btech final year project
TRANSCRIPT
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Final Year Project Report
Trip Generation and Public Transit Choice Modelling
in Indian Metropolitan Context: A Case Study of
Ahmedabad
Submitted in partial fulfillment of the requirement of
Bachelor of Technology
by
Abhishek Lodhi (11BCL027)
Shivam Patel (11BCL074)
Parth Bhatt (11BCL076)
Krushal Soni (10BCL018)
Under the guidance of
Supervisor: Mr. Ashu S. Kedia
Lecturer, Department of Civil Engineering
School of Technology
School of Technology
Pandit Deendayal Petroleum University
Gandhinagar – 382007. Gujarat - INDIA
February – 2015
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INDEX
List of Abbreviation iii
List of Tables iv
List of Figures v
1. General 1
2. Theoretical Background &Literature review 3
2.1 Theory Base: Four Stage Travel Demand Modelling 3
2. 1.1 Trip Generation 3
2.1.2 Trip Distribution 7
2.1.3 Modal Split 8
2.2Data Collection 12
2.2.1 Sampling 12
2.2.2 Survey Techniques 15
2.3 Review of Research Papers 18
3. Identification of research gaps 34
4. Objectives and scope 35
5. Methodology 35
6. Study area 38
6.1 City Background 38
6.2 Population Growth Trends 38
6.3 Vehicular Growth Trends 39
6.4 Study Zone Particulars 41
7. Data Collection & Data Handling 42
8. Accessibility Study 47
References 55
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List of Abbreviation
HIS- Household Interview Survey
FSM – Four Stage Model
TAZ – Traffic Analysis Zone
CBD –Central Business District
SPSS –Statistical Package for Social Sciences
MRS –Marginal Rate of Substitution
RP –Revealed Preference
SP –Stated Preference
CTPS –Central Transport Planning Staff
MNL –Multinomial Logit
BART –Bay Area Rapid Transit
HIG –High Income Group
MIG –Middle Income Group
LIG –Low Income Group
HHS –Household Size
HHI –Household Income
EM –Educating Members
WM –Working Members
TPCD –Trips Per Capita Per Day
AMTS- Ahmedabad Municipal Transport Service
BLRM –Binary Logit Regression Model
HL –Hosmer and Lemeshow
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List of Tables
Table 1: Research Review
Table 2: Percentage decadal variation in population for state & districts: 1901-
2011
Table 3: Modal Share as registered at RTO Ahmedabad (Oct, 2014)
Table 4: Vehicular Growth Rate of Ahmedabad (Till Oct 2014)
Table 5: West Zone Particulars (2011)
Table 6: Representative Samples from Central Zone
Table 7: Representative Samples from West Zone
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List of Figures
Figure 1: Research Methodology
Figure 2: Share of Vehicles Registered at RTO Ahmedabad
Figure 3: Study area
Figure 4: Central Zone of Ahmedabad
Figure 5: West Zone of Ahmedabad
Figure 6: Household Characteristics
Figure 7: Travel Characteristics
Figure 8: Special Trips
Figure 9: Public Transport Parameters
Figure 10: Data Collection
Figure 11: Questionnaire for Data Analysis
Figure 12: Aim of Cycle Feeder System
Figure 13: Parking facility at MYBYK Cycle Feeder System
Figure 14: Stations of Cycle Feeder System
Figure 15: Data Entry of Cycle Feeder System
Figure 16: Questionnaire of Cycle Feeder System
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ABSTRACT
Urban population in India has increased significantly from 62 million in 1951 to 378
million in 2011 and is estimated to grow to around 540 million by the year 2021. The
urban population has gone up from 17% in 1951 to 32% in 2011 and is expected to
increase up to around 37% by the year 2021. Increased urbanization has led to a
significant travel demand within urban areas. Owing to high job potential and high per
capita income in these urban areas, the affinity towards private vehicles is also on the
higher side. Daily 250 to 600 vehicles are getting added to different metropolitan cities,
of which growth in two-wheelers and cars is the dominant factor. Inadequate
development in urban public transportation sector with urbanization has led to an
imbalance in modal split. This has resulted into the problems like traffic congestion, air
pollution and huge urban transport cost. Efficient public transport facilities like BRTS,
Metro rail etc. which can shift private vehicle users to public transport mode are the dire
need of the time to make the urban transportation sustainable.
Implementation of public transport facilities will affect the network conditions which
may influence the decision making regarding their travel modes. Hence, it is must to
analyze the mode choice behaviour of urban travellers pertaining to their work as well
as education trips. The present study is precisely aimed at developing the Trip
Generation and Logit Mode Choice Models for various income groups considering the
West and Central zone of Ahmedabad city as study area. Home Interview Surveys
(HIS) with pre-designed questionnaire are conducted to collect socio-economic and RP
as well as SP data related to travel parameters which are necessary for developing
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theabove mentioned models. Trip Generation models so developed are observed to
predict the trips with lower accuracy. The developed transit choice models are observed
to predict the choice for transit mode satisfactorily as validated by the observed
preferences. Sensitivity analysis is also carried out for waiting time to influence the
transit share in the study area which could guide the transport planners for appropriate
public transport service scheduling and can consequently lead to efficient public
transport system. The present research finds applications in framing suitable transport
policies in fast growing Indian cities.
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CHAPTER 1: INTRODUCTION
Urbanization and urban population growth have firmly gripped India since the last
few decades.The share of urban population in India is growing and the number of
metropolitan cities has also reached to 41 by the end of last census (2011). With
increasing concentration of economic activities to sustain the growth in these
metropolitan areas, the rural-urban migration is getting encouraged in order to
practice better standard of living. This intensifies the travel demand of the people
residing in these areas and calls for appropriate transport infrastructure to alleviate
the situation from problems of traffic congestion, pollution, lack of parking space
and huge urban transport cost. At the moment, many Indian cities are facing
vehicular population explosion. For instance, Ahmedabad, a fast developing Indian
city is having 31.51 lakh vehicles against a population of 65 lakh people (RTO,
Ahmedabad, 2014). More than half the vehicles are two-wheelers (19.74 lakh) as
12% of the vehicles consist of privately-owned four-wheelers.The ever-growing
figures put huge pressure on the infrastructure as the city is not at all ready for
onslaught of vehicles. The population's growth rate has accelerated in the past five
years as it adds up more than 20% vehicles every year. From 1.70 lakh new vehicles
added in 2009-10, the number of new vehicles in 2011-12 was observed to be nearly
2.22 lakh. As far as the city's commuting plan is concerned, an average person rides
less than five kilometers every day. Unlike countries such as Singapore, where there
is mandatory cap on the number of total vehicles owned, India is facing exorbitant
growth in the vehicular population particularly in metro cities where the per capita
income is high owing to better job potential. It is must to strengthen the public
transport system in developing cities to make it competent with the private mode of
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transport, so that the public transport patronage can be boosted. The present study
attempts to analyze the travel pattern of people in urban areas, taking a case of
Ahmedabad city situated in the western part of the country. The impact of socio-
economic characteristics, network characteristics and the land-use parameters on the
travel behavior of people in terms of trip frequency and modal choice is being
studied here.
1.1 Objectives and Scope
To study the transport system status till date.
Comparison of trip generation pattern between residential zones and commercial
zones with the help of developed regression based trip generation models.
To develop the choice behavior models with respect to public transport service
considering all the socio-economic classes of the society.
To carry out sensitivity analysis for accessibility parameters with the help of
developed transit choice models.
1.2 Methodology
In order to achieve the objectives of this study the work is divided into various
phases like.
1st Phase: Review of Trip Generation and Mode Choice Modelling studies.
2nd Phase: Selection of study areas. Here West and Central zone of Ahmedabad city
are taken as study area.
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3rd Phase: Data Collection. Household and Socio-economic data in addition to
travel details of urbanites is being collected here through the means of pre-designed
Questionnaire form.
The questionnaire is divided into three parts. The first part includes the socio
economic information about the respondents. The second covers the travel details of
the people. And the third part focuses on the public transport particulars.
4th Phase: Sample Size Determination. Here, the number of samples to be taken is
estimated which came out to be 600.
5th Phase: Data Analysis and Descriptive Statistics. Here, the data collected has
been described across its length and breadth with the help of softwares such as MS
Excel and SPSS.
6th Phase: Model Building. Here, the regression based trip generation models as
well as Mode Choice Models are built.
7th Phase: Application of developed models have been shown here.
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Figure 1.1: Research Methodology
Research objectives
Problem Identification
Delineation of Study
Area
Literature review Design of
questionnaire
Data collection and
Processing
Home Interview
survey
Data Analysis
Model Building
MS Excel, SPSS
Study Application
Conclusion
West Zone & Central
Zone Ahmedabad
Public transport
policies
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CHAPTER 2: THEORETICAL BACKGROUND AND
LITERATURE REVIEW
2.1 Theory Base: Four Stage Travel Demand Modeling
Sequential Demand Modelling (SQM) is the fundamental tool for estimating the
travel demand and comprises of four stage process. Firstly, in trip generation,
measures of trip frequency are developed providing the propensity to travel. Trips
are represented as trip ends, productions and attractions, which are estimated
separately. Next, in trip distribution, trip productions are distributed to match the trip
attraction distribution and to reflect underlying travel impedance (time and/or cost),
yielding trip tables of person-trip demands. Next, in mode choice, trip tables are
essentially factored to reflect relative proportions of trips by alternative modes.
Finally, in route choice, modal trip tables are assigned to mode-specific network.
2.1.1 Trip Generation
It is the first step in the conventional four-step transportation forecasting process
(followed by trip distribution, mode choice, and route assignment), widely used for
forecasting travel demands. It predicts the number of trips originating in or destined
for a particular Traffic Analysis Zone (TAZ).Typically, trip generation analysis
focuses on residences, and residential trip generation is thought of as a function of
the social and economic attributes of households. At the level of the traffic analysis
zone, residential land uses "produce" or generate trips. TAZs are also destinations of
trips and hence trip attractors. The analysis of attractors focuses on non-residential
land uses. If either end of the trip is home than it is termed as Home Based and if
neither end is home than its Non Home Based.
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Factors Governing Trip Generation
1. Income: A general trend is that higher the income the higher the trip generation
rate.
2. Car Ownership: a car represents easy mobility and hence a car owing car hold
will generate more trips than the non-car owing house hold.
3. Family size: The bigger the family, the more trips there are likely to be
generated.
4. Land use characteristics: different land uses produce different trip rates. For
example, a residential area with a high density of dwellings can produce more
trips than one with a high density of dwellings.
5. Distance of the zone from the town centre: the farther the town centre, the less
number of trips are likely to be.
6. Accessibility to public transport system and its efficiency: an easily accessible
and efficient public transport system generates more trips.
Trip Generation Modeling
The trip – generation models strive to predict the numbers of trips generated by a
zone. These models try to mathematically describe the decision-to-travel phase of
the sequential demand analysis procedure. It may be mentioned here that typically
the term trip-generation is used to mean trip generation – generally trips made from
households- and trip attraction – trips made to a particular urban location or activity.
However, it is felt that analysis of trip attractions should not be within the purview
of trip generation models which attempts to quantify a populations urge or
propensity to travel. Rather, trip attractions are an outcome of the destination-choice
phase of travel behavior.
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Regression Models
In statistics, regression analysis is a statistical process for estimating the
relationships among variables. It includes many techniques for modeling and
analyzing several variables, when the focus is on the relationship between a
dependent and one or more independent variables. More specifically, regression
analysis helps one understand how the typical value of the dependent variable (or
'criterion variable') changes when any one of the independent variables is varied,
while the other independent variables are held fixed. Most commonly, regression
analysis estimates the conditional expectation of the dependent variable given the
independent variables – that is, the average value of the dependent variable when the
independent variables are fixed. Less commonly, the focus is on quantize, or
other location parameter of the conditional distribution of the dependent variable
given the independent variables. In all cases, the estimation target is a function of
the independent variables called the regression function.
