Development of a Transferable Car Travel Demand Model: A Case Study of Nairobi, Kenya and Dar-es-Salaam, Tanzania

Andrew Bwambale | Dr. Charisma Choudhury (Supervisor) | Dr. Nobuhiro Sanko (2nd Reader)

1. Motivation

2. Objectives

To investigate the hypothesis that

households make joint car ownership and

trip generation decisions;

To evaluate the local performance of

alternative modelling frameworks;

To investigate the effectiveness of directly

transferred models; and

To evaluate the impact of updating

procedures on model transferability.

3. Case Study Areas (1) 5. Modelling Framework

6. Methodology

Spatial Transferability of household car

ownership and Trip Generation models

using data from JICA household surveys -

Nairobi (2006) and Dar-es-Salaam (2008).

General Model Types: Univariate and

Bivariate Ordered Response Models

Four Scenarios:

M1 = A car trip generation model without

the car ownership variable

M2 = A car trip generation model with the

car ownership variable

M3 = A car ownership sub model for in-put

into the trip generation model

M4 = A joint car ownership and car trip

generation model

Focus will be on spatial transferability of household car ownership and Trip Generation models

using data from JICA household surveys - Nairobi (2006) and Dar-es-Salaam (2008). Four

alternative structures to be tested in pursuit of the most appropriate modelling framework.

5. Insights from Data

M1 M2 M4 M3

Comparison of

model performance

Incorporation of

aggregate variables

M1’ M2’ M4’ M3’

Comparison of

model performance

Transferability tests

and updating

Transferability tests

and updating

Evaluation

Final model

Comparison of model

performance

Comparison of model

performance

Transferability

tests and

updating

Final

Comparison

and model

selection

NAIROBI

Transferability

tests and

updating

M1D

Comparison of model

performance

Comparison of model

performance

DAR – ES - SALAAM

Incorporation of

aggregate variables

M1N/ M1D: A car trip generation

model without the car

ownership variable to test

whether the need for car

ownership data can be avoided

M2N/ M2D: A car trip generation

model with the car ownership

variable to test the

significance of car ownership

on trip generation

M3N/M3D A car ownership sub model pre-

estimating car ownership for input into the

car trip generation model to test

performance in circumstances of

unavailable/ unreliable car ownership data

M4N/ M4D: A Joint car

ownership and trip generation

model addressing suspected

endogeneity between them

4. Case Study Areas (2)

NAIROBI

DAR-ES-SALAAM

M2D M3D M4D

M1’D M2’D M3’D M3’D

M1N M2N M3N M4N

Incorporation of

aggregate variables

M1’N M2’N M3’N M3’N

1.1%3.9%

9.0%

24.7%

31.2%

42.6%40.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

31.8

74.2

148.5

318.1

530.2

742.3

848.3

Average Household Income (USD)

Car Holding Rate by HH Income

1.0% 1.0% 2.7% 5.2%

14.3%

24.0%

45.3%

65.3%

90.6%

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

34.0

102.0

170.1

238.1

340.1

476.2

612.2

1,0

20.4

1,3

60.5

Average Household Income (USD)

Car Holding Rate by HH Income

Budget constraints have resulted in

model estimation data shortage in

developing countries.

A compromise solution could be provided

by transferable models. Focus to be

placed on transferability of car ownership

and car trip generation models (Since

private cars are the main source of

congestion).

However, unreliable car ownership

information might limit transferability of

traditional trip generation models

containing a car ownership variable.

Previous studies have not tackled the

possibility of using cross-sectional data

to develop a joint car ownership / trip

generation model based on exogenous

variables to avoid this problem

Perceptions of transport network flood-risk vulnerability Are we prepared enough?

Aims

• Identify vulnerabilities of the transport network

within selected ‘flood-risk’ areas

• Establish perceptions of transport network

vulnerabilities in case study areas

• Ascertain opportunities for improvements to

existing strategies in the event of a flooding

emergency

Expected Findings

• Identification of specific training needs in

flood-risk areas, directly related to the

identification of vulnerabilities in case study

areas

• Improvement of emergency strategies for

vulnerable groups focusing on accessibility

in flood-risk areas

• Development of a framework for

vulnerable area identification and

accessibility improvements for policy

perceptions of vulnerability

Methodology

• Undertake a comprehensive analysis of existing literature and

preparation techniques for emergency response and vulnerability

identification

• Audit emergency response information - including training

programs and response strategies to identify perceptions of

vulnerability in case study areas

• Analyse accessibility issues in relation to network limitations for

vulnerable groups (e.g. disabled, homeless, isolated elderly

people and children) and methods to improve existing strategies

“more people will be at risk of coastal flooding

each year” UK CEN (2014)

Amy Friel MSc. Transport Planning FT Supervisor: Frances Hodgson

Source: Environment Agency (2014)

Background

Over recent years, the rainfall and disaster situations in the UK as a result of

flooding have been steadily increasing. As this risk increases, it is necessary

to ensure sufficient transport sector emergency preparation for vulnerable

areas and vulnerable groups.

In the UK:

• 5, 000, 000 people in direct risk of flooding

• 1 in 6 ‘flood-risk’ properties

• ~86% probability of identified areas flooding again in the next 70 years

• >300,000 homes without power (Winter 2014)

• Sea-levels rose to just 40cm below the Hull Tidal Surge Barrier limit

(December 2013)

0

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15

20

25

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Nu

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rop

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Lost

(a

pp

rox)

Tho

usa

nd

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Sea-Level Increase (metres AOD)

Sea-Levels in the Humber Estuary: Potential Property Loss

Key References Golpalakrishnan, C. 2013. Water and disasters: a review and analysis. International Jourlan of Water Resources Development. 29(2), pp. 250-271 Berdica, K. 2002. An introduction to road vulnerability: what has been done, is done and should be done. Transport Policy. 9. pp. 117-127 Environment Agency. 2010. River Hull Flood Risk Management Strategy Report. Halcrow Group Limited.

Understanding  Choice  of  Departure  Airport  and  its  Rela7on  to  Surface  Access:  A  Case  Study  of  Manchester  and  Leeds  Bradford  Airports    

 •  To  discover  the  generally  accepted  distance  at  which  

a  passenger  is  not  prepared  to  travel  beyond  to  access  a  direct  flight  and  how  this  varies  with  different  journey  and  passenger  types  

•  To  assess  the  role  of  surface  access  in  passengers  willingness  to  travel  to  a  regional  airport  of  further  distance  from  their  home  airport,  to  access  a  direct  flight    

•  Upon  the  results  of  the  two  above  objec=ves,  would  there  be  a  case  for  the  two  airports  to  work  collabora=vely.    

Research  Ques=ons:  Background    Builds  upon  the  work  of  Johnson,  Hess  and  MaGhews  (2014).    Their  study  assessed  whether  a  passenger  would  be  more  inclined  to  take  a  direct  flight  from  an  alterna=ve  airport  rather  than  an  in-­‐direct  flight  from  their  home  airport.    Concluded  that  irrespec=ve  of  improved  surface  access,  there  was  a  strong  aversion  to  in-­‐direct  flights  and  the  passenger  would  s=ll  choose  the  alterna=ve  regional  airport.    They  would  s=ll  choose  the  alterna=ve  airport  if  the  airfare  were  to  be  increased  and  the  access  =me  longer  than  their  home  airport.      

This  study  will  aGempt  to  quan=fy  the  point  at  which  passengers  no  longer  find  the  promise  of  a  direct  flight  enough  to  warrant  increased  access  =me  and  cost.    It  will  then  seek  to  assess  how  improved  surface  access  to  the  airports  region  wide,  would  affect  passengers  propensity  to  travel  to  an  alterna=ve  airport  to  access  a  direct  flight.  Would  try  to  assess  the  case  for  strategic  coopera=on  of  the  two  airports.      

Scope  of  the  Study  

Why  Manchester  and  Leeds?    In  2010,  20.2%  of  Manchester  Airports  patronage  originated  from  or  des=na=ons  were  in  the  Yorkshire  and  Humber  area,  rising  from  19.2%  in  2009.    Yorkshire  and  the  Humber  has  not  only  Leeds  Bradford  Airport,  but  Doncaster  and  Humberside  too.    This  would  suggest  that  the  people  of  Yorkshire  and  Humber  are  prepared  to  travel  a  significant  distance  to  access  the  wider  range  of  direct  flights  that  Manchester  Airport  has  to  offer.  First  Trans-­‐Pennine  express  provide  services  to  Manchester  Airport  from  across  Yorkshire  to  the  airport.  Significant?    

Key  References    Civil  Avia=on  Authority.  2010.  Passenger  Survey  Report  2010.  London,  Civil  Avia=on  Authority.        Civil  Avia=on  Authority.  2009.  Passenger  Survey  Report  2009.  London,  Civil  Avia=on  Authority.        Johnson,  D.  Hess,  S.  MaGhews,  B.  2014.  Understanding  Air  Travellers’  Trade-­‐offs  Between  Connec=ng  Flights  and  Surface  Access.      

Anna  Goldie,  MSc  Transport  Planning  FT  Supervisor:  Bryan  MaGhews    

Methodology      Will  follow  stated  preference  survey  techniques  and  mul=nomial  logit  models  to  assess  passengers  paGern  of  trades  offs    Iden=fica=on  of  a  range  of  important  aGributes  in  the  decision  making  process  such  as:  Air  Service  type  –  Full  or  Low  cost    Flight  Type  –  Direct  or  Indirect    Access  Time    Access  Mode/s    Reliability  of  Access  Modes    Price  of  Access  Modes        Approach  Airports  and  Access  providers  such  as    Trans-­‐Pennine  Express  (MAN)  and  Arrow  Cars  (LBA)    Secure  access  to  passengers  to  survey  –  failing  this  explore  online  surveying  techniques              

What  Next?  

CONTROLLING REFLECTIVE CRACKING IN ASPHALT OVERLAYS A Pavement Deterioration Study

By: Ahmad Huneidi Supervisor: Mr. David Rockliff

1. Background • Asphalt is a mixture of cement, water and

aggregate. It can be used as a surface binder

course on the top layer of the pavement.

• Pavement layers from bottom to the top layer are

sub-grade, capping layer (optional), sub-base,

main base, binder course and surface course.

• Asphalt cracking is a form of pavement

deterioration which is mainly caused by water

entering the pavement infrastructure. Other

reasons can be due to weathering conditions,

traffic loadings and lack of maintenance.

2. Objectives • To control reflective cracking in asphalt and to

choose the best cost/effective treatments available.

• Treatments to be chosen depending on the

deterioration stage. Crack sealing, surface

dressing, patching, inlaying and crack injection are

suitable solutions.

• To identify the causes of the cracking, i.e. water,

thermal or traffic related.

• Estimating the best time to intervene to maintain

the pavement is based on engineering judgment.

