e.g.
Parkpocket,
Parknav,
Parknow -
Appendix B: Survey Templates
109
Never Rarely -
yearly-
Occasionally
-monthly-
Frequently -
weekly-
Very
frequently -
daily-
Ride, car-, or
bike-Share -
e.g.
Carpooling,
Blablacar,
Nextbike,
Car2Go,
Ecobici,
Blablacar -.
Information
for transport (
traffic,
infrastructure,
locations, etc)
- e.g. Waze,
Blitzer, Stau
info -
Taxi-hailing -
e.g. Uber,
Lyft, MyTaxi -
6. How these mobility services address your expectations?
I do not
know do not fulfill fulfill a bit fulfilled
above
expectations
Planning
before the trip
(which line,
stations,
transfers,
costs, etc) -
e.g. MVG,
BVG, Google
maps -
Appendix B: Survey Templates
110
I do not
know do not fulfill fulfill a bit fulfilled
above
expectations
Navigation (
real-time
directions) -
e.g. Google
Maps,
tomtom,
Waze -
Buy tickets
for transport -
e.g. MVG,
Deutsche
Bahn, Moovel
-
For charging
stations ( for
gasoline or
electric) - e.g.
Chargepoint,
Tanktaler,
BMW Charge
now-
Car parking
assistance
(location,
reservation,
prediction,
etc) - e.g.
Parkpocket,
Parknav,
Parknow -
Ride-, car-, or
bike-Share -
e.g.
Blablacar,
Carpooling,
Appendix B: Survey Templates
111
I do not
know do not fulfill fulfill a bit fulfilled
above
expectations
Nextbike,
Car2Go,
Ecobici,
Blablacar -.
Enter or get
information
for transport (
locations,
infrastructure,
traffic, etc) -
e.g. Waze,
Blitzer, Stau
info -
Hire a taxi -
e.g. Uber,
Lyft, myTaxi -
7. Is there a mobility service through an App you would like to have and have not found
yet?
9. In which age group you are?
o Younger than 18
o 19 - 24
o 25-30
o 31 - 40
o 41- 60
o 61 or more
10. Which is your gender?
Appendix B: Survey Templates
112
o Female
o Male
o Other:
More info:
https://gabrielhernandezvaldivia.wordpress.com/transport/mobility-apps-users-needs-data-
requirements/
Version in Spanish: http://bit.do/gabrielhv-thesis-2
Thank you! // Muchas gracias!!
Gabriel Hernandez Valdivia
gabrielghv1@gmail.com, gabriel.hernandez@tum.de
In cooperation with:
TUM Mobility Services Lab: mobility-services.in.tum.de
TUM Institute for Intelligent Transportation Systems
https://www.vt.bgu.tum.de/en/research/groups/
Error! No text of specified style in document.
Appendix C: Survey Website
Appendix C: Survey Website
114
Appendix C: Survey Website
115
Appendix D: Programming Code in R for Linear Regressions
116
Appendix D: Programming Code in R for Linear Regressions
#Clean/reset memories
rm(list=ls())
#set working directory
getwd()
setwd("C:/users/Gabriel/Google Drive/1-Transportation Systems 2013
GHV/SUBJECTS/Thesis GDrive/GaboThesis")
#set the data
data<-read.table("C:/users/Gabriel/Google Drive/1-Transportation Systems 2013
GHV/SUBJECTS/Thesis GDrive/GaboThesis/data/input/data_Merge_1.csv", sep=",", fill =
TRUE,header = TRUE )
#data <- read.table("input/test.csv", sep="\t") must be CSV!!!! carefful also sep="," means
commas!
#view Data:
#run linear regression
#Dependient Variable: 6b_f_nav = 6b_Frequency_Navigation
#Indep Variables:
##Then LR is saved in variable reg, for instance:
reg=lm(X6a_plan~Smartp+X3c_pubtra+X5a_plan,data=data)
reg_1a=lm(X5a_plan~Smartp+X2_comm+age+X3a_walk+X3b_bike+X3d_car+X3c_pubtra+
X3e_cshare+X3f_taxi+X3g_rsha+X3h_mbike,data=data)
summary(reg_1a)
#See Results:
summary(reg)
Appendix E: Results of Linear Regressions
117
Appendix E: Results of Linear Regressions
Reg_1: To check which people group is more likely to use each service. The people groups
were defined by, smartphone usage, commuting time and Transport mode usage.