Yp= a1X1 + a2X2 +-……..+anXn +U (2.1)
Where, Yp = number of trips for specified purpose p
X1, X2 ….Xn= independent variables relating to, for example, land use socio
economic factors etc.
a1, a2….an = co. of the respective independent variable X1, X2….Xn, obtained
by linear regression.
U = disturbance term, which is a constant, and representing that problem of the
value of Yp not explained by the independent variables.
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Trip Rate Analysis
Trip rate analysis refers various models those are based on the determination of the
average trip-production or trip-attraction rates associated with the trip generators
within the region.
Cross Classification (Categorical Analysis)
The cross-classification model, sometimes referred to as category-analysis model, is
based on the assumption that the number of trips generated by similar households or
households belonging to the same category is the same. According to this model, if
in a Zone there are n households in a category and if T is the average rate of trip
generation per household in category k then the relation of trips generated (or
produced) by Zone I is given by:
𝑇𝑖 = ∑ 𝑛𝑘𝑖 𝑔𝑘∀𝑘 (2.2)
The model predicts the trips produced by a zone by simply aggregating the total trips
produced by all the households in that zone. However, two basic questions need to
be answered here:
2. How do we define similar households or alternatively how do we define
categories of households.
3. How do we determine the rate of trip generation for a given category of
households.
The model predicts the trips produced by a zone by simply aggregating the total trips
produced by all the households in that zone. However, two basic questions need to
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be answered here: (i) how do we define similarhouseholds, or alternatively how do
we define categories of households, and (ii) how do we determine the rate of trip-
generation for a given category of households. The answer to both these questions
is: through empirical observations and analysis. What is done is that, first, data on
demographic characteristics and trip -making behaviour of a large number of
households are collected. This data is then analyzed to see what characteristics of the
households are important in defining a homogeneous group - the households which
produce approximately the same number of trips.Based on the above analysis, tables
are made which define each category of households by listing its properties in terms
of different demographic variables. For example, a particular category of households
may be defined as households with 3 to 4 members in the age group 6 to 60, with
income in the range of Rs. 30,000 to Rs. 40,000 per month, and one automobile.
Finally, for each category of household the average number of trips generated are
listed. The listing of the definition of categories and the associated trip -generation
rates are generally referred to as trip tables.
2.1.2 Trip Distribution
Trip distribution (or destination choice or zonalinterchangeanalysis), is the second
component (after trip generation, but before mode choice and route assignment) in
the traditional four-step transportation forecasting model. This step matches ‘trip
makers’ origins and destinations to develop a “trip table”, a matrix that displays the
number of trips going from each origin to each destination. Historically, this
component has been the least developed component of the transportation planning
model.
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2.1.3 Modal Split
Modal split is the process of separating person-trips by the mode of travel. It is
usually expressed as a fraction, ratio or percentage of the total number of trips. In
general, the model split refers to the trips made by private cars as opposed to public
transport.
Factors Affecting Modal Split
Characteristics of the trip
a.) Trip purpose: the choice of mode is guided to a certain extent by the trip
purpose. To give an example, home based school trips have a high rate of usage of
public transport.
b.) Trip length: the length can govern an individual's choice of a particular mode. A
measure of trip length is also possible by the travel time and the cost of travelling.
Household characteristic
a) Income: The income of a person is a direct determinant of the expenses he is
prepared to incur the journey. Higher income groups are able to purchase and
maintain private cars, and thus private car trips are more frequent as the income
increases.
b) Car ownership: Car ownership is determined by the income and for this reason
both income and car ownership are inter-related their effect on modal choice.
c) Family Size and Composition: The number of person in the family, the number
of school going children, the number of wage earners, the number of unemployed,
the age-sex structure of the family and some other factors connected with the socio-
economic status of the family profoundly influence the modal choice.
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Zonal characteristic
a. Residential density
b. Distance from work
c. CBD
Network characteristic
a) Accessibility ratio: Accessibility ratio is a measure of the relative Accessibility
of that zone to all other zones by means of mass transit network and highway
network.
b) Travel time ratio:the ratio of travel time by public transport and travel time by
private car gives a measure of the attractiveness.
c) Travel cost ratio: the ratio is of cost of travel by public transport and cost of
travel by car is one of the most important factors influencing modal choice. The
importance of travel cost is related to the economic status.
Modal split in the Transport planning Process
Pre-distribution: Here, modal split is considered prior to trip distribution stages.
This procedure is also known as trip end modal split procedure.
Post-distribution:Here, modal split is considered after trip distribution stage.
This procedure is also known as trip interchange modal split procedure.
Recent Development in Modal Split Analysis
Probit Analysis
This Analysis is based on the principle that if member of population are subjected to
a stimulus that can range over an infinite scale, the frequency of response to stimulus
will be normally distributed.Probit analysis is a type of regression used to analyze
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binomial response variables. It transforms the sigmoid dose-response curve to a
straight line that can then be analyzed by regression either through least squares or
maximum likelihood. Probit analysis can be conducted by one of three techniques:
Using tables to estimate the probits and fitting the relationship by eye, hand
calculating the probits, regression coefficient, and confidence intervals, or having a
statistical package such as SPSScan serve the purpose.
Logit Analysis
This Analysis assumes that the probability of the occurrence of an event varies with
respect to function f(x) as a sigmoid curved called the logistic curve.
In statistics, Logistic Regression is a type of probabilistic statistical classification
model. It is also used to predict a binary response from a binary predictor, used for
predicting the outcome of a categorical dependent variable (i.e., a class label) based
on one or more predictor variables (features). That is, it is used in estimating the
parameters of a qualitative response model. The probabilities describing the possible
outcomes of a single trial are modeled, as a function of the explanatory (predictor)
variables, using a logistic function. Frequently (and subsequently in this article)
"logistic regression" is used to refer specifically to the problem in which the
dependent variable is binary i.e, the number of available categories is two, while
problems with more than two categories are referred to as multinomial logistic
regression or, if the multiple categories are ordered, as regression. Logistic
regression measures the relationship between a categorical dependent variable and
one or more independent variables, which are usually (but not
necessarily) continuous, by using probability scores as the predicted values of the
dependent variable. As such it treats the same set of problems asProbit
regression using similar techniques.
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2.2 Data Collection
2.2.1 Sampling
The selection of a proper sample is an obvious prerequisite to a sample survey. A
Sample is defined to be a collection of units which is some part of a larger
Population and which is specially selected to represent the whole population.The
selection of a proper sample is an obvious prerequisite to a sample survey. The
different types of sampling available are listed below and the adopted one is
described in detail.
Simple Random Sampling
Simple random sampling is the simplest of all random sampling methods and is the
basis of all other random sampling techniques. In this method, each unit in the
population is assigned an identification number and then these numbers are sampled
at random to obtain the sample.
Stratified Random Sampling
Stratified sampling is useful in general, to ensure that the correct proportions of each
stratum are obtained in the sample, it becomes doubly important when there are
some relatively small sub-groups within the population. With simple random
sampling, it would be possible to completely miss out on sampling members of
small sub-groups. Stratified random sampling at least ensures that some members of
these rare population sub-groups are sampled.
Multi-Stage Sampling
Cluster Sampling
Systematic Sampling
Non-Random Sampling Methods
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2.2.2 Survey Techniques
Telephonic Surveys
On-line Surveys
Home Interview Surveys (HIS)
Home Interview Survey
Home interview survey is one of the most reliable type of surveys for collection of
origin and destination data. The survey is essentially intended to yield data on travel
pattern of the residents of the household and the general characteristics of the
household influencing trip making. The information on travel pattern includes
number of trips made, their origin and destination, purpose of trip, travel mode, time
of departure from origin and time of arrival at destination and so on. The
information on household characteristics includes type of dwelling unit, number of
residents, sex, race, vehicle ownership, family income and so on. Based on these
data it is possible to relate the amount of travel to household and zonal
characteristics and develop equations for trip generation rates.
Techniques used in HIS:
Full-interview technique: It involves interviewing as many members of the
household as possible and directly recording all the information.
Home questionnaire technique: The interviewer collects only details of the
household residents to compete in regard to travel information. The completed
forms are collected by the interviewer after a day or two.
The data collected can be of two types as described below:
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Revealed preference Data
Revealed preference theory is a method of analyzing choices made by individuals,
mostly used for comparing the influence of policies on consumer behavior. These
models assume that the preferences of consumers can be revealed by their
purchasing habits. Revealed preference theory came about because existing theories
of consumer demand were based on a diminishing marginal rate of substitution
(MRS). This diminishing MRS relied on the assumption that consumers make
consumption decisions to maximize their utility. While utility maximization was not
a controversial assumption, the underlying utility functions could not be measured
with great certainty. Revealed preference theory was a means to reconcile demand
theory by defining utility functions by observing behavior.
Stated Preference Data
With RP data we are at the whim of the interrelated nature of the real world. With
SP data, since we are directly asking humans about their preferences for products
and services, we are also at liberty to construct the very products as we wish them to
evaluate. Because individuals do not have to back up their choices with real
commitments when they answer the survey, to some extent, they would behave
inconsistently when the situation really happens, a common problem with all SP
methods.SP models may therefore be accurately scaled with the introduction of
Scale Parameters from real world observations, yielding fairly accurate predictive
models.
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2.3 Review of Research Papers:
Work Travel Mode Choice and Number of Non-Work Commute Stops.
(1997)
Bhat et.al. developed a joint model of work mode choice and number of stops during
the work commute. This model provides an improved basis to evaluate the effect of
alternative policy actions to alleviate peak-period congestion. The data source used
in this study is a household activity survey of 618 employed adult individuals
conducted by the Central Transportation Planning Staff (CTPS) in the Boston
Metropolitan region. The mode share in the (weighted) sample is as follows:
76.55% solo-auto, 11.31% shared-ride and 12.14% transit. Mode choice modelling
has been done for these three mode. In this paper, they have developed a joint
model of work mode choice and number of non-work activity stops during the work
commute. As the ratio of the number of vehicles to workers in a household
increases, there is less competition for cars among household members and hence a
lower tendency to use the shared-ride mode and an even lower likelihood of using
the transit mode. Socio-economic variables, work duration significantly influence
stop-making propensity during the work commute. In-vehicle and out-of-vehicle
travel times to work negatively influence stop-making propensity, but we did not
find any significant effect of travel cost to work on stop-making propensity.
Smart Feeder/Shuttle Bus Service: Consumer Research and Design (2006)
In order to approach the design of innovative feeder/shuttle system and new
integration and routing concepts for this service Y.B.Yim et.al took a telephonic
survey (400 samples) in Castro Valley, San Francisco Bay Area. 64 percent
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commuted to work, 7 percent to school, and 2 percent both equally. Nearly four fifth
(78%) of the respondents drove to work alone while only 8 percent carpooled and 19
percent took public transit. Approximately 40 percent of the participants expressed a
high likelihood of using the shuttle service. Participants said that the most important
attribute for Shuttle Design was the cost of the shuttle service, overall travel time,
including the waiting time for the shuttle and pick-up location. 43.3 percent of those
surveyed said that they would be more likely to use BART if a shuttle is provided
and 56.8 percent said that the shuttle would not necessarily cause them to take
BART more often. The survey suggested that most people perceive benefits from the
shuttle service. It would be convenient for them and could save travel cost and time
and increase safety and reduce stress.