3. Comparisons and Limitations • A huge part of the dissertation will focus on comparing asphalt cracking and pavement deterioration

between the UK and Kuwait. Specifically to compare deterioration patterns caused by weathering

conditions, as in Kuwait the temperature can go up to 50 oC during the day, and may drop 10 degrees and

more during nightfall, where in the UK many types of weather can be experienced in a single day. Also,

maintenance procedures in terms of asset management comparisons between Kuwait and the UK, where

in Kuwait funding and budget is highly available and in the UK highway maintenance budget was cut in the

past few years.

• The dissertation will also argue the limitations set on highway maintenance, such as budget, as it’s

recommended to intervene to fix the pavement, however sometimes it’s better not to intervene to save

money.

5. Research Outcomes • Preventing asphalt cracking and on-time

intervention.

• Introduce a cost/effective pavement maintenance

procedures

• Heavily deteriorated pavements may lead to car

damage and pedestrian/cycling accidents, e.g.

potholes.

• Identify the conditions of the highway infrastructure

in Kuwait and compare it to the UK.

4. Research Methodology • The research will start off defining the asphalt

mixture and its’ mechanism.

• Functions of a pavement, layers and most

common binding courses used.

• Detailed definition of the main question.

• Designing appropriate thickness with good quality

materials to increase pavements’ life-expectancy.

• Identify asphalt cracking reasons in Kuwait and in

the UK with comparisons.

• Identify pavement maintenance procedures in

Kuwait and in the UK with comparisons.

• Look into highway maintenance and infrastructure

budget available and in terms of asset

management.

6. References • J.M. Rigo, R. Degeimbre, L. Francken (2010) Reflective Cracking in

Pavements: State of the Art and Design Recommendations. Oxford,

UK.

• L. Francken, E. Beuving, A. Molenaar (2004) Reflective Cracking in

Pavements: Design and Performance of Overlay Systems. London,

UK.

• T. Harvey (1995) Structural Design of Asphalt Concrete Pavements

to Prevent Fatigue Cracking. California, USA.

• J. Baek (2010) Modelling Reflective Cracking Development in HMA

Overlays. Illinois, USA.

• Button & Lytton (2006) Guidelines – Synthetics in HMA Overlays.

• Online references: Tensar International, Maccaferri, Adept & Institute

of Asphalt Technology.

• References from Kuwait: Arab Planning Institute, Department of

Transport, and Ministry of Public Works.

Source :http://www.transportumum.com/jakarta and https://www.google.co.uk/maps/place/Jakarta

Commuter Line

TransJakarta(BRT)

*Household Travel Survey (HTS), Commuter Travel Survey (CTS) Source : Nobel, et.al 2013

Jakarta Tangerang

city Bekasi

Depok City and

Bogor City

(2002) 262 (2010) 423 ↑1.6 times

(2002) 247 (2010) 344 ↑1.4 times

(2002) 234 (2010) 338 ↑1.4 times

(unit) in 1.000

Graph modified by researcher based on Preliminary figures of JUTPI Commuter Survey

In Total (2002) 743 (2010) 1105 ↑1.5 times

Congestion

Jakarta loss IDR 12,8 Trillion in material aspect such as time per year

(finance.detik.com,2013)

Stress

Relation between congestion and driver stress has found in high congestion (Wickens and Wiesenthal, 2005)

Pollution

Jakarta’s people loss IDR 35 Trillion per year in health issues caused by pollutions (jurnas.com,2014)

Transport Issues in Jakarta

Trip from Outside Jakarta Per Day

Strength of Car dependency in Jakarta

Relationship Between Social Status and Car Use

Determine derived issues such as instrumental and emotional issue

Recommendation for transport policy in Jakarta

Behaviour , Habit and Intention relation

Based profile segmentation to see how strong car dependency is.

• Gardner, 2009

• Brujin et.al, 2009

Changes of People Behaviour

Based on possibilities to attract people to change their behaviour

• Stradling et.al, 2000

Intention

Attitude

(behaviour, intention and

habit)

Perceive Behaviour

Control

Subjective Norm

Data collection

Using online questionnaire

(purposive and snowballing

sampling)

Data Cleansing

Validity and Reliability Check

Grouping the factors

Based on TPB analysis to look at driver

motivation

Anabel, 2005

Factor analysis

Infrastructure Improvement

In Public Transport, Traffic Management or supporting facilities

Behaviour Approach

Using ‘Push’ or ‘Pull’ approach (Stardling et.al 2000) or Smarter

Choices system (Cairns et.al,2008)

Driver Motivation

Statistical Analysis

Recommendation for Jakarta’s Transportation

Behaviour

Methodology

Objective

Background

Jakarta Profile • Area 664,01 Km²* • Population 9,604,329* • Household 2,508,869* • Total road length is 7,650 6.2% of total

area of the city • 17.1 million trips/day • (Source : http://www.kemendagri.go.id,

BPS, UI and APRU 2010)

“Is social status become a reason behind car use in Jakarta? ”

Research Question

• Steg, 2005, indicate that car use can be a variable to show status symbol in a group.

• Hiscock et.al, 2002 identify that prestige is one of factors that influence car use in Scotland.

• Shove 1998, Sheller & Urry, 2000 and Dant & Martin, 2001 mention that car gives values added to their owner on social status.

Theory Planned Behaviour (TPB) is adapted from Ajzen, 1991 use to finding the sets of behavioural of human being, it will measuring people attitude (behaviour, intention and habit (Gardner, 2009)), normative and control belief. Data collection will using online questionnaire in Indonesian Language. It will distributes to people who is commuting inner and to Jakarta.

Grouping the data from questionnaire based on factors analysis refers to Anabel, 2005 research, and do data cleansing by checking validity and reliability with cross tab analysis. Thus, this analysis will use SPSS to look at relationship strength between variables and finding the most affecting factor.

‘Push’ and ‘Pull’ system are psychological approach to change people behaviour by setting the policy adapted from Stradling et.al, 2000. And to strengthen the policy, smarter choice can become other option to reduce cost value. For infrastructure development will address to government budget and regulation as their function to provide better facilities for citizens.

Potential Risk • Data validation unfitting with the objective and misunderstanding perception with researcher intent. And its

potentially privacy question that might annoys respondent (Frederick, 2008) • Respondent resistant to answer with honest because it interfere their status symbolic (Steg ,2005) • Limitation of this research particularly find the relationship between car users in Jakarta with social status.

Jakarta Maps

Picture Source : google images for congestion in Jakarta

Researcher : Ayu Kharizsa (ml12a28k@leeds.ac.uk), MSc, Transport Planning Supervisor : Dr. Ann Jopson (A.F.Jopson@its.leeds.ac.uk) Second Reader : Frances Hodgson (F.C.Hodgson@its.leeds.ac.uk)

Mode shares by Purpose

Source : Ajzen, 1991

Anastasios Leotsarakos – MSc (ENG) Transport Planning and Engineering Supervisor: Dr. Haibo Chen University of Leeds - May 2014

OBJECTIVES

The aim of the project is to:

Identify the main accident characteristics responsible for the formation of queues.

Create a model that quantifies the effect of these characteristics.

Predict the potential of a queue to be formatted and its characteristics (maximum length and duration), when an accident occurs.

METHODOLOGY

CASE STUDY

The project investigates the case of Attiki Odos, a motorway in Athens, the capital of Greece, functioning as the Athens Ring Road, providing connection with the Athens International airport, passing through urban and rural areas.

The total length of the motorway is 65 km while there are 3 lanes plus an emergency lane in each direction.

In the median zone of the motorway runs the suburban railway.

DATA

Accident and traffic data from Attiki Odos motorway from 2007-2010.

Total number of accidents: 3,321.

Number of accidents actually used: 1,442.

Traffic data from loop detectors in 0.5 km intervals and 5 min frequency.

A total of 32 variables.

VARIABLES

1 Type of day 17 Speed (km/hr)

2 Accident duration 18 Lane Volume (pcus/hr)

3 Accident type 19 Queue max length (km)

4 Collision type 20 Queue duration (min)

5 Fatalities 21 Rainfall

6 Injuries 22 Alignment

7 Number of Lanes 23 Geometry downstream

8 Left Lane 24 Geometry upstream

9 Middle Lane 25 Tunnel down

10 Right Lane 26 Interchange down

11 Emergency Lane 27 Toll down

12 Lane type 28 More than one down

13 Number of Vehicles 29 Tunnel up

14 PC 30 Interchange up

15 PTW 31 Toll up

16 TRUCK 32 More than one up

BACKGROUND 50% of delays in motorways are non-recurrent (incident produced)

When an accident occurs the road capacity can be reduced: a shock-wave of slow-downs is created that, propagates downstream and can result in the formation of a ‘platoon’ or queue behind.

A very important factor in the development of accident management strategies is to identify and quantify the conditions affecting the nonrecurrent congestion caused by accidents once they have occurred.

Identify Shockwaves

Identify Queues in Shockwaves

(Length and Duration)

Create a Model that Calculates Queues:

f(Qlength) = ...

f(Qduration) = ...

Attiki Odos, Airport Interchange Schematic shockwave caused by traffic accident

• Hills and activity related issues recognised as key issue by

Gatersleben & Appleton:(2007) in their cycle to work study in a hilly area of Surrey.

• Figure 2 displays what they found to be key factors leading to a bad cycling experience.

24%

19% 13%

8%

36%

Figure 2: Factors relating to bad cycling experiences

Bad Weather/Darkness

Activity related issues: hills & feeling tired

Traffic issues

Mechanical

Other

Source: Gatersleben & Appleton (2007)

What are electric bikes? Electric bicycles (also known as Pedelecs and e-bikes) are bicycles which offer the rider electrical assistance when pedalling. This comes from a battery power source.

Expected findings: - Technologically e-bikes are now a viable form of transport. - Lack of awareness of the benefits from the public and policymakers which is limiting the uptake

of e-bikes amongst most groups. - The increased Cost of an e-bike is a key barrier to uptake, particularly for lower-income groups

and those new to cycling. Industry bodies recommend >£1000 for a quality model. - Concerns which prevent people using conventional bikes will still form a barrier. These include

road safety and weather (Rose 2013).

Background

Methodology & Data collection 1. Qualitative interviews with e-bike retailers, manufacturers and industry experts. The findings will

feed into and complement the questionnaire survey. 2. Questionnaire survey of existing e-bike users and those who do not currently cycle. This will

assess the impact of the technology on travel habits of existing owners and the attitudes of non-owners .

3. Analyse & Triangulate both qualitative and quantitative data to gain significance and depth of understanding.

Key references consulted: Gordon, E., Xing, Y., Wang, Y., Handy, S., & Sperling, D. (2012). Can Electric 2-wheelers Play a Substantial Role in Reducing C02 Emissions?. Institute of Transportation Studies, University of California, Davis. Rose, G. (2012). E-bikes and urban transportation: emerging issues and unresolved questions. Transportation, 39(1), 81-96. Dill, J., & Rose, G. (2012). E-bikes and transportation policy: Insights from early adopters. Transportation Research Record: Journal of the Transportation Research Board, (2314), 1-6 Gatersleben, B., & Appleton, K. M. (2007). Contemplating cycling to work: Attitudes and perceptions in different stages of change. Transportation Research Part A: Policy and Practice, 41(4), 302-312.