a. Service: Journey planner: Reg_1a
Call: lm(formula = X5a_plan ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -3.7468 -0.5490 0.1758 0.7813 2.1346 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1790773 0.3370600 -0.531 0.596 Smartp 0.5655547 0.0641892 8.811 < 2e-16 *** X2_comm 0.0009234 0.0021620 0.427 0.670 X3a_walk 0.0366868 0.0578974 0.634 0.527 X3b_bike 0.0724870 0.0411305 1.762 0.079 . X3d_car 0.0100924 0.0462439 0.218 0.827 X3c_pubtra 0.2189751 0.0541215 4.046 6.52e-05 *** X3e_cshare -0.0550128 0.0806510 -0.682 0.496 X3f_taxi -0.0155837 0.0590562 -0.264 0.792 X3g_rsha 0.0215203 0.0751882 0.286 0.775 X3h_mbike -0.1190209 0.0794651 -1.498 0.135 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.083 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.2922,Adjusted R-squared: 0.2704 F-statistic: 13.38 on 10 and 324 DF, p-value: < 2.2e-16
b. Service: Navigation: Reg_1b
Call: lm(formula = X5b_nav ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -2.9767 -0.4947 0.1250 0.6744 1.9151 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.162e-01 3.154e-01 0.368 0.71285 Smartp 5.080e-01 6.006e-02 8.458 9.44e-16 *** X2_comm -2.132e-05 2.023e-03 -0.011 0.99160 X3a_walk -1.740e-02 5.418e-02 -0.321 0.74831 X3b_bike 7.306e-02 3.849e-02 1.898 0.05856 . X3d_car 1.322e-01 4.327e-02 3.055 0.00244 ** X3c_pubtra 1.056e-01 5.064e-02 2.086 0.03779 *
Appendix E: Results of Linear Regressions
118
X3e_cshare -1.677e-02 7.547e-02 -0.222 0.82432 X3f_taxi 3.623e-02 5.526e-02 0.656 0.51248 X3g_rsha 4.236e-02 7.036e-02 0.602 0.54751 X3h_mbike -7.730e-02 7.436e-02 -1.040 0.29929 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.014 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.2447,Adjusted R-squared: 0.2214 F-statistic: 10.5 on 10 and 324 DF, p-value: 2.14e-15
c. Service: Purchase of tickets: Reg_1c
Call: lm(formula = X5c_purch ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -2.0259 -0.9409 -0.2084 0.8118 3.1020 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0359070 0.3574086 -0.100 0.920037 Smartp 0.2364565 0.0680644 3.474 0.000582 *** X2_comm 0.0005574 0.0022925 0.243 0.808052 X3a_walk -0.0033640 0.0613927 -0.055 0.956336 X3b_bike 0.0681271 0.0436136 1.562 0.119250 X3d_car -0.0659370 0.0490357 -1.345 0.179671 X3c_pubtra 0.0374927 0.0573889 0.653 0.514020 X3e_cshare -0.0442999 0.0855200 -0.518 0.604807 X3f_taxi 0.2139977 0.0626215 3.417 0.000713 *** X3g_rsha 0.0640635 0.0797273 0.804 0.422256 X3h_mbike 0.0252852 0.0842625 0.300 0.764311 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.149 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.1133,Adjusted R-squared: 0.08592 F-statistic: 4.139 on 10 and 324 DF, p-value: 2.134e-05
d. Service: Charging stations: Reg_1d
Call: lm(formula = X5d_charge ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -0.9424 -0.3941 -0.2498 -0.0542 3.3866 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1332806 0.2400590 -0.555 0.5791 Smartp 0.0943408 0.0457165 2.064 0.0399 * X2_comm 0.0001021 0.0015398 0.066 0.9472
Appendix E: Results of Linear Regressions
119
X3a_walk -0.0098881 0.0412353 -0.240 0.8106 X3b_bike -0.0120732 0.0292937 -0.412 0.6805 X3d_car 0.0413100 0.0329356 1.254 0.2106 X3c_pubtra -0.0250051 0.0385461 -0.649 0.5170 X3e_cshare 0.0194941 0.0574408 0.339 0.7345 X3f_taxi 0.1229899 0.0420607 2.924 0.0037 ** X3g_rsha 0.0543880 0.0535501 1.016 0.3106 X3h_mbike 0.0684509 0.0565962 1.209 0.2274 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7714 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.07839,Adjusted R-squared: 0.04995 F-statistic: 2.756 on 10 and 324 DF, p-value: 0.002828