Behavioral models of work trip mode choice in Shanghai (2006)
Gang Liu have analyzed travelers’ choice behavior by using data from a stated
preference survey on work-trip mode choice in Shanghai. In this paper they have
conducted household survey & face to face interview. Totally 100 respondents were
selected of the central Shanghai area by using stated preference and revealed
preference survey. With the help of model build by survey data they had found the
probability of traveller of choosing any mode. This model showed that the income is
indeed an important variable in work-trip mode choice decisions. Travelers with
higher Income care more about time loss and less about money cost than those with
lower income. Bicycle seems to be an inferior mode for all the income levels while
Bus and Subway are also inferior for higher income levels. Taxi is a normal mode
for all the income levels and a luxury mode in particular for the middle income
level. The results also show that In-vehicle time of choosing Bus and money cost of
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choosing Taxi are more important attributes for those with higher income levels.
While for those with lower income, money cost and In-vehicle time of choosing Bus
seem to be more important.
Bicycle – as a feeder mode for bus service, Delhi (2006)
Mukti Advani et.al examined the feasibility of the bicycle as an access mode(feeder
mode) to public transit. They have conducted a survey of bus commuters on
different bus routes of existing bus service to understand the various factors (e.g.
Access, egress, cost, age, income etc.) affecting the trip profile of a person. They
have collected 3632 samples, Out of all 711 persons (20%) own bicycle, 652 (18%
of total) have 1 bicycle and 58 (2% of total) have 2 bicycles at home and only 6
(0.15%) persons out of total 3632 are using bicycle for access trip to bus. The people
owning bicycle are not using it for their access trip. The reason behind this can be,
absence of parking facility at bus stops, short distance from their origin to bus stop,
lack of safe cycling facility along the road. As results of the survey shows, there is
no facility provided to encourage the use of bicycles to make access trip to bus
transit service. Few commuters are using bicycle for access trip and because they do
not have any other option. Bicycle owners from lower income group can easily shift
to cycle mode if they are provided with basic bicycle friendly facilities. Results
show that if bicycles are provided as feeder system at or near bus stops, commuters
can reach easily at the bus stop using their bicycle. People who are presently using
their own vehicle for the complete trip may shift to public transit if some basic
facilities are provided to them.
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Analyzing evolution of urban spatial structure: a case study of Ahmedabad,
India (2009)
Adhvaryu et.al. have analyzed trends in evolution of the urban spatial structure of
the city of Ahmedabad, with two key objectives in mind 1) to generate a quantitative
understanding of the evolution of the spatial structure and 2) use such a quantitative
understanding to inform the formulation of alternative planning policies for the
future. There are three measures available for analyzing the urban spatial structure
they are density gradient, dispersion index, and the concentration deconcentration
measure. All three measures discussed in this paper show that Ahmedabad is
gradually dispersing. Reduction in the density gradients indicate that population is
moving from the centre to peripheral areas. This paper shows that in a situation
where only time-series population data are available at a reasonable spatial
disaggregation level, very simple but useful analysis of evolution of the urban spatial
structure of a city can be carried out.
Estimating modal shift of car travelers to bus on introduction of bus
priority system (2009)
Vedagiri et.al studied the shifting behavior of people from private vehicles pubic
transport mode. Heterogeneous traffic leads to traffic congestion. It is difficult for
bulky bus to through traffic on road. More the utility, more the usage will be. Shift is
maximum in case of trips mode for work purpose, On providing exclusive bus lane,
travel time in bus gets reduced. Simulation model of heterogeneous traffic, Binary
logit model of mode choice, Discrete choice models, Simulation model, Aggregate
model this Techniques are used. Details of the Tools/Surveys & No. of samples is
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Binomial logit models based on SP data using proposed Jalkarta bus-way system, SP
surveys, Modal shift probability curve.
Modeling mode choice in short trips-shifting from car to bicycle (2011)
Halldorsdottir et.al .investigated the mode choice behaviour of Danish population
from the Greater Copenhagen Area when travelling short trips using mixed logit
model. Survey data identify the travel behaviour of the Danish population through
interviews collecting travel diaries and socio-economic variables of a representative
sample of the population. The investigated sample includes 7,966 individuals and
11,072 trip chains. The share of respondents using each mode is with 11% walking,
28% cycling, 47% driving, 6% being driven and 8% taking public transport. The
model considers five alternatives (i.e., car driver, car passenger, public transport,
walk and obviously bike).Travel time is important for cyclists as other transport
modes, The men are more likely to drive car than women, while women were more
likely to be car passengers, mode choice behavior was also strongly linked to
household characteristics, Presence of slopes has in average a significant negative
impact on cycling. Income has no significant effect on bicycle share in mode choice,
Individuals with children were more likely to be a car driver, when compared to
other transport modes, This study helps uncovering factors that are able to make
cycling more attractive, for example improving accessibility, enhancing
infrastructures, addressing specific population groups for specific trip purposes.
21
Factors affecting mode choice of work trips in developing cities- Gaza as a
case study (2013)
Essam et.al developed mode choice model for work trips in Gaza city and therefore
investigating the factors that affect the employed people’s choice for transport
modes. The model was developed using about two thirds of 552 questionnaires
distributed for this purpose. The questionnaire is divided into three parts. The first
part includes the socio-economic information (gender, age, job, income, family size,
ownership of private car, own- ership of motorcycle, ownership of bicycle… etc).
The second part focuses on the factors that affect the mode choice (travel cost, travel
time, waiting time, weather conditions, privacy, comfort, health status, and trip
length). The third part focuses on the trip characteristics. Multinominallogit model is
used in this paper. The total travel time, total travel cost divided by personal income,
ownership of transport means, age, distance and average family monthly income are
the factors that affect the mode choice of employed people in Gaza city. The
developed model can be used in travel demand analysis and in developing transport
policies for Gaza city .
Mode Choice Modelling For Work Trips in Calicut City (2013)
Rajalakshmi et.al has investigated mode choice behaviour of employers in Calicut
city. A multinomial logit model (MNL) with statistical data processing software
SPSS was used for explaining travel patterns and mode choice of employees
residing in Calicut city. This study focused on home-to-work trip and work to home
trips. It enables to understand travel demand behavior to work trips and constraints
that travelers face. The study identified the factors effecting mode choice for work
trips and also make model for work trips in the city of Calicut. 514 employees was
22
interviewed in Calicut city with Random sampling method. To understand the
relations between characteristics and mode choice of the employees, a multinomial
logit model (MNL) is applied to distinguish the difference among the mode usage.
The employees were chosen set of alternatives, such as: car, two-wheeler, bus, auto
rickshaw and walk. The software used for modelling is SPSS (Statistical Package for
the Social Sciences). The data was analyzed using the SPSS 16.0 MNL program.
514 samples were collected, from this 25 samples are rejected due to missing values
and other reasons, 489 samples were taken for model formulation. Age, gender,
income, time and cost are proved to be the significant factors that influence the
mode usage of the employees. Regarding vehicle ownership, 56.6 % of employees
having one car and 84.6% having one two-wheeler. Auto and Walk are found to be
less (<5%) popular among the working groups. The age group 18-35 is choosing
two- wheeler (32.2%) for their work trip purposes and followed by bus and car. Age
group (>45) are choosing car for their work trip purposes.
Modelling on Mode Choice Behavior of Rural Middle Class Residents – An
Activity Based Approach (2013)
Milimol Philip et.al has analyzed the mode choice behavior using activity based
approach among the middle class rural residents. The data for the analysis was
collected using household travel survey. Multinomial logit model is adopted to
analyze mode choice behavior. In this paper, the rural area selected for data
collection is a village in Ernakulam district in the Indian state of Kerala. There were
a total of 596 data sets including both trips and non-trips. 75% of the data (449) is
taken for calibration of the model and 25% of data (147) is taken for the validation
of the model developed. the variables found significantly influencing the mode
23
choice are only 7. They are four wheeler, three - wheeler and license ownership,trip
walking time ,waiting time, cost and duration. A multinomial logit model to study
the mode choice behavior of rural middle class residents is developed. People who
have no four wheeler ownership, has a negative influence on choosing four wheelers
than two wheelers compared to those having four wheeler ownership. The same is
the case with choosing three wheelers compared to two wheelers between those
without and with three wheeler ownership. The variable no license has a positive
influence on all modes compared to two wheelers than those who hold a license.
Trip duration is having a negative influence on 4 and three wheelers compared to
two wheelers.
Modeling trip attributes and feasibility study of co ordinate bus for school
trips of children (2013)
Dave et.al examined the travel behaviour of primary school children in the study
area of Vadodara city and develops a model to check the feasibility and model share
of coordinate bus.Total 821 samples were collected. An attempt was made through
co-ordinated bus mode offering service for the school trips of children to respond to
transport needs of children. As about 83 % respondents agreed for decision to travel
in co-ordinated bus, so this indicates higher probability regarding acceptance of this
bus service for school trips. Nearly 70% respondents have given positive response
regarding agreement to allow children from other schools to use this bus service.
Safety and comforts of the students travelling in buses are dominant compare to that
of auto rickshaw and van, they prefer coordinate bus system for as it is processed to
be economically viable auto rickshaw and van creates traffic congestion near school.
Auto rickshaw and van should not be having over occupancy of children. In this
24
paper Logit model, Logistic regression analysis, Binary logit model are used. The
significant variables for creating model are age of child, number of persons in
family, expected walk time, monthly family income, number of employed persons in
family, travel distance, travel cost and car ownership in family at 90 % confidence
level. Result shows that The probability of shifting to co-ordinated bus mode
increases if reductions in travel distance and cost can be achieved and the service is
economically viable for morning and afternoon timing of schools among the
proposed routes in selected study area.
Availability and Accessibility Assessment of Public Transit System in Jaipur
City (2013)
Gahlot et.al. examined the impact of new Public Transit System in Jaipur
city.Pedestrian accessibility is the key issue in planning effective Public Transit,
which affects the ridership significantly, The present public transport system
available for the Jaipur is under modification with implementation of mass transit
projects such as bus rapid transit system and Metro, Routes are in-efficiently
rationalized and are not properly regulated with too many buses on some routes
whereas other routes have very less frequency. Hence the present bus transport
system along with improper pedestrian accessibility for public transit system is
insufficient to cater the need of the city. Lack of accessibility and poor quality
pedestrian infrastructure has led to the continued loss of mode share for public
transit trips in cities. AutoCAD, GIS, Public Transit Coverage Index, Bus
Capacities Index, Bus Route Coverage Index , Ideal Stop Accessibility Index, Actual
25
Stop Accessibility Index are used in this study. The study was made in Jaipur city by
referring the city through 70 Traffic Analysis Zone, Bus stop Coverage are used.
Impact of proposed modal shift from private users to bus rapid transit
system: An Indian city case study (2014)
Kumar, Electricwala examined the impact of reduction of CO2 from atmosphere. 49
% of the commuters were observed willing to shift to BRTS based on Biogeme
analysis, Shift of 46% from two wheeler to BRTS is observed based on biogeme
model. Auto to BRTS (87.40%), Car to BRTS (11.49%), Bus to BRTS (85.16%),
Bicycle to BRTS (65%) were observed in their study. Logit model, Binary
logitanalysis, SPSS &Biogeme model were adopted. The proposed and unbiased
disaggregate model and its approximation model was based on the combination of a
RP and SP data 1250 commuters.