How much of a barrier are hills and intense physical activity?

Where is the potential for e-bikes? It has been acknowledged that e-bikes can encourage cycling amongst:

• The Elderly • Physically disadvantaged • Cyclists in hot, hilly or windy areas • Those wishing to avoid the need to

change clothes (see Rose 2012 & Gorden et. al. 2012).

Source: COLIBI 2013

Electric bike sales Sales have been strongest in China with Germany and the Netherlands jointly making up 65% of the European market in 2012.

Research questions arising

1. Can e-bikes encourage more cycling trips in hilly areas and amongst those less able in the UK? 2. What are the barriers to e-bike ownership, given the slow take-up in the UK?

Are electric bikes a solution to hills? A UK perspective.

A hub-mounted electric motor

A frame-mounted electric motor

Folding electric bicycle

Student: Alexander Lister Course: MSc. Transport Planning Dissertation Supervisor: Frances Hodgson

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

2010 2011 2012

E-b

ike

sal

es

Year

E-bike sales 2010-2012

Great Britain

Germany

Netherlands

45%

20%

5%

5%

4%

21%

Europe 2012 e-bike sales share

Germany

The Netherlands

France

Italy

Great Britain

Other(s)

Monica Corso - ts10mec@leeds.ac.uk

MSc (Eng) Transport Planning and Engineering

Supervisor: Daniel Johnson May 2012

Bing Li – MSc Transport Planning and Engineering Supervisor: Dr. James Tate

E-mail: ml12b2l@ leeds.ac.uk Institute for Transport Studies University of Leeds 05 - 2014

BACKGROUND

OBJECTIVES

CASE STUDY

➢ The main aim of this project is to better understand the impacts of traffic congestion on fuel consumption and vehicle emissions performance in the study area – Headingley.

➢ Two key objectives:

Actual Tracked Vehicles Data in Headlingley from RETEMM Project (Speed, Acceleration, Gradient)

One Vehicle with PEMS

Model Validation and Data Obtaining – Real and Predict Emissions & Fuel Consumption

VEHICLE EMISSIONS FUEL CONSUMPTION

➢ Road transport is the main source of air pollution in urban areas.

➢ Speed profiles will help to run emission model.

➢ Speed profiles are obtained using a GPS data logger and the data is historic collected.

➢ Road gradient is considered which will be used in PHEM through vehicle specific power (VSP) formula and to make model more accurate. ➢ Analysing the relationship between congestion and vehicle emissions, and how driving behaviours in traffic congestion affect emissions

➢ Fuel consumption is greatly influenced by road gradient because it is related to different engine load.

➢ Fuel consumption usually increases under congestion and the changing of driving behaviours makes contributions to the increasing part.

◎ Using actual tracked data to study how driving behaviours and vehicle movements adapt to congestion and the effect on tail-pipe emissions.

◎ Analysing how driving behaviours and vehicle movements influence fuel consumption under congestion.

➢ Health Effect: Public Health England(PHE) said 5.3 per cent of all deaths in over-25s were linked to air pollution, which is more than road accidents.

➢ Emission Trends: The emissions of petro vehicles (e.g. NOX) decreases from Euro0 to Euro5, however, it is almost unchanged for diesel vehicles and increases from Euro3.

*Real-world Traffic Emissions Monitoring and Modelling (RETEMM). EPSRC January 2008, Grant Reference: GR/S31136/01

Five vehicles with GPS data

Analysis and Comparison

Emission Model – PHEM Real Observations (CO2)

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•Review of relevant literature

• formulation of questions for interviews

Methodology

Step one

•Interview of policy makers and Non Governmental Organizations (NGOs)

• Interview analysis.

Methodology

Step two

•An appraisal of the factors that have influenced the focus on CO2 reduction in transport in the UK;

•Exposition on the consequences of such focus.

Expected Findings

Benedictus Dotu Nyan ID: 200819480 Sustainability (Transport) Supervisor Antonio Ferreira (Dr) Caroline Mullen (Dr) Second Reader TRAN5911M

Background- emergence of focus on CO2 reduction in the UK

• Stern Review (2006) urged transition to a low carbon economy.

• UK Climate Change Act (2008) :-

transport is a major source of CO2 and other Greenhouse gases (DfT, 2012).

• Carbon Plan (2011) :- move toward

achieving an 80% reduction in and CO2 other Greenhouse gases (DfT, 2012)

• (DECC, 2014) CO2 emissions from the

transport sector in the UK in 2013 accounted for ¼ of all domestic CO2

emissions

Objectives Identify factors that have

influenced the focus on CO2

reduction in the UK.

Examine the pros and cons of

focusing on CO2 reduction in the

UK.

Research Questions What are the factors that have influenced the focus on CO2 reduction in the UK?

What are the pros and cons of

focusing on CO2 reduction in the UK?

Problem • Humanity has already transgressed the

climate change planetary boundary . • It is based on two critical thresholds- CO2

and radiative forcing. • Exceedence of 350 PPM of CO2 and 1 watt of

radiative forcing will result to irreversible climate change.; However,

• The biodiversity boundary has been transgressed;

• Change in land use has become problematic

(Rockstrom et al., 2009). • Ambient air pollution (PM2.5, PM10 NO2, SO2,

etc.) was responsible for 3.5 million deaths in 2012 (WHO, 2012);

Google Image

DEVELOPING TRIP GENERATION MODELS: COMBINING SURVEY AND MOBILE PHONE DATAAuthor: Christopher O. Edeimu

Supervisor: Dr. Charisma F. Choudhury

A trip is a one way journey and may be classified as home or non-home based. Trip rates are the rates at which trips are generated. Can be considered the rate socio-economic activities are loaded onto the transport network. They indicate the network capacity to plan and provide for. They are influenced by land use and socio-economic attributes of the population.

ABSTRACT

Their accuracy and reliability depend on the reliably of the data. The cost of data gathering and trip rates estimation make this a major challenge in most developing countries. However, mobile phone data are accurate and reliably generated and, properly harnessed, presents a low cost, alternative source of transport planning data.

Trip generation has been studied at the: • Aggregate level employing linear regression and categorical analysis. (Vickerman and Barmby, 1985). • Disaggregate level using discrete choice models. Regression method will be adopted in this study (Washington 2000), because they:

• Facilitate identification of variables that are correlated with trip origination. • Are useful for prediction and policy impact assessment

Neumann et al, (1983) directly estimated all-purpose trip production rates using traffic ground count to data. Obtained estimates within 96% of true rates. Caceres et al., (2007) inferred OD matrices from mobile phone data.

OBJECTIVES

Contribute towards developing trip generation models that countries with limited resources to undertake household surveys can use to reliably estimate trip rates. Specifically: • Develop regression-based trip generation models combining mobile phone CDR and socio-economic

variables. • Improve reliability of trip rate estimation. • Reduce data requirements for trip rates estimation. • Reduce trip rates modelling cost.

To examine the feasibility of combining mobile phone CDR and socio-economic data in trip rates estimation. Two questions to be investigated. 1. Will trip rates derived using mobile phone data be statistically different from those derived using household

survey data? 2. If yes, what might the reason(s) be?

EXPECTED OUTCOMES LITERATURE REVIEW

REFFERENCES 1. Arabani M. and Amani B. 2007. Evaluating the Parameters Affecting Urban Trip-Generation. Iranian Journal of

Science & Technology, Vol. 31, No. B5, pp. 547-560. 2. Caceres et al. 2007. Deriving Origin–Destination Data from a Mobile Phone Network. IET Intelligent Transport

System Vol. 1, pp. 15–26 3. Neumann E. S. et al. 1983. Estimating Trip Rates from Traffic Counts. Journal of Transportation Engineering,

Vol. 109, No. 4, pp. 565-578. 4. Ortúzar, J. D. & Willumsen, L. G. 2001, Modelling Transport, Third Edition, Wiley, New York. 5. Vickerman, R. W., and Barmby T. A. 1985. Household Trip Generation Choice: Alternative Empirical

Approaches, Transportation Research B, vol. 19, no. 6, pp. 471-479. 6. Washington, S. 2000, "Iteratively Specified Tree-based Regression: Theory and Trip Generation Example",

Journal of Transportation Engineering-Asce, vol. 126, no. 6, pp. 482-491.

Traffic Assignment

Modal Split

Trip Distribution

Trip Generation

Trip Rates

Because they feed into every aspect of the transport planning process they should be properly and reliably estimated. Inaccuracies will be greatly magnified in inefficient and ineffective transport policies and system.

Area-wide, all-purpose linear regression estimates of trip generation rate for motorized journeys suitable for systems with limited resources and access to appropriate data.

• Area-wide, all-purpose, because they produce equally accurate results. (Coomer and Corradino, 1973). • The implicit assumption by conventional models of mutually independent trips may not properly reflect

behavioural reality. (Goulias et al. 1991).

Statistical Methodology for model estimation

Trip Generation Model

𝑡𝑘 = 𝛼 + �𝛽𝑖𝑋𝑖𝑘 + 𝜀 𝑛

𝑖=1

(𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑁𝑡. 𝑁𝑎, 1983)

1. Divide study area into units (TAZ). 2. Map mobile data to TAZ. 3. Infer residual traffic counts. 4. Regress residual traffic counts on socio-economic variables. 5. Results: all-purpose TAZ trip generation rate.

• 𝑡𝑘 = Area−wide all−purpose trip rate for TAZ k (dependent variable)

• 𝑋𝑖𝑘 = Matrix of socio−economic variables (explanatory variables)

• 𝛽𝑖= Vector of coefficients

Error Tests

Model Estimation

Model Validation

Model Calibration

Model Specification

Tested Trip rates to be relied

on for their purposes anywhere in the transport

planning phase.

Step Equation R2 Parameters

1 𝒗𝒌 = 𝜶 + 𝜷𝟏𝑿𝟏 + 𝜺 d% 𝑿𝟏

2 𝒗𝒌 = 𝜶 + 𝜷𝟏𝑿𝟏 + 𝜷𝟐𝑿𝟐 + 𝜺 e% 𝑿𝟐

. ... … … …

n 𝒗𝒌 = 𝜶 + 𝜷𝟏𝑿𝟏 + 𝜷𝟐𝑿𝟐+. . +𝜷𝒏𝑿𝒏 + 𝜺 f% 𝑿𝒏

• All parameter to have the required sign and order of magnitude.

• R2 will be significant in determining validity of the results. Statistically large samples sizes are critical to proving the significance of relationships.