e. Service: Parking assistance: Reg_1e
Call: lm(formula = X5e_park ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -0.8217 -0.2864 -0.1624 -0.0100 3.6783 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.228532 0.204550 1.117 0.264718 Smartp 0.015935 0.038954 0.409 0.682757 X2_comm -0.001333 0.001312 -1.016 0.310264 X3a_walk -0.056868 0.035136 -1.619 0.106527 X3b_bike 0.027755 0.024961 1.112 0.266988 X3d_car 0.036392 0.028064 1.297 0.195639 X3c_pubtra -0.027552 0.032844 -0.839 0.402157 X3e_cshare -0.007237 0.048944 -0.148 0.882543 X3f_taxi 0.068333 0.035839 1.907 0.057450 . X3g_rsha -0.006640 0.045629 -0.146 0.884385 X3h_mbike 0.183943 0.048225 3.814 0.000164 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6573 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.09223,Adjusted R-squared: 0.06422 F-statistic: 3.292 on 10 and 324 DF, p-value: 0.0004431
f. Service: Vehicle/ride share: Reg_1f
Call: lm(formula = X5f_share ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -1.8623 -0.6418 -0.1578 0.4207 3.6685 Coefficients:
Appendix E: Results of Linear Regressions
120
Estimate Std. Error t value Pr(>|t|) (Intercept) -0.343657 0.280175 -1.227 0.220872 Smartp 0.120456 0.053356 2.258 0.024637 * X2_comm 0.001305 0.001797 0.726 0.468395 X3a_walk 0.015400 0.048126 0.320 0.749182 X3b_bike 0.259760 0.034189 7.598 3.26e-13 *** X3d_car -0.091557 0.038439 -2.382 0.017803 * X3c_pubtra -0.023922 0.044988 -0.532 0.595268 X3e_cshare -0.050126 0.067040 -0.748 0.455177 X3f_taxi 0.165403 0.049089 3.369 0.000844 *** X3g_rsha 0.235475 0.062499 3.768 0.000196 *** X3h_mbike -0.058181 0.066054 -0.881 0.379077 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9003 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.2918,Adjusted R-squared: 0.27 F-statistic: 13.35 on 10 and 324 DF, p-value: < 2.2e-16
g. Service: Additional information: Reg_1g
Call: lm(formula = X5g_info ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -2.31655 -1.14058 -0.00349 1.06621 3.08746 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1480305 0.4065348 -0.364 0.716000 Smartp 0.2236460 0.0774199 2.889 0.004128 ** X2_comm 0.0005507 0.0026076 0.211 0.832873 X3a_walk -0.0501491 0.0698312 -0.718 0.473184 X3b_bike 0.1210614 0.0496083 2.440 0.015209 * X3d_car 0.2059022 0.0557757 3.692 0.000261 *** X3c_pubtra 0.0709133 0.0652770 1.086 0.278134 X3e_cshare -0.2002794 0.0972748 -2.059 0.040303 * X3f_taxi 0.1822138 0.0712289 2.558 0.010978 * X3g_rsha -0.0092691 0.0906859 -0.102 0.918653 X3h_mbike -0.0002169 0.0958444 -0.002 0.998196 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.306 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.1306,Adjusted R-squared: 0.1037 F-statistic: 4.866 on 10 and 324 DF, p-value: 1.503e-06
h. Service: Taxi hailing: Reg_1h
Call: lm(formula = X5h_taxi ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data)
Appendix E: Results of Linear Regressions
121
Residuals: Min 1Q Median 3Q Max -3.1907 -0.7286 -0.1161 0.6886 2.9193 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.112890 0.325710 -0.347 0.72912 Smartp 0.282600 0.062028 4.556 7.39e-06 *** X2_comm -0.002810 0.002089 -1.345 0.17956 X3a_walk -0.085913 0.055948 -1.536 0.12561 X3b_bike 0.021284 0.039745 0.536 0.59267 X3d_car -0.005014 0.044687 -0.112 0.91074 X3c_pubtra -0.077675 0.052299 -1.485 0.13846 X3e_cshare 0.134918 0.077935 1.731 0.08438 . X3f_taxi 0.516977 0.057068 9.059 < 2e-16 *** X3g_rsha 0.196765 0.072656 2.708 0.00712 ** X3h_mbike 0.026350 0.076789 0.343 0.73171 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.047 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.3377,Adjusted R-squared: 0.3173 F-statistic: 16.52 on 10 and 324 DF, p-value: < 2.2e-16