Introduction of public bus transit in Indian cities: A case study of
Bardoli, Gujarat, India (2014)
Kumar et.al examined the impact of a new public bus transit system by applying a
binary logit analysis for assessing the possible variation in modal shift behavior. The
mode-choice model was developed, calibrated, and validated using socio-economic
data collected on six proposed corridors in the city of Bardoli, which is not having a
public transport system and commuters use para-transit (3-wheelers) and private
vehicles to commute to their destination. The average trip length is observed to vary
from 6.0–12.0 km. Using SP approach survey, a total of 1250 commuters were
interviewed and each individual commuter was presented. The binary Logit analysis
was used to model the attributes and preferences of the commuters through their
26
stated choices. These traffic compositions were used as input in VISSIM simulation
6.0. There were five modes considered, namely 4-W, 2-W, 3-W, bicycle and
walking. Higher income groups were found less probable to use two-wheelers,
shared auto and more probable to use car. The Statistica and Biogeme models
showed a shift of 6.78% and 11.49% from cars to the bus transit system and a
significant shift of 55.87% and 64.91% from bicycle to the bus transit system.
Mode Choice Analysis Using Generalized Nested Logit Model (2014)
Sekhar et.al. analyzed the mode choice behavior with the help of Nested Logit
models. The questionnaire consisted of sections namely household information,
personal information, commute information and mode choice information.
Household and personal information collected socio demographic data such as age,
gender household size, and household income, number of vehicles in the household,
marital status, education status, and zone of residence in NCR. Commute and Mode
choice information were gathered to information about the mode choice, the purpose
of trip , the total cost involved and the time taken in the commute of the respondent.
The online survey form was circulated on popular social media sites and e mailing
services targeted as random citizens from Delhi and around Delhi. The data
collection and the prediction of the mode choice is similar to the data collected from
the home interview surveys. Instead of doing HIS web base survey can be
performed because it save time and labor required in such home based surveys.Data
accuracy and prediction is similar to the HIS surveys. The model developed by the
Generalized Nested Logit Model is the superior with higher accuracy and better
exploratory power than the Logit models.
27
CHAPTER 3: STUDY AREA: A BRIEF PROFILE
3.1 City Background
Ahmedabad is the fifth largest city and seventh largest metropolitan area in India.
Ahmedabad is the city where the number of vehicles grows faster than humans.
With newly-found money and need for speedier transport, the citizens added nearly
14 lakh vehicles between 2001 and 2011. At the moment, the city has vehicular
population of 31.51 lakh against a population of 65 lakh - meaning every second
person owns a vehicle. More than half the vehicles are two-wheelers (19.74 lakh) as
12% of the vehicles consist of privately-owned four-wheelers.
3.2 Population Growth Trends
From the table 3.1 it can be inferred that the population of Ahmedabad city is
increasing every decade. In the decade 1931-1941 it increased an enormous amount
of 89.7%. Further it has been increasing substantially.
Table 3.1: Decadal Population Growth of Ahmedabad
Census Population Percentage Growth
1911 216,800 16.6
1921 270,000 24.5
1931 313,800 16.2
1941 595,200 89.7
1951 788,300 32.4
1961 1,149,900 45.9
1971 1,950,000 69.6
1981 2,515,200 29.0
1991 3,312,200 31.7
2001 4,525,013 36.6
2011 6,352,254 40.1
Source: RTO, Ahmedabad
28
3.3 Vehicular Growth Trends
As per the city RTO, everyday 500 vehicles are added to the population of the
vehicular traffic. 1.70 lakh new vehicles added in 2009-10, the number of new
vehicles in 2011-12 was 2.22 lakh.
Table 3.2: Modal Share as registered at RTO Ahmedabad (Oct, 2014)
Sr.
No. Types of vehicles
Total vehicles upto
2014
Total vehicles added
in 2014
1 2 Wheelers 2369400 89074
2 3 Wheelers 169000 6193
3 Cars 498748 27206
4 Taxi 18280 634
5 Trucks And Buses 198184 2425
7 Jeeps 30747 18
8 Ambulance Van 1186 18
9 Others 10855 1730
Total 32,96,400 1,27,298
Source: RTO, Ahmedabad
Figure 3.1: Share of Vehicles Registered at RTO Ahmedabad
At the time of formation of the state of Gujarat, in 1961, there were only 43,000
vehicles registered. This figure has risen to over 70 Lakh vehicles by the year 2004,
72%
5%
15%
1%
6%
1%
2 wheelers 3 wheelers Cars Taxi
trucks and buses jeeps ambulance van others
29
recording a rise by 160 folds in four decades. In the recent past, annual additions
have been high and increasing. During the years 2001 to 2002, the increase in the
number of vehicles registered was by 4.3 lakh. This has risen to 5.1 and 5.7 during
2002 to 03, and 2003 to 04 respectively.
Table 3.3: Vehicular Growth Rate of Ahmedabad (Till Oct 2014)
Years Vehicular Population Growth
2000-2001 69811 0
2001-2002 74952 6.85
2002-2003 91643 18.21
2003-2004 109161 16.05
2004-2005 136983 20.31
2005-2006 147560 7.16
2006-2007 158290 6.77
2007-2008 145057 -9.12
2008-2009 127449 -13.82
2009-2010 170449 25.23
2010-2011 219119 22.21
2011-2012 222450 1.49
2012-2013 190256 -16.92
2013-2014 184745 -2.98
Source: RTO, Ahmedabad
From the Table 3.3, it can be inferred that the vehicle growth was higher in the
period of two years from 2009-2011, whereas, in the last two years the vehicle
growth has been observed to be negative.
3.4 Study Zone Particulars
Ahmedabad is divided into 7 zones as per election committee of Ahmedabad which
are shown in the figure. In this study, two zones are considered as Traffic Analysis
Zones which are Central Zone and West Zone. The reason behind selection of these
particular zones is that the central zone is seen as a commercial zone whereas west
zone is seen as a residential zone. Hence comparison of travel behavior between a
30
central zone and a residential zone can be done. Also trip purpose would be different
in both the zones. The zone which is residential will be highly responsible for Trip
Generation in the morning peak hours where as the commercial zone will attract
more trips. In the evening, the scenario gets vice versa. The statistics of both the
zones are shown in Table 3.4.
Figure 3.2: Study Area (Source: AMC)
Table 3.4: Study Area Particulars(2011)
Spatial Unit West Zone Central Zone
Population 8.24 Lakh 6.16 Lakh
Area (sq.km) 56.53 sq km 16.6 sq km
Population Density (persons/sq.km) 14986 10898
No of households 1.64 lakh 1.23 Lakh
Source: Election department, AMC 2011, Census Report.
West
Zone
New West Zone
Central Zone
East Zone
South
Zone
North Zone
31
3.5 Sampling and Data collection
Random Stratified Sampling was undertaken for data collection. West and Central
Zones were divided into 19 sub-zones. The sub-zones under the Central zone of
study area are as shown in Figure 3.3 and Figure 3.4 respectively. Numbers of
samples are worked out accordingly taking the population percentage of the West
zone and Central zone as shown in Table 3.5 and Table 3.6 respectively. A total of
500 samples were taken by conducting Household Interview Survey across both the
zones. A pre-designed Questionnaire was made for conducting HIS.
Figure 3.3: Central Zone of Ahmedabad (Source: AMC)
Figure 3.4: West Zone of Ahmedabad (Source: AMC)
32
Table 3.5: Representative Samples from Central Zone
Sr.No. Name of Area
Area
(sq
km)
Population Percentage
Required
Sample
Quantity
1 Girdharnagar 2.33 61,496 4.3 21
2 Madhupura 2-88 62,714 4.4 22
3 Dudheshwar 2.68 71,512 5.0 25
4 Shahpur 1.03 70,207 4.9 24
5 Dariyapur 0.79 68,533 4.8 24
6 Kalupur 1.38 58,151 4.0 20
7 Raikhad 2.12 74,368 5.2 26
8 Jamalpur 1.17 79,813 5.5 28
9 Khadia 2.22 69,249 4.8 24
Total 16.6 6,16,043 42.8 214
Table 3.6: Representative Samples from West Zone
Sr. No. Name of Area Area
(sq km) Population Percentage
Required
Sample
Quantity
1 Chandkheda
– Motera 16 1,19,409 8.3 41
2 Vasna 5.05 98,699 6.9 34
3 Sabarmati 5.34 66,817 4.6 23
4 Old Vadaj 3.42 75,687 5.3 26
5 Nava Vadaj 2.14 77,814 5.4 27
6 Naranpura 3.21 80,822 5.6 28
7 Sp Stadium 3.33 79,190 5.5 27
8 Navrangpura 7.16 67,794 4.7 24
9 Ambawadi 6.33 76,789 5.3 27
10 Paldi 4.55 81,579 5.7 28
Total 56.53 8,24,600 57.2 286
33
Figure 3.5: Typical Home Interview Survey Conducted in Naranpura
3.6 Data Processing
Data processing is broadly the collection and manipulation of items of data to
produce meaningful information. In this sense it can be considered a subset of
information processing. This term is often used more specifically in the context of a
business or other organization to refer to the class of commercial data processing
applications.
Data processing includes various processes such as:
Validation – ensuring that supplied data is “clean , correct and useful”
Sorting – “arranging items in some sequence and/or in different sets”
Summarization – reducing detail data to its main points
Aggregation – combining multiple pieces of data
Analysis – the “collection, organization, analysis, interpretation and presentation
of data”
Reporting – list detail or summary data or computed information
Classification – separates data into various categories
34
Figure 3.6: Typical Dataset of Household characteristics
Figure 3.6 shows a typical dataset of Household characteristics which comprises of age
wise division of family members, the household size, household income, income group
and the vehicular ownership of the households being interviewed.
Figure 3.7: Typical Travel characteristics (RP data)
35
Figure 3.7 shows a snapshot of typical dataset of Travel characteristics, which
includes the parameters like trip purpose, present mode of travelling, trip length,
travel cost, travel time, trip generated per family, number of working and
educational members per family and trips per capita per day.
Figure 3.8: Typical Dataset of Special Trips (RP data)
Figure 3.8 shows a snapshot of typical dataset of Special trips,these trips are
considered to be the trips which are occasional especially for recreational purpose.
These trips are not always made on a regular basis. Generally special trips are
believed to be occurring on a weekends. Here various travel impedance are
incorporated to evaluate the transportation behavior. It can be observed that mainly
special trips are made by personal vehicles as it gives the more flexibility in the
sense of route and timing.
36
Figure 3.9: Typical Dataset of Public Transport Parameters (SP data)
Figure 3.9 shows a snapshot of typical dataset of Public transport parameters, these
parameters are evaluated by both SP data and RP data. Firstly, existing reason for
using public transportation was asked. Later on thepreferences of commuters for
accessibility parameters were asked. It provides a platform to assess the success of
any transportation system. Here attributes like ingress-egress time, waiting time
were asked for both existing and desired scenario. Load factor is a very crucial thing
to determine as it shows the level of satisfaction for the current fleet size. Frequency
also plays a very important role which shows the hike in ridership by incorporating
some specific policy.
3.7 Questionnaire Survey Form
The designed questionnaire survey form comprises of three sections, viz. Household
and Socio-economic details, Travel Characteristics and Public Transport Particulars.
After conducting pilot survey, few questions were added and few were modified
before going for final survey. The typical questionnaire form is shown below.
37
Questionnaire Survey Form – Home Interview Survey
38
CHAPTER 4: DATA ANALYSIS
Analysis of data is a process of inspecting, cleaning, transforming, and
modeling data with the goal of discovering useful information, suggesting
conclusions, and supporting decision-making.
The analysis, irrespective of whether the data is qualitative or quantitative, may:
• Describe and summarize the data
• Identify relationships between variables
• Compare variables
• Identify the difference between variables
• Forecast outcomes
4.1 Household &Socio-Economic Analysis
It is the study that examines social and economic factors to better understand how
combination of both influences something.