• The expected sample of CDR data is over 900m.

• The Mohring Effect is the background for the reimbursement guidance of Concessionary Travel from the UK Department for Transport.

• In England, the Concessionary Travel has been introduced in 2006 for elderly people and disabled residents allowing free travel in off peak time. In 2009/2010 concessionary passengers on local bus represented the 30% of local bus trips.

•The principle for reimbursement to bus operators was “NBNWO” , “No better no worse off” than without the scheme: costs incurred by carrying extra passengers may be significantly different depending on behaviour of the operator facing the extra –demand. Operators may:

• Allow for higher load factor without any additional service

• Run larger vehicles

• Run additional service to the route, leading to the Mohring Effect.

The power relationship between frequency and demand is challenged by real world considerations, such as:

• Indivisibilities, desire to maintain “round numbers” for service level

• Load factor constraints, passenger constraints

• Predatory competition

Does the Mohring effect really exist? Student: Dario Nistri

Supervisor: Jeremy Toner - Second Supervisor: Antony Whiteing

Background

What is the Mohring effect?

Demand Q= 1 Opt. Number of bus =B*

• The Mohring effect is a form of economy of scale by user side in public transport services. For scheduled and urban public transport, an increase in frequency, produces economy of scale for users in term of time savings.

•Supposing a Welfare Maximising operator, Mohring (1972) states that whether it occurs an exogenous increase of travel demand, the bus service would increase with the square-root.

Mohring Square root

Opt. Number of bus =1.40B*

• Supplying the service with additional 40% bus units, travellers double

Meaning of the rule

Task 1

Task 2

Demand and cost estimation Max Load Factor

Crowding Threshold

Overload Departures

Does elasticity is =0.5? How much is different?

Square Root Method

Mohring Effect Size of Mohring Factor

Size of Mohring Factor Policy implications

Methodology

Objectives Expected results

References

However a second order demand of commercial travellers is generated beyond concessionary extra traffic.

•Investigation of operators behaviour when the increase of the demand occurs, attempting to identify the crowding threshold above that the operators upgrade the service increasing the frequency.

•Estimation of the effect of the agreement for full or partial reimbursement of Concessionary Travel on service level .

•Estimation of the size of Mohring Factor in the context of unregulated market populated by profit-maximising operators.

•At network level, calculation of costs and Load Factor change due to the introduction of Concessionary Fare, in case of full and partial reimbursement . Application of Abrantes and Last methodology to calculate Mohring Effect.

•Application of the Square Root at route level to calculate Mohring Factor, taking in account two routes one with the demand double of the other.

Hp 2

Hp 1

Abrantes & Last Method

Concess. pax

Generated Pax

Task 1

Task 2

•Abrantes and Last study established two crowding threshold that we consider such as milestones. So it is reasonable to expect that the threshold for the data taken in account would be between 85% and 100% of load factor.

•The condition “no better no worse off” , if not fully applied, may prevent the profit maximising operator to increase the service, allowing the load factor to increase.

•The size of Mohring Factor in the context of unregulated market populated by profit-maximising operators is expected to vary that estimated for the welfare maximising scenario.

•Nelltorph et al. (2010) proposed the value of 0.6 in welfare-maximising framework. Abrantes and Last (2011), studying the commercial decisions by operators in three English Metropolitan areas, calculated the values reported in the following table.

•Abrantes, P., & Last, A. (2011). Estimating additional capacity requirements due to free bus travel.

•Toner J.P.(2013). Mohring Effect: theory and Existing Evidence. Institute for Transport Studies.

B*= optimal n. of bus /hour

C= unit cost to produce bus/h

Q= passenger / hours

V= value of time

• If an hexogen change doubles the travel demand, it can be supplied with 40% additional bus units

Demand Q= 2

Introduction

Landlocked Developing Countries (LLDCs) face peculiar

transport problems, particularly in freight transport, because

of their dependency on other countries co–operation for

access to international trade routes.

Freight costs per km in most LLDCs are more than 50

percent (value of export), higher than in United States of

America and Europe. Transport costs can be as high as 75

percent of the value of exports (Faye et al., 2004).

A number of studies have been conducted on freight

transport problems encountered along the northern corridor;

however, very few have used system dynamics thinking to

analyse and present these problems.

Problems faced

Dependence on transit neighbour: neighbours’

infrastructure; administrative practices; peace and stability.

Long distance from the sea.

High freight transportation cost.

Aims and Objectives

Identify problems faced by transporters.

Identify factors hindering efficiency of cargo clearance along

the Northern Corridor.

Carry out system analysis of factors that hinder freight

transportation along the Northern Corridor and develop

causal loop diagrams that describe feedback mechanisms

between these factors.

SYSTEM ANALYSIS OF BARRIERS TOWARDS FREIGHT TRANSPORTATION IN LANDLOCKED DEVELOPING COUNTRIES: A CASE OF ROAD FREIGHT

TRANSPORTATION IN UGAANDA

Student: OKELLO Cypriano: (Msc.) Transport Planning

Supervisor: Dr. Astrid Guehnemann; Co-supervisor : Prof. Paul Timms

University of Leeds

Methodology

Causal Loop Diagrams (CLDs)

The CLDs are important tool for representing the feedback

structure of systems (Sterman, 2001). The CLDs are

excellent for:

Capturing hypothesis about causes of dynamics

Eliciting and capturing mental models of

individuals/teams

Communicating important feedbacks believed to be

responsible for problems

Data collection

Face to face interviews using semi–structured

questionnaires

Sampling techniques

Purposive sampling (Ministries, Departments and

Agencies)

Missing voices to be included (allows for flexibility)

Data analysis

Draws out patterns from concepts and insights

Data presentation

Causal Loop Diagrams will be used to describe feedback

mechanisms between freight transport problems identified.

Scope

Main focus on road transport along the

northern corridor, from Mombasa to

Kampala

Malaba Border Post (Malaba–Uganda and

Malaba–Kenya)

50% of Travel Time: waiting at BPs & other stops

References

FAYE, M. L., MCARTHUR, J. W., SACHS, J. D. &

SNOW, T. 2004. The challenges facing landlocked

developing countries. Journal of Human

Development, 5, 31-68.

STERMAN, J. D. 2001. System Dynamics Modelling:

TOOLS FOR LEARNING IN A COMPLEX WORLD.

California management review, 43.

Car Dependency in the City of Leeds: The Impact of Infrastructure and Culture

Objectives

The purpose of this dissertation is to explore some of the key questions in relation to car dependency within the City of Leeds: •  What is the extent of car dependency in the city? •  What are the main causes for it?

o  In particular what is the extent of the role of two of the main contributing factors towards car dependency:

Attitudes and Infrastructure

On gaining a measure of these issues, this dissertation will set out what could be done to reduce it through Policy Changes and/or Capital Investments.

Background

Campaign for Better Transport 2012 Annual survey that ranks each city by its dependency on cars. Cities are scored on: Of the 26 cities included in the scorecard, Leeds was 20th overall In relation to amount of car use it was joint 24th

Why is car use bad? •  Roads are Congested •  Economic Impacts e.g. disutility of time spent in traffic •  Accessibility Impacts e.g. people unable to get to where they want to •  Environmental Impacts e.g. pollution, effect on health, carbon •  Social e.g. effect of inactivity, leading to obesity issues

Why is it particularly bad for Leeds? •  Car ownership in Leeds is still growing

o  2001-2011 – 2% increase in number of households that have a car (ONS, 2001 and 2011 Census)

•  Two-way AM peak traffic volumes increased by 10% between 1990-2012 •  Population still rapidly growing:

o  11.8% larger in 2021 from 2011 with 840,000 people living within the Leeds district area (ONS, Sub-National Population Projections, 2012) and;

o  74,000 new homes planned to be built between 2012-2028 (LCC, Core Strategy, 2013)

•  A net importer for jobs, with more travelling into the city to work than travel out: o  Circa 460,000 people employed in Leeds (ONS, Nomis Job Density Data, 2011)

o  50,000 more than flow out of Leeds (ONS, Commuter Annual Population Survey, 2011)

Methodology 1.   Desktop Study •  Examine the existing infrastructure in Leeds - including GIS analysis •  Determine if there is any validity in claims that Leeds is a car dependent city due to

infrastructure compared with cities that scored well on the CfBT scorecard

2.   Opinion Survey •  Scope - Aimed at car users commuting into Leeds, focusing on attitudes to car use,

infrastructure, public transport and active travel provision from an individual perspective •  Influence – Survey will be informed by existing literature e.g. Linda Steg’s article, Car Use:

Lust and Must (2005) in which surveys were used to examine motives for car use •  Concepts from the TPB model will also be used to inform the direction of the survey •  Method - Survey to be carried out using an online survey website, circulated through Metro’s

business contacts who are signed up with the Travel to Work team •  Sample Size – Circa 200. If this cannot be attained through the on line survey, manual

surveys will be carried out at key locations in city centre e.g. car parks •  Analysis - Designed to allow for ANOVA to explore the variations in people’s responses in

respect of their attitudes towards different aspects of transport and car use •  T-tests to be used to demonstrate whether there is any significance in the different

responses from the different groups •  Analysis will enable results to be tied back to the aims and objectives to provide suggestions

for possible targeted policy changes or investments to reduce car dependency

Key Thoughts – Attitudes Steg (2005) – Car Use: Lust and Must •  Car use not just about fulfilling a function i.e. getting people to work. It has a large

symbolic status, with pleasure being derived from its use, even just for commuting “the car is much more than a means of transport”

•  People use cars because of the experience of driving, because of its status •  This reinforces people’s choice to drive •  Policy needs to target these attitudes - offer a real alternative in public transport?

Ajzen (1985) – Theory of Planned Behaviour •  People’s choice of mode such as car, is dependent on their attitudes, social norms and

perceived behavioural control •  In order to change people’s behaviour and choice of car as a mode, you need to target these

areas o  Offer real alternatives to the car, change the social norm so that public transport /

active travel is how you get about in Leeds o  Improve the image of public transport / active travel and discourage that of the car

Ellaway et al (2003) – In the Driving Seat •  Explored the psychological benefits associated with private and public transport to help

explain why so many people drive i.e. car has greater psychological benefits than public transport.