Appendix F: Goals addressed by declared service responses
122
7 Appendix F: Goals addressed by declared service responses
Physiological Safety Belonging /
LoveEsteem
Self-
actualization
Availability,
Accesibility,
Optimisation
of Mobility
Economic
development
, safety,
seccurity,
affordability
Social
Developmen
t, Customer
care
human well
beign,
Quality,
Comfort,
Ergonomy,
Innovation,
life-style,
sustainability
Bus information 4
Integrate a delay alarm 1
Integrate possible
delays and warnings1
Multi-modal and multi-
city for long distance
traveling
1
Multimodality 5
Preciseness 4
More cities 8
Integration of energy
consumption and
reliability
1
Frequency based
shortest paths1
Dyn
am
ic
Lo
catio
n
sh
arin
g
Preciseness
1
locatio
n
sh
arin
g
Dynamic locations of
public transits vehicles1
Bus information 2
Parking in critical
areas 2
Multimodality 3
Preciseness for
addresses 1
Proper cycle lane
indicators 1Po
ints
of
Inte
rest
(PO
I) Preciseness for
addresses 1Park
ing
info
rma
tion Parking information
3
Traffic flow in real time 1
Integrate with official
information of traffic 1Matc
hin
g u
ser
to u
ser
Cycle-share and Walk-
share / companions1
show routes of ride
share offers 1
Easier carpooling apps 1
all car sharing services
in one app 1
higher quality 2
Motorcycle share 1
Ch
arg
in
g
sta
tion
s
Gas stations with
additional services
(Restaurants, etc) 2Accidents and traffic
jams 1
Location based
probability to have an
accident 1
Location based
probability of crime 1
An
aly
zed
serv
ice
rela
ted
Response concept
Fre
qu
en
cy
Alre
ad
y e
xis
ts
Ro
ute
pla
nn
ing
Map
vie
wC
ar-/B
ike-, R
ide-
sh
arin
g
Lo
catio
n-b
ased
info
rmatio
n
Tra
ffic
info
rmati
on
Appendix F: Goals addressed by declared service responses
123
Tracker and route
predictor 1
Multimodal navigation 8
Bus information 1
Cycling navigation 3
Pu.T. Navigation 3
Speed
recommendations 1
Tra
velin
g
an
aly
tics
Freight transport
tracking 1
Walking Safe walking app3
Cycling app 3
Cycling app integrating
safety 1
Routes, Workshops,
Shops for cycling 1
Safe
ty
an
d
secu
rity
Integrate safety and
security in the
algorithm 5
Integrate weather
information 2
Connection with
emergency services 1
App versions for
disabled people 1
Multi-Lingual services
with local major
dialects/translations 1
Orientation for Rural
villages 1
Comparison of local
and international bus
and train costs 1
Illustrated train seating-
maps (with advises) -
similar as SeatGuru for
aircaft seating 1
cheapest flight tickets
without indication of
destination (Like in
Ryan air) 1
multi national and multi-
modal planning/
booking/ Paying/
Navigation app 1
To enter and generate
O/D information 2
Low carbon mobility 1
Autonomous car
hailing 2
Cable cars in the alps
Apps with low Battery
consumption 3
Wi-Fi in transport 1
Location more precise
than GPS 1
Inte
gra
tio
nA
ccessib
ilityH
ard
ware
Fu
ture
Pre
sen
tC
yclin
gN
avig
atio
n
Declaration concerning the Master’s Thesis / Bachelor’s Thesis
124
8 Declaration concerning the Master’s Thesis / Bachelor’s
Thesis
I hereby confirm that the presented thesis work has been done independently and
using only the sources and resources as are listed. This thesis has not previously been
submitted elsewhere for purposes of assessment.
Munich, July 10th, 2016
_________________________
Gabriel Hernandez Valdivia
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