4.1.1. Income Spectrums
Table 4.1 shows the demarcation of various socio-economic classes based on
monthly household income. Three income classes viz. HIG (high income group),
MIG (middle income group) and LIG (low income group) are defined as per the
criteria laid by Gujarat Housing Board.
Table 4.1: Categorical Income Division
Income Category Monthly Income Range
LIG < 15000
MIG 15000 - 45000
HIG > 45000
Source: Gujarat Housing Board
39
Figure 4.1a: Socio economic classification across study area of two zones
From the Figure 4.1(a)it can be inferred that over half of the collected samples fell
into the category of MIG whereas a small proportion of 12 % belongs to the LIG.
The average household income in West zone is observed as 43,197 INR whereas
Central zone is having a HHI of 24,983 INR.
Figure 4.1b: Socio-Economic classifications in Central & West Zone
From the Figure 4.1(b)it can be inferred that MIG is dominating in both the zones,
but HIG concentration is quite higher than LIG in west zone unlike in the case of
central zone where both HIG and LIG proportion is observed to be similar.
HIG
33%
MIG
55%
LIG
12%
LIG11%
MIG78%
HIG11%
Central zone
LIG12%
MIG47%
HIG41%
West Zone
40
4.1.2 Occupation based Household Composition
The household composition on the basis of the occupation such as working,
education and others among the three income groups is described below.
Figure 4.2: Family composition for respective Socio-economic classes
FromFigure 4.2, it can be seen that the proportion of working member is highest in
LIG followed by MIG and HIG. Reverse is the trend observed for education
members.
Figure 4.3: Area wise Family Composition in West Zone
35%
26%
39%
HIG
W M E M OTHERS
37%
20%
43%
MIG
W M E M OTHERS
44%
15%
41%
LIG
W M E M OTHERS
43 5139 32 33 34 37 38 38
29 1916 19 20
29 1735
18
29 3046 49 47 36 46
2744
W M E M OTHERS
41
Figure 4.3 shows the percentage share of working members, educating members and
others for West zone which is a residential zone. Observation shows that the area
like Sabarmati is having the highest proportion of working members whereas area
like SP stadium shows higher proportion of educational members. Members which
are neither of the earlier two are observed to be on higher side in Chandkheda-
Motera.
Figure 4.4: Area wise Family Composition in Central Zone
Figure 4.4 shows the percentage share of working members, educating members and
others. Central zone is considered to be a commercial zone. Observation shows that
the area like Dariyapur is having the highest share of working members whereas
areas like Jamalpur and Dariyapur showshigher proportion of educational members.
Members which are neither educational nor working are more in Madhupura.
37 38 31 37 36 35 30 38 41
26 138
19 15 11 18 1026
37 49 6144 48 54 52 52
33
W M E M OTHERS
42
Figure 4.5: Family Composition across Two Zones
It is inferred from the Figure 4.5, that the proportion of working members are almost
same across both the zones but the same differs in case of education members. This
might be due to concentration of LIG and lower MIG people in central in contrast to
west zone. Members which are neither engage in education nor working activity are
more in central zone.
4.1.3 Cross-classification Table
Most traffic analyses zones tend to contain a mixture of social and economic classes.
The use of regression equations is based on zone wise aggregated measures of the
zonal characteristics that tend to submerge important characteristic of the travel
demand. If the aggregation of data used in the zonal regression equations is to be
effective, it is important to have as much homogeneity within the units of
aggregation as possible. The purpose of regression equations is to explain the
variation in trip making between the zones in terms of number of independent
variables. Category analyses or cross classification technique is a method developed
by Wotton and Pick that has been used in some transportation studies in UK. It is
based on the estimate range of income, level of car ownership and household
structure.
37.74 36.41
23.05 17.83
39.21 45.76
WEST ZONE CENTRAL
ZONE
W M E M OTHERS
43
Table 4.2: Cross Classification Table of Vehicle Ownership v/s HHS
HHS/ VO 0 1 2 3 4,5
1 1 2 1 0 0
2 2 8 3 1 0
3 2 6 8 4 2
4,5 2 13 18 9 5
>5 0 4 2 5 3
From the above Table 4.2, it can be observed that households with family size four
or more are of higher proportion, whereas the families with one or two vehicles are
most common.
4.1.4 Comparison of House Hold characteristics
Figure 4.6 shows the comparison of various parameters such as HHS, Vehicles, WM
,EM and TPCD. It has been observed that west zone is dominant in all the stated
parameters except EM whereas Central zone is dominant in EM.
Figure 4.6: Socio Economic Parameters across Two Zones
4.05
2.19
1.54
1.08
1.41
3.75
1.521.36 1.36 1.28
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
WEIGHTED
AVERAGE HHS
WEIGHTED AVG
VEHICLES
WEIGHTED AVG
WORKING
MEMBERS
WEIGHTED AVG
EDU MEMBERS
WEIGHTED AVG
TPCPD
WEST ZONE CENTRAL ZONE
44
4.2 Travel Characteristics
Travel characteristic deals with the impedance which are related to a trips. It caters
with the parameters like trip length, modal shares, trip cost, trip length, etc. it cover
up all the factors which influence the nature of the trip.
4.2.1 Mode share
Mode share is a process of separating person trips by the mode of travel. It is usually
expressed in the form of a fraction, ratio or percentage of the total number of trips.
In general, modal split refers to the, trips made by made by private vehicles as
opposed to public transport.
Figure 4.7: Modal Share across both the zones
The following figures depict the mode share in respective West and Central zone in
Ahmedabad city.
58%19%
22%
1%
2w car cycle others
45
Figure 4.7(a): Modal Share across Central Zone
From the figure 4.7(a), it can be observed that two-wheeler is having the maximum
modal share with 61% followed by bicycle.
Figure 4.7(b): Modal Share across West Zone
From the Figure 4.7(b), it can be observed that the two-wheeler is having the
maximum share with 56% followed by four-wheelers which is quite high than in the
Central zone.
61%8%
28%
3%
2w car cycle others
56%25%
19%
0%
2w car cycle others
46
Figure 4.8(a): Modal Share for LIG
Figure 4.8(b): Modal share for MIG
From the figure, it can be observed that the two-wheeler is having the maximum
share with 58% followed by bicycle among the people of middle income group.
Figure 4.8(c): Modal Share for HIG
The figure 4.8(c) shows that the proportion of two-wheelers is higher in HIG.
51%
5%
35%
1% 6% 2%
2w car cycle others AMTS BRTS
64%12%
17%
4% 2% 1%
2w car cycle others AMTS BRTS
53%31%
7%7%
1%1%
2w car cycle others AMTS BRTS
47
Table 4.3: RP and SP data of WT across West Zone for BRTS and AMTS
4.2.2 Sub zonal mode share
Central Zone
From the below figures it can be inferred that mode share for two-wheelers is in
maximum proportion in Dudheshwar (85%) whereas, least proportion in Jamalpur
(36%) mode share for four-wheelers is in maximum proportion in Girdhar nagar
(26%) whereas, least proportion in Madhupura(7%) and Raikhad (7%).
Figure 4.9(a): Mode share of Dudheshwar Figure 4.9(b): Mode share of Khadia
85%
4% 11%
2w walk cycle
57%23%
4%11%
5%
2w 4w BRTS walk cycle
Zone Mode Users(%) Trip
length(km) Cost(Rs/km)
Maintenanc
e(Rs/km)
Time
(min/km)
West 2w 59 8.41 4.3 0.3 2.4
4w 32 10.15 7.3 0.5 2.9
AUTO 5 6.3 8.8 0 3.2
AMTS 3 11.56 2.1 0 2.6
BRTS 1 8.25 2.8 0 1.8
Central 2w 86 5.65 5 1.1 1.1
4w 6 14.28 6.2 0.6 2.2
AUTO 3 11.88 5 0 4.3
AMTS 4 8.81 1.9 0 2.6
BRTS 1 11 2 0 2.5
48
Figure 4.9(c): Mode share of Jamalpur Figure 4.9(d): Mode share of Madhupur
Figure 4.9(e): Mode share of Kalupur Figure 4.9(f): Mode share of Dariyapur
36%
7%7%
1%1%
12%
36%
2w 4w auto
BRTS Private bus walk
cycle
70%
3%
27%
2w 4w cycle
48%
13%4%
18%
17%
2w 4w auto walk cycle
63%15%
2%
10%
1%9%
2w 4w auto
BRTS Private bus cycle
49
Figure 4.9(g): Mode share of Shahpur Figure 4.9(h): Mode share of Raikhad
Figure 4.9(i) Mode share of Girdharnagar
37%
25%
6%2%
17%
13%
2w 4w auto BRTS walk cycle
52%
7%2%
22%
17%
2w auto BRTS walk cycle
74%
26%
2w 4w
50
West Zone
From the below figures it can be inferred that mode share for two-wheelers is in
maximum proportion in Paldi (70%) whereas, least proportion in SP road (37%)
mode share for four-wheelers is in maximum proportion in SP road (63%) whereas,
least proportion in Old vadaj (12%).
Figure 4.10(a): Mode share of Ambawadi Figure 4.10(b) Mode share of C-Motera
Figure 4.10(c): Mode share of Paldi Figure 4.10(d): Mode share of Vasna
54%40%
2% 2% 2%
2w 4w BRTS walk cycle
41%
20%
12%
18%
9%
2w 4w BRTS walk cycle
70%
18%
6%
3% 3%
2w 4w BRTS walk cycle
49%
43%
4%
2%2%
2w 4w BRTS walk cycle
51
Figure 4.10(e): Mode share of Sabarmati Figure 4.10(f): Mode share of S.P.Road
Figure 4.10(g): Mode share of Old Vadaj Figure 4.10(h): Mode share of New Vadaj
58%
14%
5%
1%
8%
14%
2w 4w BRTS
Private bus walk cycle
37%
63%
2w 4w
59%
12%
5%
1%
3%
1%5%
14%
2w 4w
auto BRTS
44%
15%
3%
1%
10%
27%
2w 4w
BRTS Private bus
52
Figure 4.10(i): Mode share of Naranpura
4.2.3 Vehicular ownership
Figure 4.11: Vehicular Ownership across Two Zones
It is seen that the share of 2w and 4w west zone is almost same. This might be the
reason for relatively mixed traffic condition than the central zone where modal share
for 2w is measurably more that is 60% of the all the vehicles owned. There can be
another reason of more purchasing power of west zone people than the central zone.
Even the road space of central zone is more suitable to 2w which might be the result
of greater shares.
47%
43%
1% 3%
6%
2w 4w BRTS Private bus cycle
37 34 29
60
16 24
2W 4W CYCLE
WEST ZONE CENTRAL ZONE
53
4.2.4 Trip Length Distribution
Figure 4.12(a): Trip Length distributions across two zones
From the figure 4.12(a), it can be observed that most of the person trips are in the
range of 0.6 to 1 kms, 4.1 to 5 kms and 10.1 to 15 kms.
Figure 4.12(b): Trip Length distribution across Central Zone
The average trip length of the central zone is 4.65 km.It is observed that around 30
% of the person trips have trip length of less than 1 km. If we bifurcate the trips by
0.00
2.00
4.00
6.00
8.00
10.00
12.00P
erce
nta
ge o
f Tr
ips
Trip Length (km)
2216
32
20
10
47
15
21
10
7
30
16
29
17
9
< 1 1.1 < 3 3.1 - 6 6.1 - 10 > 10
Avg
Educational
Working
54
their purposes than 47% and 22 % of the total population of a central zone are
generating trips of less than 1 km trip length for the education and work purposes
respectively. On the other hand it is observed that only 9% of the person trips have
trip length greater than 10 km. If we bifurcate the trips by their purposes than 7%
and 10% of the total population of the central zone are generating trips of more than
10 km trip length for the education and work purposes respectively.The average trip
length of working member is observed to be 5.5 km whereas in the case of
educational member, it is 3.5 km.