•  Suggests that in order to encourage reduction in private car use policy must take these types of benefits people derive from car use into account

Key Thoughts – Infrastructure

Leeds’ transport system focuses around its city centre, with a large number of commuting trips coming in from outside the Outer Ring Road (ORR) •  29% of commuting trips to Leeds city centre made within the ORR during AM Peak •  71% from further afield – people commuting in are more likely to use a car •  45% of total trips made by car •  30% by rail and 25% by bus (LCC, Transport for Leeds Project 2008/09)

Car •  Well established road and motorway network built in ‘spoke and wheel’ layout •  Makes travelling by car easy and allows direct access to city centre from suburban areas and

other districts •  Large amount of car parking in city centre – circa 22,000 spaces (LCC, Annual Parking

Report, 2011/12)

Rail •  Network serves limited radial corridors, with few stations within ORR •  High rail demand, circa 16,800 arriving in Leeds City Station during morning peak in 2013,

compared to 12,400 in 2004 (LCC, Cordon Count Data) •  Figure has tapered off in recent years, suggesting network is reaching capacity •  There are plans to expand network capacity – new stations, longer trains and improvements

to the lines to cope with more train services

Bus •  Well established network – Patronage remains at consistent level year on year •  Bus mode share for commuting trips higher than car within ORR – 59% •  Outside ORR it is 18% and 47% for car (LCC, Transport for Leeds Project 2008/09) •  Long dwell times at stops due to boarding – smartcard ticketing is being phased in •  Bus punctuality – 88.6% run ‘on time’ (1minute early and 5 minutes late) (Metro,

MetroFacts, 2009/10) •  This falls short of Traffic Commissioner’s target of 95% •  Large journey time variability

Rapid Transit •  City has none – largest city in Europe to have nothing •  Trolleybus networked planned to provide a real alternative to car. •  However, only one initial line so limit impact

Cycling/Walking •  Little infrastructure for cycling, although it is improving e.g. Cycle Superhighway •  However, city geography makes it difficult to encourage large numbers of cyclists •  Long distances between suburban areas and city centre

2  Way  Traffic  Cordon  Flows  For  All  Vehicles  in  AM  Peak    (LCC  Monitoring)  

1990   2004   2012   1990-­‐2004  Growth  

2004-­‐2012  Growth  

1990-­‐2012  Growth  

 145,474      163,098      160,484     12%   -­‐2%   10%  

Chris Payne Supervisor: Ann Jopson

Accessibility and planning

Buses and trains quality and uptake

Cycling and walking as alternatives

Driving and car use Larger  squares  =  be9er  rankings  in  category  

Source:  Steer  Davies  Gleave,  2009  

Source:  Leeds  City  Council  

Leeds  Transport  Geography  

Congested  Routes  in  Leeds  District  

• Complex road network with 245,000 miles worth of road (DfT, 2012) • 35 million vehicles on British roads in 2013 and that is a 1.5% increase

from 2012 (DfT, 2014) • Roads don’t last forever, wear and tear, accidents means that there is a

need for increased investment to being maintained • The maintenance of local authority managed roads is being reduced:

- 2009/10 £3.3 million - 2010/11 £3.1 million - 2011/12 £3.0 million (DfT, 2013)

• By 2020/21 £6 billion will have being invested to help repair and sustain

local roads (Great Britain & HM Treasury, 2013) • Must use resources more efficiently, how is this decided? How should it

be decided • Providing a service for the public, so ask the public what they think

• The customer satisfaction surveys involve 46 local authorities • Cross comparison of two models, will be the same except with

the addition of customer satisfaction in one of them • What determines the cost?

Cost= f(Type of treatment, time constraints, Customer Satisfaction,

availability of resources, traffic management, utilities)

• Estimate the significance, size and signs of the variables based on the economic background

• Problems, which could be encountered: missing variables, errors in variables larger data set needed, the independence of the authorities

• To solve these problems appropriate tests will be taken

Disadvantages

• Only when habits are changed can there be a true value

• Instruments for measuring customer satisfaction not readily available

• Difficult to apply costs to a 5 point scale (Abou-Zeid 2008)

• Personality and taste will affect the results making it biased

• Not consistent when surveys are repeated because there will be a different range of income, age, gender, employment

𝐻0 : Customer satisfaction plays a valid role 𝐻𝐴 : Customer satisfaction plays no significance

Abou-Zeid, M. Moshe B, and Michel B. 2008. Happiness and travel behavior modification. Proc. of the European Transport Conference. Department for Transport. (2012). Road lengths in Great Britain: 2011. Department for Transport. (2013). Road Conditions in England: 2012 Department for Transport. (2014). Vehicle Licensing Statistics: 2013. Great Britain & HM Treasury. (2013). Investing in Britain’s future. Vol 8669. Stationary Office. Highways Agency. (2014). Listening to our customers. Olsson, L. Friman, M. Pareigis, J. Evardsson, B. (2012). Measuring service experience: Applying the satisfaction with travel scale in public transport. Journal of Retailing and Customers Satisfaftion. 19, pp. 413-418.

Charlotte Stead- 200386644 MA Transport Economics Phill Wheat

• 𝐻0 : Customer satisfaction plays a valid role - Fail to reject the null hypothesis, - Customer satisfaction should be used - How can it be improved? • 𝐻𝐴 : Customer satisfaction plays no significance - Can reject the null hypothesis - What are the alternatives

• The problems encountered in the model • How this model can be improved

Advantages

• Used to assess the non monetary costs such as time, smoothness of the journey, cleanliness (Olsson 2012)

• Surveys are used to assess how well services are meeting expectations

• This is needed to influence investment decisions

“Understanding the needs of our customers is an integral part of the Agency’s operations. To help us achieve our vision we need help.”

(Highways Agency, 2014)

All roads needs maintenance

Highway Maintenance Strategy

SOURCE: Public Transport Authority of Western Australia

Workplace test group: One40 William

PHOTO CREDIT: Hassell

One40 William

Sub-headings (36 pt) Main body (24 pt) Captions (18 pt)

Author (50 pt) Title (80 pt)

Results Oral presentation Written dissertation Summary report to

participating organisations

Analysis Data cleansing Cross-

tabulation Principal

Components Analysis

Multiple Discriminant Analysis

Data Collection Questionnaire: Test group - One40 William Control group - same/similar

organisations, alternate sites

Literature Review Car dependency Land use &

urban design Mode shift Habit disruption

TIPPING THE SCALES

Do active and public transport facilities at the workplace reduce commuter car use?

Researcher: Catherine Wallace (ts13clw@leeds.ac.uk), MSc Sustainability (Transport) Supervisor: Ann Jopson (A.F.Jopson@its.leeds.ac.uk); Second Reader: Frances Hodgson

Institute for Transport Studies FACULTY OF ENVIRONMENT

Increased use of active and public transport for commuting?

Office building integrated with train station

Close to bus stops & central bus station

Free Transit Zone

End of trip facilities

Parking restrictions and

high fees

Context

Objectives

Methods

Analysis

Research Questions Introduction CAR DEPENDENCY §  Private car use is reaching unsustainable levels in many industrialised countries (Kenworthy & Laube 1996).

§  There is a particular interest in reducing the negative effects of congested commuter traffic in cities (O’Fallon et al. 2004).

§  Many of the negative effects (to the economy, health and the environment) seemingly cannot be mitigated by technological improvements alone (Bamberg 2007). Behavioural change is required.

§  The psychological motives for car use are not just instrumental (practical) ones, like travel time and convenience. Car use has an affective/symbolic function – it represents power and control; it is a status symbol and extension of self (Steg 2005).

LAND USE & URBAN DESIGN §  ...have a cumulative effect on travel behaviour (Litman, 2014) §  Parking management can reduce car trips between 10-30%; multi-modal site design also thought to contribute (Litman 2014)

§  When examining commuter mode choices, most studies look at the impact of residential location and access to transport services and infrastructure from home (Vale 2013)

§  Proximity to public transport and quality of active transport facilities near home affect mode choice (Naess 2009)

§  People may also self-select their home location to reflect their preferred mode choice (Cao et al. 2009), but self-selection may play a lesser role in workplace location and particularly workplace relocation (Vale 2013)

§  Research gap: how do workplace (destination) facilities/access influence commuting choices (Vale 2013; Litman 2014)

MODE SHIFT §  Commuting accounts for 15-20% of trips, but >50% of congestion (Litman, 2014)

§  To change behaviour, you change the person or the conditions (Stradling et al. 2000)

§  City centres, where many workplaces are focused, ”are more amenable to alternative modes” (Litman 2014, p.18)

à Is changing the conditions at a city centre workplace enough to change commuter behaviour?

HABIT DISRUPTION §  Commuting is habituated (de Brujin et al. 2009) §  To break a habit, you need an impetus that makes people re-evaluate their choices (Handy et al. 2005; Bamberg 2006)

§  Research gap: what disrupts commuter habit? (de Brujin et al. 2009)

Develop and pilot online questionnaire: §  Travel behaviour before and after office relocation §  Commuting habits (Self-Reported Habit Index, adapted from de Brujin et al. 2009)

§  Psychological motives (affective & instrumental factors, adapted from Steg 2005 and Bergstat et al. 2011)

§  Personal characteristics (age, gender, postcode, household car & bike ownership, private/company vehicle, income, employment type, # children, major life changes, etc)

Administer questionnaire: §  Test group: One40 William (building opened 2011; majority government tenants, with some private, retail & hospitality)

§  Control group: same or similar organisations at alternative sites (with less favourable PT & AT access/facilities)

§  Aim for 100+ respondents per group

Analysis will be conducted using SPSS: §  Data cleansing: check for errors, outliers; run descriptive stats; t-tests; check sample distribution, transform if necessary

§  Cross-tabulation: test group travel behaviour before and after relocation (Stradling et al. 2000)

§  Principal Components Analysis: psychological factors (Steg 2005) §  Multiple Discriminant Analysis: analyse difference between test and control groups in current travel behaviour, psychological motives and habits.

The key objective of this study is to understand the impact of active and public transport infrastructure and services at the workplace on commuter mode choice. This involves its: §  impact on commuter behaviour §  ability to disrupt habit and influence psychological motives §  implications for future policy

Many cities are seeking to shift commuters away from car use in favour of public transport and active transport (walking and cycling). A significant shift offers many advantages, including:

§  Reduced congestion (and associated costs) §  Reduced emissions and better air quality §  Improved health outcomes (including a reduction in major preventable diseases, such as obesity)

The Australian city of Perth will be used as a case study.

Congestion costs: Expected to reach AU$2.1b by 2020, up from AU$900m in 2005 Transport emissions: 15.3% of total emissions, Australia-wide (2nd highest growth rate; 34.6% growth 1990-2009) Health impacts: 63% Perth metropolitan area, obese or overweight

Potential Implications Workplace conditions and employment practices are arguably easier (and more expedient) to influence than residential ones. If workplace (destination) factors: have a significant effect on mode choice; can disrupt commuter habits; and/or influence psychological motives for car use, this could inform policies to reduce commuter car use and its negative effects in cities.

Literature Review Data Collection Will the below workplace (commuter trip destination) factors: §  Increase the use of active and public transport for commuting? §  Reduce commuter car use? §  Disrupt the habit of commuter car use? §  Affect psychological motives (affective/instrumental) for car use?

Fast facts: §  4th largest Australian city §  Pop. 1.83M (⎡22% 1990-2009) §  20% of all jobs in city centre §  High car ownership (600 cars per 1,000 people)

§  Lagging larger Australian cities in AT & PT commuting

SOURCES: 2014 MapData Services Pty Ltd, PSMA Australia Ltd, Google Maps.

Perth, Western Australia

~115km (71.5mi)

Infographics created by the researcher based on cited source information.

ROAD TRANSPORT EMISSIONS AND ITS EFFECT ON PUBLIC HEALTH IN GHANA

A CASE STUDY OF THE ACCRA PILOT BRT ROUTE

Daniel Essel: Msc Transport Planning & Environment Supervisor: Dr. James Tate Co-supervisor: Jeffrey Turner

Background

A major problem facing the world today is road

transport emissions which have been increasing at

a much faster rate than anticipated. There is little

evidence to support the fact that the current

growth in vehicle ownership especially in

developing countries will decline.

Vehicle population in Ghana increased from

511,755 in 2000 to 1,591,143 in 2013 and

projected to grow by 10% per annum.

A roadside study reports high levels of PM10

exceeding the EPA- Ghana 24 hour mean of

70µgm-3 even though WHO limit value for PM10

is 50µgm-3.

79% of the samples collected at 3 roadside sites

along the BRT route exceeded the EPA-Ghana

24-hour PM10 air quality guideline of 70 µgm3.

Exposure to emissions at roadsides are 7 times

higher within 15 metres but decay as distance

increases.

Epidemiological studies have confirmed short

and long-term effect of vehicular emissions on

respiratory related illnesses.

Objectives

Model current levels of vehicular emissions along

the BRT route

Assess air quality concentrations along the BRT

route

Assess its health implication on residents, traders

and commuters along the BRT route

Expected Outcomes

Residents living within 150m from the BRT

route would have higher exposure to traffic

pollutants than those living further away

The health implications will vary as traffic

levels changes

Commuter and traders spending longer

hours along the BRT route will have

higher exposure to traffic emissions

References

Driver and Vehicle License Authority, 2013: Unpublished Report of Vehicles

Registered in Ghana

Ebenezer Fiahagbe, 2012. Air Quality Monitoring in Accra, Ghana

Kim, J.J. et al. 2004. Traffic-related air pollution near busy roads: the East Bay

Children's Respiratory Health Study. American journal of respiratory and critical

care medicine.

Wright, L. and Fulton, L. 2005. Climate change mitigation and transport in

developing nations. Transport Reviews

Proposed Methodology

Pilot BRT Route 24-Hour PM10 Concentration along the route

Date: 2nd May, 2014

Extract of a section of the BRT route

Proposed Methodology

Source: Adapted from Google Maps Source: EPA Ghana- Air Quality Monitoring Programme

Student : David Nunoo

Programme : MSc. Transport Planning and Engineering

Supervisor : Dr. Samantha Jamson

Leeds – Bradford Canal Towpath Improvements:

Will it encourage social and commuter cycling along the canal? Institute of Transport Studies

Date : May 2014

Author 1 | Author 2 | Author 3 (edit this list on View > Slide Master)

Research questions:

1. Is perceived risk a serious obstacle to cycling and walking along

the canal?

2. Is cycling and walking considerably affected by perceived risk

along the canal?

3. Who the predominant users of the towpath are and their trip

purpose(s)?

Background

The Department of Transport (DfT) granted Leeds and

Bradford City Councils permission to implement a £29million

‘cycle superhighway’ between the cities.

It is foreseen that this will improve the economy, environment,

road safety and people’s health (Bradford-Telegraph-Argus,

2013).

As part of the grand scheme, 14 miles of the existing Canal

Towpath between Shipley and Armely is to be upgraded with

high quality resurfacing.

Methodology

Only the unit name and authors should be

edited in the slide master.

Our templates use the concept of a ‘Slide

Master’ in PowerPoint to ensure that the

crucial elements in the page cannot be

changed, moved or distorted unintentionally.

Content contained in the slide master

includes:

• the University device

• the unit name (must be your official unit name)

• the colour of the banner at the top of the page

• the list of authors

• a ‘master text frame’ that defines the sizes and styles for each level of bullet in the document

You must be on the top master slide in order

to edit the unit name and authors (this is the

slide on the top of the left-hand column in

Slide Master view).

Figure 1: Location plan

References 1. Bassuk, S.S. and Manson, J.E. 2005. Epidemiological evidence for the role of physical activity in reducing risk of type 2 diabetes and

cardiovascular disease. Journal of Applied Physiology. 99(3), pp.1193-1204.

2. Bradford-Telegraph-Argus. 2013. £29 million 'Highway To Health' cycling road scheme announced. Bradford Telegraph and Argus.

3. Caltabiano, M.L. 1994. Measuring the similarity among leisure activities based on a perceived stress-reduction benefit. Leisure

Studies. 13(1), pp.17-31.

4. Chapman, L. 2007. Transport and climate change: a review. Journal of transport geography. 15(5), pp.354-367.

5. Organization, W.H. 2009. Global status report on road safety: time for action. World Health Organization.

Contact information • David Nunoo | Institute of Transport Studies, University of Leeds

• Email: ts12dkn@leeds.ac.uk

• www. leeds.ac.uk

Proposed Methodology

Research aims:

1. To determine if the improvement works along the canal towpath

will result in an increase in the number of commuter and leisure

cyclists along the route.

2. To determine if the improvement works will improve the safety

perception of cyclists and pedestrians along the route.

3. To determine if there is an improvement in the cycling and

walking experience along the route following the works.

Figure 2: Existing section of the towpath

Expected outcome

It is anticipated that the improvement works of the Canal Towpath

will result in a general increase in cycling and pedestrians activities

along the route.

Benefits of Cycling:

1. Cycling decreases the occurrence of ischaemic heart disease,

cerebrovascular disease, depression, dementia, and diabetes

(Bassuk and Manson, 2005).

2. Cycling reduces the occurrence of respiratory problems

(Organization, 2009).

3. Cycling could reduce stress in individuals (Caltabiano, 1994)

4. Cycling is energy efficient because air emissions, noise pollution

and greenhouse gases are not derived from it (Chapman, 2007).

Author 1 | Author 2 | Author 3 (edit this list on View > Slide Master)

BACKGROUND

The private finance functions in developing and expanding Manchester Airport Supervisor: Nigel Smith

Dayuan Xu MSc (Eng) Transport Planning and Engineering Institute for Transport Studies University of Leeds Email: ts13dx@leeds.ac.uk

Analyse the functions of private finance in those successfully extended airports.

Analyse the risk of private investment and measures to reduce the risk.

Identify the most effective mechanisms for utilising private finance in future Manchester Airport development.

How to create a win-win model in PPP.

Manchester Airport ranks only second to Heathrow airport in

the UK.

There are now three passenger terminals and two runways.

The forecasts for Manchester suggest that the Airport could

be handling some 38 million passengers by 2015 and the

number could rise to around 50 million by 2030.

Planning to provide an additional terminal to expand

capacity and exploit economic benefits.

FURTHER WORK

OBJECTIVES

METHODOLOGY

Preliminary activities

Design issues

Limitations

Obtain database of Manchester Airport. Risk analysis of different PFI forms on

both ground and air sides. How to reduce risks in the PPP. What could Manchester Airport learn

from the completely extended airports. The pros and cons of private

investment on airport. Evaluate the private finance in airport

development.

Prepare

Generate dissertation

Data collection Analyse

Manchester Airport

Documents Archival records

Interviews

Protest -characterised asbeing non violent they involvea collection of people whocome together to protest acultural, social, political oreconomic issue (Oliver, et al.2012).

Riot - Riots are one exampleof anti-governmentdemonstrations which is aspontaneous outburst ofviolence from a large group ofpeople (Barkan. 2012)

Flashmob - Strangers meet ata predetermined publiclocation, perform an unusualbehaviour, and then disperse(Duran. 2006)

Mediated Crowd - A newsocial phenomenon relatingto collective action whichemerges as a result of thevirtual arena of ‘’Web 2.0’’and new mobile technologies

Web 2.0– Characterised asbeing a interactive socialmedia and user generatedcontent allowing users toexchange content

The mobile criminal: Protests, Riots & FlashmobsEmma O’Malley

Supervisor: Frances Hodgson

Aim

Understand how the communication aspect of social media can influence theorganisation of Protests, Riots and Flashmobs, specifically those which occur ontransport networks or are in response to changes on the network.

Objectives

• How is transport used as the stage and\or reason or action?• Do changes in communication technologies, particularly social media

significantly influence social organisation to initiate new forms of protests (e.g.,flashmobs, critical mass) on the transport system?

• Following acts on transport networks how do transport systems respond andrecover?

Background

The world is made up of networks whether environmental, social or economic; this projectlooks at the links between communication networks and transport networks, specifically at howcommunication networks as part of new social media is used to support action which disruptsor is a result of changes to transportation system.

Transport Networks• Transportation systems are often the focus of this action as it is intertwined with practically

every aspect of human life meaning that:• Large numbers are people are affected by changes or disruption to the system whether on a

local or global scale• Transportation networks are easily accessible• Action on the system will be very visible(Blickstein and Hanson. 2001).

Communication NetworksThe creation of the mediated crowd is an example of how public communication practices havechanged in the twenty-first century (Baker. 2012). Social Media allows everyone with access tohave a voice and removes physical and spatial barriers allowing communication with a vastamount of people who may share the same mindset (Baker. 2012; Moler. 2013). This allows fora new form of social organisation where people can create or connect with social movementsoutside existing channels far quicker and easier than ever before (Bartlett. 2013).

PreparednessIt is important to understand how this new form of communication influences the organisationthese forms of protest, especially those which occur spontaneously and have large negativeimpacts, as it can assist in preparedness and recovery from such events.

As we move deeper into the ‘internet age’ it is important to understand mobility not just in theform of movement but also related to the ‘new mobilties perspective’ includes the movementof information through the use of the internet and media outlets (Sheller and Urry. 2006).

New Mobilties Perspective

Method

Complete an in depth study on literature surrounding three main areas:• Mobility and communication• Networks (Transport, Communication) and how these networks influence

each other• Existing policy to enhance ‘preparedness’ in the face of new forms of

protestConduct questionnaires with people who have been involved with protestsand interviews with participants who took a leadership role in organisingprotests such as Critical Mass or the London Die- In.

Major Case Study

Critical Mass is an urban sustainability and cycling movement where once amonth a large groups of cyclist ride through a city in rush hour in order toincrease the visibility of cycling (Carlosson. 2002). The event is decentralisedwith no one leader, today the internet has allowed participation to increase,continue and transfer to other cities in a cheap and quick way (Blickstein andHanson. 2001). Other Case Studies will include London Die-In, Plane Stupid, andLondon 2011 Riots.