Figure 4.12(c): Trip Length distribution across West Zone
The average trip length of the west zone is 7.78 km .It is observed that around 28 %
of the person trips have trip length in the range of 3.1-6 km. If we bifurcate the trips
by their purposes than 30% and 27 % of the total population of a central zone are
generating trips in the range of 3.1-6 km trip length for the education and work
purposes respectively. On the other hand it is observed that only 8% of the person
trips have trip length less than 1 km. If we bifurcate the trips by their purposes than
13% and 5% of the total population of the central zone are generating trips of less
than 1 km trip length for the education and work purposes respectively. The average
511
27 27 30
13
22
3020 15
8
15
28
25
15
< 1 1.1 < 3 3.1 - 6 6.1 - 10 > 10
Working Educational Avg
55
trip length of the working member is 8.9 km whereas that of educational member is
6.6 km.
4.3 Public Transport Characteristics
4.3.1 Load factor
Figure 4.13(a): Load Factor across West Zone for AMTS and BRTS
Above chart shows the Load Factor in the West Zone for respective sub zones. It is
noted from the above figure that the average load factor for BRTS is 101.85 whereas
the average load factor for AMTS is 97.89.
Figure 4.13(b): Load Factor across Central Zone for AMTS and BRTS
OLDWADAJ
SABARMATI
NEWWADAJ
PALDIAMBAW
ADIC M
NARANPURA
NAVRANGPUR
AS P vasna
BRTS 108.69 97.36 105.35 88.33 98.33 119.84 98.43 92.85 105.55 97.61
AMTS 125 125 0 90.62 90 92.39 102.27 94.44 105.55 98.07
0
20
40
60
80
100
120
140
Load
Fac
tor
JAMALPUR
SHAHPURMADHUP
URAKHADIA RAIKHAD
GIRDHARNAGAR
KALUPURDUDHESHWAR
DARIYAPUR
BRTS 0 105.76 157.84 103.12 107.69 0 113.75 104 88.46
AMTS 101.78 109.09 108.33 0 0 127.47 0 0 104.54
0
20
40
60
80
100
120
140
160
180
Load
Fac
tor
56
The average load factor of a central zone for AMTS and BRTS is 110.65 and 109.9.
The area wise load factor for both BRTS and AMTS is shown in the above figure.
Figure 4.14(a): Load Factor across West Zone
Figure 4.14(a) shows that maximum load factor of 116.84 is observed in Old Vadaj.
Whereas, New Vadaj is observing minimum load factor of 52.87. The average load
factor observed from the above figure is 99.87.
Figure 4.14(b): Load Factor across Central Zone
Figure 4.14(b) shows that maximum load factor of 133.08 is observed in
Madhupura. Whereas, Jamalpur is having minimum load factor of 50.69.The
average Load Factor observed from the above chart is 110.27.
116.845 111.18
52.675
89.475 94.165106.115 100.35 93.645
105.5597.84
West Zone
50.89
107.425
133.085
51.56 53.84563.735
56.875 52
96.5
Central Zone
57
4.3.2 Waiting time
Table:4.4RP and SP data of WT across West Zone for BRTS and AMTS
From the table 4.4 waiting time of the respective zones for BRTS and AMTS can be
inferred. It is observed that average existing waiting time of BRTS is 12 minutes
whereas it is 13 minutes in the case of AMTS. This average time is based on the
existing conditions. When asked about desired waiting time to the commuters, the
values come out to be 7 minutes and 6 minutes for the BRTS and AMTS
respectively.
Sub-Zone Mode
Waiting time (min)
Existing Desired
Old Wadaj BRTS 13.04 5.95
AMTS 15 10
Sabarmati BRTS 11.05 8.15
AMTS 10 8.75
New Wadaj BRTS 11.6 9.28
AMTS 0 0
Paldi BRTS 7 5
AMTS 8 5
Ambawadi BRTS 6.36 4.9
AMTS 11 5
Chandkheda-Motera BRTS 11.84 6.05
AMTS 11.3 6.08
Naranpura BRTS 14.68 5.93
AMTS 12.27 5
Navrangpura BRTS 13.38 5.42
AMTS 15.55 6.66
S P BRTS 14.16 5.72
AMTS 16.11 5
Vasna BRTS 18.8 6.9
AMTS 16.15 5.76
58
Table 4.5: RP and SP data of WT across Central Zone for BRTS and AMTS
From the table 4.5, waiting time of the respective zones for BRTS and AMTS can
be inferred. It is observed that average existing waiting time of BRTS is 10 minutes
whereas it is 13 minutes in the case of AMTS. This average time is based on the
existing conditions. When asked about desired waiting time to the commuters, the
values came out to be 6 minutes and 8 minutes for the BRTS and AMTS
respectively.
4.3.3 Ingress and Egress distance
Ingress distance is the distance required to be travelled from origin to the bus stand.
Egress time is the distance required to be travelled from the bus stand to the
destination.
Sub-Zone Mode
Waiting time (min)
Existing Desired
Jamalpur BRTS 0 0
AMTS 15.35 7.85
Shahpur BRTS 8.46 5.76
AMTS 8.18 5.45
Madhupura BRTS 11.15 5.76
AMTS 10 5.55
Khadia BRTS 7.91 5
AMTS 0 0
Raikhad BRTS 8.26 5.57
AMTS 0 0
Girdharnagar BRTS 0 0
AMTS 15 7.14
Kalupur BRTS 8.75 5.5
AMTS 0 0
Dudheshwar BRTS 10.8 6.08
AMTS 0 0
Dariyapur BRTS 18.84 9.6
AMTS 16.81 10
59
Table 4.6: RP and SP data of Egress and Ingress for AMTS and BRTS across
West Zone
Sub-Zone Mode
Ingress (m) Egress (m)
Existing Desired Existing Desired
Old Wadaj BRTS 189.13 84.78 184.78 82.6
AMTS 150 50 100 50
Sabarmati BRTS 197.36 107.89 171.05 100
AMTS 187.5 150 200 150
New Wadaj BRTS 301.78 106.07 169.64 100
AMTS 0 0 0 0
Paldi BRTS 860 433.33 696.66 373.33
AMTS 325 200 475 250
Ambawadi BRTS 218.33 121.66 295 101.66
AMTS 100 80 130 100
C M BRTS 644.73 163.15 639.47 189.47
AMTS 258.69 143.47 393.47 159.13
Naranpura BRTS 1031.25 462.25 793.75 406.25
AMTS 609.09 372.72 554.54 272.72
Navrangpura BRTS 1171.41 392.85 885.71 335.71
AMTS 888.88 477.77 988.88 477.77
S P BRTS 547.22 297.22 411.11 275
AMTS 833.33 438.88 1144.44 427.77
Vasna BRTS 1300 519.04 871.42 457.12
AMTS 784.61 407.61 838.46 469.2
From the Table 4.6, Ingress and Egress distance for the west zone can be known. It
is observed that existing value of the Ingress distance for AMTS and BRTS is 510 m
and 582 m respectively. The desired value for the same is 284 m and 243 m
respectively. The existing Egress value for the BRTS and AMTS is 446 m and 609
m respectively. The desired value for the same is 219 m and 289 m respectively.
60
Table 4.7: RP and SP data of Egress and Ingress for AMTS and BRTS across
Central Zone
Sub-Zone Mode
Ingress(m)
Egress(m)
Existing Desired Existing Desired
Jamalpur BRTS 0 0 0 0
AMTS 257.14 130.35 244.64 126.78
Shahpur BRTS 1107.69 88.46 330.76 88.46
AMTS 100 90.9 754.54 86.36
Madhupura BRTS 892.3 153.84 280.7 130.76
AMTS 144.44 111.11 577.77 105.55
Khadia BRTS 560.41 104.16 366.66 108.33
AMTS 0 0 0 0
Raikhad BRTS 500 98.38 584.61 88.46
AMTS 0 0 0 0
Girdharnagar BRTS 0 0 0 0
AMTS 261.9 121.42 221.42 107.42
Kalupur BRTS 362.5 95 470 97.5
AMTS 0 0 0 0
Dudheshwar BRTS 896 96 418 96
AMTS 0 0 0 0
Dariyapur BRTS 1090.3 376.92 1000 430.76
AMTS 690.9 290.9 863.63 381.81
From Table 4.7, Ingress and Egress distance for the central zone can be known. It is
observed that existing value of the Ingress distance for AMTS and BRTS is 283 m
and 718 m respectively. The desired value for the same is 142 m and 128 m
respectively. The existing Egress value for the BRTS and AMTS is 483 m and 431
m respectively. The desired value for the same is 132 m and 148 m respectively.
61
4.3.4 Public Transport Features
Figure 4.15(a): Features of Public Transportation across West Zone
Figure 4.15(a) shows that the priority of existing commuters is seats. Whereas, the
desired priority is AC.
Figure 4.15(b): Features of Public Transportation across Central Zone
Figure 4.15(b) shows that the priority of existing commuters is seats. Whereas, the
desired priority is AC.
22
9
51
8
3
6
37
11
13
3
16
20
AC
CHEAP
Seat
SPEED
SPACE
FREQUENCY
D % E %
5
21
66
2
1
4
43
4
37
0
10
5
AC
CHEAP
Seat
SPEED
SPACE
FREQUENCY
D % E %
62
CHAPTER 5: Model Development
Model development is considered an effective research method. It assists
investigators and scientists in relating more accurately to reality; it also aids them to
describe, predict, test or understand complex systems or events. Thus, models often
provide a framework for the conduct of research and might consist of actual objects
or abstract forms, such as sketches, mathematical formulas, or diagrams. A model is
an abstraction, a mental framework for analysis of a system. It involves simplified
representations of real-world phenomena. In general terms different authors
suggested the importance of: A theoretical framework for the definition, criteria and
characteristics of models:
Practical guidelines that describe the procedural aspects of model building;
The availability of data defining the factual situation for the model which is
being constructed.
The following section shows the developed six regression based models including
the pilot regression model viz. HIG, MIG, LIG, Central zone, West zone and Total
500 samples.
5.1 Regression based Trip Generation Model (Multi-Linear Regression)
Regression model of few samples formulated with the help of SP & RP data
collected from the HIS. This equation shows the relation between dependent
variable and independent variables stated as below.
Y= number of trips (TPCD)
63
U = Representation of (disturbance term) (t-stat: 5.79)
X1= Household size (HHS)(t-stat: 3.38)
X2= Working members(t-stat: 3.60)
X3= Education members (t-stat: 3.60)
Y = U +a1 X1 +a2 X2 +a3 X3,
R2= 0.43
Standard Error= 0.38
Y = 0.57 -0.25 X1 + 0.45 X2 + 0.44 X3 (Pilot Model)
The results show that the number of trips (TPCD) of a particular family depends on
the working and educating members.