Glossary of Terms

Expected Outcomes

Identify to what extend new social mediaaffects the organisation of social protestsin the UK

Establish what measures can be taken toreduce negative impacts of such protestsand whether integrating new social mediacan help this aim

TRANSPORT IN DEVELOPING COUNTRIES

(The benefit of implementing NMT Master Plan in Tema, Ghana)

Emmanuel N. Tetteh: MSc Transport Planning and Engineering Supervisor: Jeff Turner

1. BACKGROUND

Transport planning policies in many developing

countries have followed the western systems by using

of models such as Highway Development Management

(HDM-4) which focuses on or mainly dominated by

motorists transport. Hence the gap between motorist

and NMT especially in Africa.

Non motorists transport is the ideal mode of transport

travel within cities. This due to the fact that they require

less space, less energy as well as zero noise and air

pollution . NMT enhance safety and also has direct link

with health

It is widely established, from current studies that a

sustainable transport in terms of impact on areas such

as social economy, environment is the choice mode of

walking and cycling, the two major means of urban

NMT. In developing countries NMT is recommended as

most sustainable transport mode.

2. AIM

The aim of this dissertation would be to look at some of

the benefits that the City of Tema would gain from

implementing the master Plan.

3. OBJECTIVES

The main objective will focus on the following:

Identification of general NMT benefits

Congestion benefits

Challenges in terms of infrastructure

A critical review of why NMT in Accra did

not work

4. METHODOLOGY

Secondary data available in final submitted

report of ministry of road transport and

Highways of Ghana 2013 master plan for Tema

would be the main source of data to be used for

this research.

The existing data will be used to access the

relative benefits of promoting NMTs such as

health.

5. PROPOSED SCOPE

This research will be limited to the analysis of

congestion and economic benefits after the

implementation of NMT Master Plan in the City

of Tema in Ghana.

The research will also look at some challenges

that will need to be addressed during the

implementation in terms of infrastructure for

Non Motorists Transport (NMT).

Cyclist in Tema

Congested road in Tema

Google Map of Accra and Tema

Geographical Location of Study Area

THE EFFECT OF AXLE LOAD ON THE TRANS WEST AFRICA HIGHWAY – A CASE STUDY ON THE AGONA JUNCTION TO ELUBO ROAD SECTION IN GHANA

MSc (Eng) Transport Planning and Engineering

OBJECTIVES The study will generally seek to analyse the economic effect of

strict enforcement of axle load control limits on transit trade

and road investment. And will specifically aim to answer the

research questions.

METHODOLOGYEFFECT OF AXLE LOAD CONTROL REGIME

Purposive Sampling Technique

TARGET GROUP1. Heads of Institutions /Senior 

Officers (GPHA, GSA  & HAULERS)2. Transit Trucks only 

GROUP 1Personal Interviews using Questionnaires

GROUP 2Field Survey  to Collect 

Axle Weights

Secondary Data & Design Parameters of Case Study

Design Scenarios for Sensitivity Analysis

DATA ANALYSISExploratory and Confirmatory

ECONOMIC VIABILITYHDM‐IV or CBA

KEY FINDINGS, RECOMMENDATIONS AND 

CONCLUSIONS

Source:  National Overloading Control Technical Committee, South Africa (1997) 

EFFECT OF OVERLOADING

OVERLOADING TREND IN GHANA

BACKGROUND The West African Regional trading block, ECOWAS, is

aligning its priorities towards economic integration of

its member states.

The development of the Trans West Africa 

Highway transiting five (5) member states 

(Cote D’Ivoire, Ghana, Togo, Benin & 

Nigeria) has been given the highest priority.

56% of this corridor lies within the

boundaries of Ghana of which 20% is the

case study area (i.e. Agona Junction to

Elubo Road)

A major threat to the life span of this road pavement

is the axle weights of transit trucks.

Transit trade is however a major contributor to

Ghana’s Economy (World Bank, 2010).

How to determine the balance of implementing an

axle control limit that is viable for revenue generation

at the port and prevent premature pavement

deterioration.

What is the current level and extent of axle

overloading on the studied road?

What is the design traffic loading used for the Agona

Junction – Elubo road pavement design?

What axle load limit will be economically viable to

implement?

DESCRIPTION OF STUDY AREA

RESEARCH QUESTIONS

PROBLEM STATEMENT

A 110km road length along the coast of 

Ghana to the border with Cote D’Ivoire.

Lies in the equatorial climatic zone 

and is the wettest part of Ghana.

Nationally, it serves a population of 

about 1.84million inhabitants and an 

area of 23,921km2 (World Bank, 2010).

Name: ERNEST O. A. TUFUOR (ID‐200661275) 2013/2014              Supervisors: JEFFREY TURNER AND DAVID ROCKLIFF

Rutting

Not Safe

Source: Ghana Highway Authority,  2012 Annual Axle Load Report (2013)

2008 2009 2010 2011 2012Number of Weighed Trucks 14625 47480 49586 140311 194516Number Overloaded 3773 7026 9452 34302 34245Percentage Overloaded 26% 15% 19% 24% 18%

26%

15%

19%

24%

18%

0%

5%

10%

15%

20%

25%

30%

0

50000

100000

150000

200000

250000

A MAP OF WEST AFRICA

Mode Choice Analysis for

Shopping Trips in Great Britain Gandrie R. Apriandito (200737853)

Supervised by Jeremy Shires and Daniel Johnson

• To examine the relationship between

expenditure and transport accessibility

• To identify what factors influence people

in determining the choice of mode for

shopping trips

• To design relevant transport policy

recommendations in order to get more

people using bus instead of cars

Objectives

• Primary data was

collected by ITS for DfT

through an online

survey across Great

Britain

• Distance and time

travelled are

compiled from

Transport Direct to

calculate journey cost

Data Collection

Methodology

Primary Data Secondary Data

Expenditure and

Accessibility Mode Choice

Analysis

Regression

Analysis Logit Model

Transport Policy Recommendations

• The dominant journey purpose for bus

trip in Great Britain is shopping with 1.3

millions per annum, surpassing

commuting purpose with 1.1 million

passengers per annum (National Travel

Survey)

• Nearly 70% of shopping activities are

located in either city or town centres. Bus

service is essential in providing efficient

accessibility to the potential demand

• More than half of shopping trips are

undertaken by cars

Background Key Issues

Logit Model

Un,j = Vn,j + εn,j

Example for single

observation n with j

different modes

• U: Utility choice

function

• V: Deterministic

function of the

attributes

• ε: Unobserved part

(distributed

independently

and identically)

Expenditure Model

• It is the function

of individuals

characteristics

and generalised

cost

• Relates to

income,

employment,

shopping

location, modes,

specific purpose

• Generalised cost

is the function of

accessibility and

fares (for bus)

Structuring a well-defined decision

guidelines based on demand and supply

characteristics of the traveller and

alternatives available

The railway system in Great Britain is the oldest in the world.

The world's first locomotive-hauled public railway opened in

1825. Rail passenger demand has experienced significant

growth in the last decade. The study is aimed at undertaking

analysis to determine quantitative elasticity variations with key

factors that drive passenger rail demand in Great Britain for the

period 2002 to 20011. This information is vital in facilitating

decision making, planning, management, policy formulation and

investments in the transport sector.

A measure frequently used to summarize the responsiveness of

demand to changes in the factors determining the level of

demand is the elasticity. Given as :

Where ∆y is the change in the demand y, and ∆xi is the change

in the explanatory variable xi.

• The study offers valuable insights to the variations in the

responsiveness of key drivers to rail travel growth.

• There is plenty of empirical evidence on elasticity’s but not so

much evidence on examining variations. The main aim of this

study is to produce quantitative indications of elasticity’s

variation with key factors such as distance; route; ticket type;.

i. To find evidence on fare elasticity variations.

ii. To find evidence on service quality elasticity variations

iii. To determine general journey time elasticity variations

iv. To find evidence on elasticity variation with the strength of

competition

v. To investigate evidence on GDP elasticity variations across

routes , distance and overtime.

• The study will adapt the conventional modelling approach, the fixed

effect model (FEM) expressed as: • 𝒍𝒏𝑽𝒊𝒋𝒕 = 𝝁𝒊𝒋 + 𝜶𝒍𝒏𝑭𝒊𝒋𝒕 + 𝜷𝒍𝒏𝑮𝑱𝑻𝒊𝒋𝒕 + 𝜸𝒍𝒏𝑮𝒊𝒕 + 𝜹𝒍𝒏𝑷𝒊𝒕 + 𝜼𝒍𝒏𝑻𝒊𝒋𝒕 +

𝝀𝒍𝒏𝑪𝒊𝒋𝒕 + 𝝆𝑯𝒊𝒕 +𝜺𝒊𝒕

• The FEM allows the time invariant differences between flows which

cannot be included or the time-invariant difference between flows to be

expressed as a specific function of included variables as compared to

the ratio modal approach.

• The beauty of greater generality of FEM makes it preferable for

estimation of panel data.

• The basic model for the study is the fixed effect model (FEM)

as opposed to the previous studies that used the ratio model

(RM) and the PDFH.

• Quantitative secondary panel data from rail operating

companies will be used in this research, consisting of 184 flows

ranging from 20 to 300 miles between stations, in 13 periods

from 2002 to 2011.

• Econometric analysis will be done using Eviews soft ware.

Presentation of results in forms of figures, tables, charts

• Output will be in two forms:

o Within group variation: variation over time for each

flow

o Between group variation: variations flows

AN ANALYSIS OF ELASTICITY VARIATIONS IN RAIL

PASSENGER DEMAND IN GREAT BRITAIN

2002 -2011

• Resent developments in the field of elasticity’s have led to

renewed interest in extending the analysis to variations in

elasticity’s across different key factors.

• Fares and quality of service are fundamental to the operation of

public transport since they form major sources of income to

operators. Evidence on fare elasticity and quality of service

elasticity variations are crucial in decision making on pricing

policy, service level changes and evaluation of non equal-

proportional fare changes for cost effective schemes.

• The last decade has seen transformation of the railway

therefore, it is important for policy-making to be informed by

best available knowledge about the variations in elasticity's

• The GDP elasticity represents the positive impacts of economic

activity on business trips and income on leisure trips.

BACKGROUND

WHY IS IT AN IMPORTANT SUBJECT?

WHY ARE WE STUDYING IT?

WHAT DO WE HOPE TO ACHIEVE?

HOW ARE WE GOING TO DO IT?

WHY THIS MODEL?

WHAT EVIDENCE IS THERE?

Gerald Harry Ekinu- MA Transport Economics

(ID: 200734159)

Supervisor: Professor Mark Wardman

area; elapsed time and levels that this variables take

• A need to recognize and address the limitations of previous/ current

studies in the modelling approaches used.