Regression Model – 1 (HIG)
Y= number of trips (TPCD)
U = Representation of (disturbance term)(t-stat: 8.022)
X1= Household size (HHS)(t-stat:-6.059)
X2= Working members (t-stat: 5.595)
X3= Education members (t-stat: 6.136)
X4=Household income (HHI)(t-stat:-0.74)
X5=Total Vehicle(t-stat:0.255)
Y = U +a1 X1 +a2 X2 +a3 X3,
R2= 0.342722
Standard Error= 0.495758
Y = 1.475 - 0.193 X1 + 0.383 X2 + 0.218 X3 -1.6E-06 X4 + 0.012 X5
Regression Model – 2 (MIG)
Y= number of trips (TPCD)
64
U = Representation of (disturbance term)(t-stat:10.14)
X1= Household size (HHS)(t-stat:11.0)
X2= Working members (t-stat: 11.3)
X3= Education members (t-stat: 11.3)
X4=Household income (HHI) (t-stat:-0.48)
X5=Total Vehicle (t-stat: 0.32)
Y = U +a1 X1 +a2 X2 +a3 X3,
R2= 0.455
Standard Error= 0.532
Y = 1.422 - 0.346 X1 +0.595 X2 +0.511 X3 - 2.5E-06 X4 + 0.017 X5
Regression Model – 3 (LIG)
Y= number of trips (TPCD)
U = Representation of (disturbance term) (t-stat: 8.022)
X1= Household size (HHS) (t-stat:-6.05)
X2= Working members (t-stat: 5.595)
X3= Education members (t-stat: 6.136)
X4=Household income (HHI) (t-stat:-0.74)
X5=Total Vehicle (t-stat: 0.255)
Y = U +a1 X1 +a2 X2 +a3 X3,
R2= 0.342722
Standard Error= 0.495758
Y = 1.475 - 0.193 X1 + 0.383 X2 + 0.21 X3 - 1.6E-06 X4 + 0.012 X5
Regression Model – 4 (Central)
Y= number of trips (TPCD)
65
U = Representation of (disturbance term) (t-stat: 6.625)
X1= Household size (HHS) (t-stat:-6.58)
X2= Working members (t-stat: 4.74)
X3= Education members (t-stat: 4.852)
X4=Household income (HHI) (t-stat:-0.44)
X5=Total Vehicle (t-stat:-1.23)
Y = U +a1 X1 +a2 X2 +a3 X3+a4 X4+a5 X5
R2= 0.474
Standard Error= 0.655
Y = 2.27 - 0.52 X1+0.543 X2 + 0.578 X3 -1.1E-05 X4 - 0.140 X5
Regression Model – 5 (West)
Y= number of trips (TPCD)
U = Representation of (disturbance term) (t-stat: 11.9)
X1= Household size (HHS) (t-stat: 12.1)
X2= Working members (t-stat: 10.9)
X3= Education members (t-stat: 11.9)
X4=Household income (HHI) (t-stat: 1.20)
X5=Total Vehicle (t-stat: 0.86)
Y = U +a1 X1 +a2 X2 +a3 X3+a4 X4+a5 X5
R2= 0.459741
Standard Error= 0.50
Y = 1.582-0.341 X1 + 0.492 X2 + 0.471 X3 +1.78E-06 X4-0.03 X5
Regression Model – 6 (Combined)
Y= number of trips (TPCD)
66
U = Representation of (disturbance term) (t-stat: 20.1)
X1= Household size (HHS) (t-stat:-15.7)
X2= Working members (t-stat: 14.6)
X3= Education members (t-stat: 15.3)
X4=Household income (HHI) (t-stat: 0.520)
X5=Total Vehicle (t-stat:-0.53)
Y = U +a1 X1 +a2 X2 +a3 X3+a4 X4+a5 X5
R2= 0.45
Standard Error= 0.53
Y = 1.467 - 0.34 X1 + 0.569 X2 + 0.528 X3 +7.21E-07 X4 - 0.016 X5
The developed trip generation models find application in predicting the trips
provided the predictor variables in various sub-zones of the two study zones.
5.2 Transit Choice Modelling
With the substantial increase in the number of vehicles in the urban area, the need
for a better transit system arises. Introduction of a new transit system helps in the
reduction of congestion, parking problems, travel cost, travel time.
5.2.1 Logistic Regression
It determines the impact of multiple independent variables presented simultaneously
to predict membership of one or other of the two dependent variable categories.
Logistic regression employs binomial probability theory in which there is only two
values to predict: that probability (p) is 1 rather than 0, i.e. the event/person belongs
to one group rather than the other. Logistic regression forms a best fitting equation
67
or function using themaximum likelihood method, which maximizes the probability
of classifying the observed data into the appropriate category given the regression
coefficients.
There are two main uses of logistic regression:
The first is the prediction of group membership. Since logistic regression
calculates theprobability of success over the probability of failure, the results of
the analysis are in the form of an odds ratio.
Logistic regression also provides knowledge of the relationships and strengths
among the variables (e.g. marrying the boss’s daughter puts you at a higher
probability for job promotion than undertaking five hours unpaid overtime each
week).
Assumptions of logistic regression
Logistic regression does not assume a linear relationship between the dependent and
independentvariables. The dependent variable must be a dichotomy (2
categories).The independent variables need not be interval, nor normally distributed,
nor linearly related, nor of equal variance within each group. The categories
(groups) must be mutually exclusive and exhaustive; a case can only be in one group
and every case must be a member of one of the groups.Larger samples are needed
than for linear regression because maximum likelihood coefficients are large sample
estimates. A minimum of 50 cases per predictor is recommended. The logistic
equation is expressed as below:
logit [p(x)] = log [ p(x)/ 1- p(x)] = a =b1x1 = b2x2 = b3x3……
68
5.3 Transit choice Model Development
This section includes the developed model by logistic regression viz. HIG , MIG,
LIG, Central zone, West zone and Total 500 samples. The below table includes the
list of abbreviation of the variables used in the regression.
Table 5.1: List of Variables
BLRM Binary logistic regression model
WT Waiting time
TV Total vehicles
LF Load factor
HHS Household size
Model – 1, BLRM-HIG
A logistic regression analysis is conducted to predict the choice behaviour for city
transit service using household size, total vehicles, waiting time, load factor, ingress
and egress as predictors for High Income Group. The output of the logistic
regression as obtained from SPSS software is shown and discussed below.
Table 5.1(a): Hosmer and Lemeshow goodness of fit Test
Step Chi-square df Sig.
1 5.457 8 .708
If the Hosmer-Lemeshow goodness-of-fit test statistic is greater than 0.05, as we
want for well-fitting model, we fail to reject the null hypothesis that there is no
difference between observed and model-predicted values, implying that the model’s
estimates fit the data at an acceptable level. That is, well-fitting models show non-
significance on the H-L goodness-of-fit test. This desirable outcome of non-
significance indicates that the model prediction does not significantly differ from the
69
observed. Our H-L statistic has a significance of 0.708 which means that it is not
statistically significant and therefore our model is quite a good fit.
Table 5.1(b): Classification Table
Observed
Predicted
Y Percentage Correct
0 1
Step 1 Y
0 53 23 69.7
1 23 39 62.9
Overall Percentage 66.7
Rather than using a goodness-of-fit statistic, we often want to look at the proportion
of cases we have managed to classify correctly. For this we need to look at the
classification table, which tells us how many of the cases where the observed values
of the independent variable were 1 or 0 respectively have been correctly predicted.
Prediction success overall is 66.7 % (62.9% for public transport and 69.7% for other
modes of transport) which depicts satisfactory performance of the model.
The BLRM equation is as follows:
U=0.098 HHS + 0.461 TV - 0.164 WT + 0.001 egress – 0.243
Table 5.1(c): Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a
Household_Size .098 .115 .731 1 .393 1.103
Total_Vehicles .461 .210 4.800 1 .028 1.585
Waiting_Time -.164 .049 11.313 1 .001 .849
Load_Factor .000 .003 .002 1 .961 1.000
Ingress .000 .000 .189 1 .664 1.000
Egress .001 .001 .994 1 .319 1.001
Constant -.243 .885 .075 1 .784 .784
The Wald statistic and associated probabilities provide an index of the significance
of each predictor in the equation. The simplest way to assess Wald is to take the
70
significance values and if less than 0.05 then reject the null hypothesis as the
variable does make a significant contribution. In this case, Wald criterion
demonstrated that total vehicle and waiting time made a significant contribution to
prediction. Household size, Load Factor, egress and ingress are not significant
predictor.
MODEL-2 , BLRM MIG
A logistic regression analysis is conducted to predict the choice behaviour for city
transit service using household size, Load Factor, waiting time, egress and ingress as
predictors for Middle Income Group. The output of the logistic regression as
obtained from SPSS software is shown and discussed below.
Table 5.2(a): Hosmer and Lemeshow goodness of fit Test
Step Chi-square df Sig.
1 8.842 8 .356
Our H-L statistic has a significance of 0.356 which means that it is not statistically
significant and therefore our model is quite a good fit.
Table 5.2(b): Classification Table
Observed
Predicted
Y Percentage Correct
0 1
Step 1 Y
0 10 80 11.1
1 6 187 96.9
Overall Percentage 69.6
Prediction success overall is 69.6 % (96.9% for public transport and 11.1 % for other
modes of transport) which depicts moderate performance of the model.
71
The BLRM equation is as follows:
U= 0.105 HHS – 0.93WT – 0.002LF + 0.001Egress + 1.495
Table 5.2(c): Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a
Household_Size .105 .098 1.141 1 .286 1.111
Waiting_Time -.093 .029 10.474 1 .001 .911
Load_Factor -.002 .003 .626 1 .429 .998
Ingress .000 .000 .042 1 .837 1.000
Egress .001 .000 2.093 1 .148 1.001
Constant 1.495 .610 6.012 1 .014 4.460
Here, from table 5.2(c), it can be inferred from the Wald statistic values that the
waiting time made a significant contribution to prediction.
MODEL-3 , BLRM LIG
A logistic regression analysis is conducted to predict the choice behaviour for city
transit service using household size, Total vehicles, Waiting Time, Load Factor,
Egress and Ingress. The output of the logistic regression as obtained from SPSS
software is shown and discussed below.
Table 5.3(a): Hosmer and Lemeshow goodness of fit Test
Step Chi-square df Sig.
1 10.788 8 .214
Our H-L statistic has a significance of 0.214 which means that it is not statistically
significant and therefore our model is quite a good fit.
72
Table 5.3(b): Classification Table
Observed
Predicted
Y Percentage Correct
0 1
Step 1 Y
0 5 13 27.8
1 4 56 93.3
Overall Percentage 78.2
Prediction success overall is 78.2% (93.3% for public transport and 27.8% for other
modes of transport) which depicts moderate performance of the model
The BLRM equation is as follows:
U= 0.425 HHS – 1.307 TV + 0.36 LF – 0.145 WT + 0.003 egress - 2.217
Table 5.3(c): Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a
Household size .425 .331 1.642 1 .200 1.529
Total vehicles -1.307 .570 5.258 1 .022 .270
Load factor .036 .020 3.395 1 .065 1.037
Waiting time -.145 .072 4.028 1 .045 .865
ingress .000 .001 .248 1 .618 1.000
egress .003 .001 6.038 1 .014 1.003
Constant -2.217 2.466 .808 1 .369 .109
Here from the table 5.3(c) it can be inferred from the Wald statistics that waiting
time, total vehicles and egress made a significant contribution to prediction. Ingress,
Load Factor and household size are not significant predictor.
MODEL -4, BLRM 500
A logistic regression analysis is conducted to predict the choice behaviour for city
transit service using Total vehicles, Waiting Time, HHI, Egress and Ingress. The
73
output of the logistic regression as obtained from SPSS software is shown and
discussed below.
Table 5.4(a): Hosmer and Lemeshow goodness of fit Test
Step Chi-square df Sig.
1 9.668 8 .289
Our H-L statistic has a significance of 0.289 which means that it is not statistically
significant and therefore our model is quite a good fit.
Table 5.4(b): Classification Table
Observed
Predicted
y Percentage Correct
0 1
Step 1 y
0 73 112 39.5
1 37 278 88.3
Overall Percentage 70.2
Rather than using a goodness-of-fit statistic, we often want to look at the proportion
of cases we have managed to classify correctly. Prediction success overall is 70.2%
(88.3% for public transport and 39.5% for other modes of transport) which depicts
moderate performance of the model.
The BLRM equation is as follows:
U= - 0.107 WT – 0.01 egress – 2.46 TV + 2.199
74
Table 5.4(c): Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a
HHI .000 .000 20.372 1 .000 1.000
Waiting Time -.107 .022 23.430 1 .000 .898
Ingress .000 .000 .333 1 .564 1.000
Egress .001 .000 3.630 1 .057 1.001
Total Vehicles .246 .130 3.560 1 .059 1.279
Constant 2.199 .312 49.666 1 .000 9.014
Here from the table 5.4(c) it can be inferred from the Wald statistics that waiting
time is a significant predictor.
MODEL -5, BLRM CENTRAL ZONE
A logistic regression analysis is conducted to predict the choice behaviour for city
transit service using household size, load factor, waiting time, egress, and ingress as
predictors with walking distance central zone. The output of the logistic regression
as obtained from SPSS software is shown and discussed below.
Table 5.5(a): Hosmer and Lemeshow goodness of fit Test
Step Chi-square df Sig.
1 7.801 8 .453
Our H-L statistic has a significance of 0.453 which means that it is not statistically
significant and therefore our model is quite a good fit
75
Table 5.5(b): Classification Table
Observed
Predicted
Y Percentage Correct
0 1
Step 1 Y
0 13 27 32.5
1 5 105 95.5
Overall Percentage 78.7
Prediction success overall is 78.7 % (95.5 % for public transport and 32.5 % for
other modes of transport) which depicts moderate performance of the model.
U= - 0.05(load factor) + 0.001(ingress) - 0.113 (waiting time) + 0.108 (HHS) +
0.010 (TV) + 0.002(egress) + 1.939
Table 5.5(c): Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a
Loadfactor -.005 .004 1.717 1 .190 .995
Ingress .001 .001 1.976 1 .160 1.001
Waiting time -.113 .041 7.456 1 .006 .893
Householdincome .000 .000 1.281 1 .258 1.000
Householdsize .108 .130 .685 1 .408 1.114
Totalvehicles .010 .328 .001 1 .975 1.010
Egress .002 .001 3.936 1 .047 1.002
Constant 1.939 1.022 3.595 1 .058 6.949
Here from the table 5.5(c) it can be inferred from the Wald statistics that waiting
time is a significant predictor.
MODEL -6, BLRM WEST ZONE
A logistic regression analysis is conducted to predict the choice behaviour for city
transit service using household size, load factor, waiting time, egress, and ingress as
76
predictors with walking distance central zone. The output of the logistic regression
as obtained from SPSS software is shown and discussed below.
Table 5.6(a): Hosmer and Lemeshow goodness of fit Test
Step Chi-square df Sig.
1 9.849 8 .276
Our H-L statistic has a significance of 0.276 which means that it is not statistically
significant and therefore our model is quite a good fit
Table 5.6(b): Classification Table
Observed Predicted
Y Percentage
Correct 0 1
Step
1
Y 0 67 71 48.6
1 35 152 81.3
Overall Percentage 67.4
Prediction success overall is 67.4 % (81.3 % for public transport and 48.6 % for
other modes of transport) which depicts moderate performance of the model.
The BLRM equation is as follows:
U= 0.084HHS + 0.273TV – 0.085 WT + 1.564
Table 5.6(c): Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a Household Size .084 .083 1.024 1 .312 1.088
Total Vehicles .273 .136 4.032 1 .045 1.314
Waiting Time -.085 .026 10.861 1 .001 .918
Household Income .000 .000 19.541 1 .000 1.000
Constant 1.564 .499 9.817 1 .002 4.778
Here from the table 5.6(c) it can be inferred from the Wald statistics that waiting
time is a significant predictor
77
CHAPTER 6: SENSITIVITY ANALYSIS
Sensitivity analysis is the study of how the uncertainty in the output of a
mathematical or system can be appoint to different sources of uncertainty in its
input. A related practice is uncertainty analysis which has a greater focus on
uncertainty quantification and propagation of uncertainty. Ideally uncertainty and
Sensitivityanalysis should be run in tandem.
Waiting Time – PT Shares (HIG)
Figure 6.1: Sensitivity of Waiting Time on Public Transportation for HIG
Most of the time waiting time is one of the factors which govern the mode of the
trips. It is a general assumption that people prefer the mode with lesser traveling
time. Above chart depicts the effect of waiting time on the modal shares of public
transportation by applying variation in waiting time. Observation from the study
states that 84 percent of the people tendto choose public transport if waiting time
gets reduced by 30 percent from the existing value of 12 minutes. If waiting time
222429
45
64
7884
0
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30
40
50
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PT
shar
es
% variation of WT
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increases than modal share for public transportation drops down to 22 percent from
45 percent.
Waiting Time – PT Shares (MIG)
Figure 6.2: Sensitivity of Waiting Time on Public Transportation for MIG
Above chart depicts the effect of waiting time on the modal shares of public
transportation by applying variation in waiting time. Observation from the study
states that 100 percent of the people tendto choose public transport which is
unrealistic if waiting time gets reduced by 30 percent from the existing value of 8.5
minutes. If waiting time increases than modal share for public transportation drops
down to 65 percent from 68 percent.
65676868
100100100
0
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50
60
70
80
90
100
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Pt
Shar
es
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Waiting Time – PT Shares (LIG)
Figure 6.3: Sensitivity of Waiting Time on Public Transportation for LIG
Above chart depicts the effect of waiting time on the modal shares of public
transportation by applying variation in waiting time. Observation from the study
states that 97 percent of the people are tends chose public transport if waiting time
gets reduced by 30 percent from the existing value of 8.0 minutes. If waiting time
increases than modal share for public transportation drops down to 67 percent from
77 percent. LIG people are observed to be inelastic in nature with changes in waiting
time as far as transit choice is concerned.
677272
77
949597
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80
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100
-40 -30 -20 -10 0 10 20 30 40
Pt
Shar
es
% Variation of WT
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CHAPTER 7: ACCESSIBILITY STUDY
Accessibility is a term often used in urban, rural, transport planning and land use
studies. The main aim of the study is to understand, appreciate and survey a range of
measures of accessibility along with its applications in context of urban planning.
Accessibility generally conveys the meaning of ‘ease of reaching’. Accessibility
indicates the location of households or a group of households in relation to the
distribution of activities and the transport system connecting them.
Accessibility measures can be determined from alternative combinations of two
alternative combinations of two items for cycle traffic.
Accessibility of a place: Location (district, zone, household location)
Accessibility by a mode:Mode (walk, cycle, scooter, car, bus, chartered bus)
Accessibility during time period:Time (peak hour, off peak, average daily both
weekday –weekend)
Feeder system in Ahmedabad city:
MYBYK is a cycle feeder system initiated at Ahmedabad for the easy accessibility
of BRT service. It helps make use of public transport more accessible, convenient,
and economic and time efficient.
Purpose:
It aims to save the commuters out of pocket cost when they use Single Occupant
Vehicle (SOV) to reach the BRT junction or their destination from BRT
junction.
It serves as an alternative to motorized transport or private vehicles, thereby
reducing traffic congestion, noise and air pollution.
81
Figure 7.1: Functioning of Cycle Feeder System
82
Figure 7.2: Typical Parking facility at MYBYK Cycle Feeder Junction
Figure 7.3: Stations of Cycle Feeder System
7.1 Questionnaire Survey Form
The designed questionnaire survey form comprises of three sections, viz. Household
and Socio-economic details, Travel Characteristics and Public Transport Particulars.
The typical questionnaire form is shown below.
83
Feeder Service Questionnaire Form
84
7.2 Observations from the Questionnaire survey
An extensive survey was carried out in the areas which have this feeder service viz.
Shivranjini, Andhjan Mandal, CTM, Sola and Memnagar. Out of the total 800 cycles
made available, only some of the cycles were used for the actual purpose of feeding
the BRT system. Whereas, most of the cycle were used for purpose like health
related issues. As a transport facility this system has failed to serve its purpose of a
feeder system.
85
CHAPTER 8: SUMMARY AND CONCLUSION
Modal choice is as such third stage process in urban transportation planning
package. Mode choice in fact is subjective decision of traveler associated with his
socio-economic, modal, trip and network characteristics. Logit modelling in this
regard is a popular conventional approach based on crisp inputs. BLR Transit
Choice Models therefore have been developed here for work as well as education
purpose trips employing Binary Logistic regression using SPSS with the help travel
data from residential and commercial zones in Ahmedabad city. The likely shift
pattern of different socio-economic sections of the society to the public transit
service is being analyzed here. There is wide scope of model applications in
formulation of transit planning strategies for fast developing metropolitan cities in
the country.
Socio-Economic and Travel Analysis
Three income classes viz. HIG (high income group), MIG (middle income
group) and LIG (low income group) are defined as per the criteria laid by
Gujarat Housing Board.
Over half of the collected samples fell into the category of MIG whereas a small
proportion of 12 % belongs to the LIG. The average household income in West
zone is observed as 43,197 INR whereas Central zone is having a HHI of 24,983
INR.
MIG is dominating in both the zones, but HIG concentration is quite higher than
LIG in west zone unlike in the case of central zone where both HIG and LIG
proportion is observed to be similar.
It can be seen that the proportion of working member is highest in LIG followed
86
by MIG and HIG. Reverse is the trend observed for education members.
It can be observed that the two-wheeler is having the maximum share with 56%
followed by four-wheelers which is quite high than in the Central zone.
The average trip length of the central zone is 4.65 km. It is observed that around
30 % of the person trips have trip length of less than 1 km.
If we bifurcate the trips by their purposes than 47% and 22 % of the total
population of a central zone are generating trips of less than 1 km trip length for
the education and work purposes respectively.
It is observed that only 9% of the person trips have trip length greater than 10
km. If we bifurcate the trips by their purposes than 7% and 10% of the total
population of the central zone are generating trips of more than 10 km trip length
for the education and work purposes respectively.
The average trip length of working member is observed to be 5.5 km whereas in
the case of educational member, it is 3.5 km.
Public Transport Parameters
The average value of RP and SP data of public transport parameters such as
Ingress and Egress distance, waiting time and load factor for BRTS and AMTS
in central zone and west zone.
On an average, existing and desired ingress distance for the central zone is 501
meters and 135 meters respectively. In west zone, the distances are 546 meters
and 263 meters respectively for the same.
Existing and desired value of the egress distance for BRTS and AMTS in central
zone is 457 meters and 140 meters respectively whereas in west zone it is 528
87
meters and 254 meters respectively.
Average existing and desired waiting time for BRTS and AMTS in central zone
is 11 minutes and 6 minutes respectively whereas in west zone, it is 12 minutes
and 6 minutes respectively.
The average load factor of central zone is 110 and of west zone is 99.
Regression Trip Generation Models
Regression based Trip Generation models for different income groups for two
zones of metropolitan city in India are developed and compared with respect to
the trip frequency and the influence of responsible parameters behind trip.
The developed models are observed to predict the trip rates with lesser co-
efficient of determination value and hence scope lies in its improvement.
Binary Logistic Transit Choice Models
Logistic Regression based Transit choice models have been developed for two
zones of Ahmedabad city for various sections of the society based on the RP
data pertaining to Public Transport parameters.
The likely shift to transit service with variations in accessibility parameters such
as waiting time, in-gress, e-gress, inside comfort, etc. can be predicted with the
help of these developed models.
These BLR Transit Choice Models find application in faming of sustainable
public transport facility and can guide transport planners and decision makers in
urban and regional level transportation planning.
88
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IEEE Projects 2016-2017Updated Top list of Cloud Computing for ME/MTech,BE/BTech Final Year students
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