The Role of Incomes in Discrete Choice Models: implications in

welfare measure in transport investment appraisal

1. Background, Motivation & Objectives

2. Theoretical Framework

3. Overall Methodology 4. Case studies: railways

5. Expected Results

6. References

•Small, K.A. and Rosen, H.S (1981) “Applied welfare economics with discrete choice models’. Econometrica, 49 (1) 105-130. •Batley, I. and Ibanez, N. “Applied welfare economics with discrete choice models: Implications of Theory for Empirical Specification”. Working Paper. •Jara-Diaz, S.R. (2007). Transport economic theory. Oxford: Elsevier. •MaFadden, D. (1973)“Conditional Logit Analysis of Qualitative Choice behavior”.

UNIVERSITY OF LEEDS Institute for Transport Studies (ITS)

• Since the theoretical work of Small and Rosen (1981), applications in discrete choice models to welfare analysis in transportation sector have taken relevance in the academic and policy-makers grounds.

• The mis-specifying of incomes in discrete choice models might potentially lead to inaccurate measures of welfare.

• The theory in discrete choice models has made an important progress over years, that its recent approaches may cope potentially the mis-specifying of incomes in discrete models.

• To examine the role of incomes in discrete choice models from: (1) the theoretical basis in welfare measure; and (2) practical application in investment transport appraisal.

Literature Review

Theoretical basis: to review the concepts stated in Batley and Ibanez (2010).

Practical basis: to examine how incomes have been specified in DCM in literature.

Cases study

(a) Crossrail and (b) Linea 2: to review the way of incomes have (or not) been specified in the calculation of welfare. to analyse the assumptions regarding incomes in measuring user benefits.

Report of findings

Implications of mis-specifying incomes in welfare analysis.

Outline a practical guidance of income effects in discrete choice models.

• Incomes in discrete choice models might have implications for practical purposes in measuring welfare.

• The implications in welfare measure of mis-specifying incomes in discrete choice models might be significant and lead to inaccurate calculations of benefits in transport investment appraisal under some circumstances.

• Assumptions regarding incomes might be potentially more compatible with techniques in advanced discrete models.

Marshallian Demand X=X(P,M)

Hicksian Demand X=X(P,U0)

P

X1 M/P1

P0

P1

Subst. Effect

Income Effect

a b a + b = CS a = CV

𝑀𝑀𝑀𝑀𝑀𝑀 𝑈𝑈 𝑋𝑋 s.t. ∑ 𝑃𝑃𝑖𝑖𝑋𝑋𝑖𝑖 ≤ 𝐼𝐼𝑖𝑖 ; 𝑋𝑋𝑖𝑖 ≥ 0

X2

X1

M/P0

A

B

M/P1

C

U1

U0

𝐶𝐶𝐶𝐶 = −� �𝑋𝑋𝑖𝑖𝑐𝑐(𝑃𝑃𝑖𝑖𝑈𝑈0)𝑑𝑑𝑃𝑃𝑖𝑖𝑖𝑖

𝑃𝑃𝑃

𝑃𝑃0

Neo-classical approach of welfare

Δ𝑀𝑀𝐶𝐶𝑀𝑀 = −� �𝑋𝑋𝑖𝑖(𝑃𝑃, 𝐼𝐼 )𝑑𝑑𝑃𝑃𝑖𝑖𝑖𝑖

𝑃𝑃𝑃

𝑃𝑃0

𝐶𝐶𝐶𝐶 = −∫ ∑ 𝑋𝑋𝑖𝑖𝑐𝑐 𝑃𝑃𝑖𝑖𝑈𝑈0 𝑑𝑑𝑃𝑃𝑖𝑖𝑖𝑖𝑃𝑃𝑃𝑃𝑃0 = 𝑒𝑒 𝑃𝑃0,𝑈𝑈0 − 𝑒𝑒(𝑃𝑃𝑃,𝑈𝑈0)

A theoretical approach of income effect in welfare measure

Discrete choice models in demand

Discreteness in demand can be modelled in at least three forms when goods may be (Small and Rose, 1981): (a) available in continuous quantities; but in only one mutually exclusive varieties, e.g. housing/rent; (b) available in discrete large units that one or two are chosen, e.g. transport modes; and (c) purchased because nonconcavities leads corner solutions, e.g.tv show aired simultaneously. To exemplify illustratively a probabilistic choice, a decision-maker faces the following task:

Decision-maker 𝑃𝑃𝑃𝑃𝑘𝑘 = 𝑃𝑃𝑃𝑃(𝑤𝑤𝑘𝑘 + 𝜀𝜀𝑘𝑘 > 𝑤𝑤𝑘𝑘 + 𝜀𝜀𝑘𝑘)∀𝑖𝑖 ≠ 𝑘𝑘

𝑃𝑃𝑃𝑃𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡 = 𝑃𝑃𝑃𝑃(𝜀𝜀𝑏𝑏𝑏𝑏𝑏𝑏 − 𝜀𝜀𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡< 𝑤𝑤𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡 − 𝑤𝑤𝑏𝑏𝑏𝑏𝑏𝑏)

𝑃𝑃𝑃𝑃𝑏𝑏𝑏𝑏𝑏𝑏 = 𝑃𝑃𝑃𝑃(𝜀𝜀𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡 − 𝜀𝜀𝑏𝑏𝑏𝑏𝑏𝑏< 𝑤𝑤𝑏𝑏𝑏𝑏𝑏𝑏 − 𝑤𝑤𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡)

Institute for Transport Studies MA Transport Economics FACULTY OF ENVIRONMET Presented by: Gian Carlos Silva Ancco - ml12gcs@leeds.ac.uk - May 2014

Advised by: Dr. Richard Batley

Crossrail (London): £10.2 billion investment; 21km twin-bore tunnel, NPV £6.5 billion using DfT VoT or £11.5 billion using TfL VoT; increased capacity of London transport network; time saving DfT £7.4 bn or TfL £10.2 bn; congestion relief DfT £5.9 bn or TfL £8.1 bn; 200,000 passengers morning peak; discount rate 3.5 and 3.0; conventional BCR DfT 1.87 or TfL 2.55. Metro Linea 2 (Lima): USD 6.5 billion

investment; PPP contract; 35km twin-bore tunnel; 4 to 6 year of construction; 35 stations; number of trains from 26 to 42; 662,346 estimated passengers daily; NPV USD 759 miles; discount rate 9%, BCR 1.15, VoT USD 2.51; max reduced journey time between two stations: 70 min.

The income effect (variation in the purchase power) may be present in a lump-sum or change in price. The formulation of welfare is given by:

Δ𝑀𝑀𝐶𝐶𝑀𝑀 = −∫ ∑ 𝑋𝑋𝑖𝑖(𝑃𝑃, 𝐼𝐼 )𝑑𝑑𝑃𝑃𝑖𝑖𝑖𝑖 = −𝑃𝑃𝑃𝑃𝑃0 ∫ ∑ −

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

𝑑𝑑𝑃𝑃𝑖𝑖𝑖𝑖𝑃𝑃𝑃𝑃𝑃0

If marginal utility of income (denoted by λ) is constant, then ∆MCS equals CV:

Δ𝑀𝑀𝐶𝐶𝑀𝑀 = 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

𝑣𝑣 𝑃𝑃𝑃,𝑦𝑦 − 𝑣𝑣 𝑃𝑃0,𝑦𝑦 = 𝑒𝑒 𝑃𝑃0,𝑈𝑈0 − 𝑒𝑒(𝑃𝑃𝑃,𝑈𝑈0)

The assumption of constant λ implies path-independency in Marshallian demand, i.e. alike welfare measure in Hickesian and Marshallian approach.

Where: 𝜆𝜆 = 𝑑𝑑𝑉𝑉𝑑𝑑𝑑𝑑⟹ 𝜕𝜕𝑥𝑥𝑖𝑖

𝜕𝜕𝑝𝑝𝑝𝑝= 𝜕𝜕𝑥𝑥𝑝𝑝

𝜕𝜕𝑝𝑝𝑖𝑖∀𝑖𝑖, 𝑗𝑗

f

f

Site Profile: Merseyside

Population: 1,381,200 (9th)

645 km2 (43rd)

5 Boroughs – Knowsley, Liverpool, Sefton, St Helens, Wirral

Valued at £21.9 Billion (2.1% of U.K economy) GVA (Gross Value Added)

0 1 2 3 4 50.5

Miles

Ward_Boundaries

No. of Households With Access To A Vehicles

18 - 94

95 - 171

172 - 247

248 - 323

324 - 400

401 - 476

477 - 552

553 - 628

629 - 705

706 - 781

No Data

¡

No. Of Households with No Access To A Vehicle

(Inset of Wirral and Liverpool Boundaries)

0 2.5 5 7.5 101.25

Miles

0 1 2 3 4 50.5

Miles

Ward_BoundariesNo. Of Households with Dependents

8 - 28

29 - 49

50 - 69

70 - 89

90 - 110

111 - 130

131 - 150

151 - 170

171 - 191

192 - 211

No Data

¡

No. Of Households with Dependents in Merseyside

(Inset of Wirral and Liverpool Boundaries)

0 2.5 5 7.5 101.25

Miles

The Maps:

The Maths:

PC = TC TC + TT

CIJ = a1.tIV + a2.tWK + a3.tWT + a4.tIN + a5.F + a6

The Method:

Ai = Σ [BJf(CIJ)]

Accessibility To Destination:

This will demonstrate how accessible a location is based on either Thiessen polygons (zone inside polygon is closer to that sample) or isochrones (coloured bars of equal value).

Modal Split/Destination Attractiveness:

Multi-nomial logit models calculating modal split. (Equation can be used for destinations). These can be portrayed 3D or by colour (large spike, more accessible)

Route Allocation/ Trip Chaining:

Network Analyses using the cost equation above will show the “cheapes t” rou te fo r an i n d i v i d u a l t o u s e . A l s o demonstrates trip-chaining (best way to carry out multiple tasks).

D e m o n s t r a t e a n d evaluate disaggregate t e c h n i q u e s o f accessibility analysis

Potential Issues

Establishing parameters. How to

measure a preference?

How disaggregate is too disaggregate?

Issues with deterrence functions.

How viable are the methods?

Literature Review: 1970

Geographical Space Accessible Space

2011

Hagerstrand developed a time-geographies concept.

3 M a i n C o n s t r a i n t s : “capability constraints”, “coupling constraints” and “authority constraints”.

Computing power h a s a l l o w e d f o r disaggregate activity model l ing to be c a r r i e d o u t .

S t i l l a “ p i o n e e r i n g ” m e t h o d . B u f f e r s a r e predominant technique

Not a spatially defined problem. – Large geographical space, small accessible space.

Need to consider area mobility, individual m o b i l i t y a n d a r e a a c c e s s i b i l i t y .

Economic status, car availability and physical or social preferences and limitations can affect how a person can or wants to travel.

A Local Travel Plan (LTP) set up by the Local Authority and Merseytravel seeks to “develop a fully integrated and sustainable transport network... And ensures good access for all in the community” (Merseytravel 2000)

The End Result: GIS outputs of varying types with two main aims:

1. Levels of accessibility for individuals demonstrating how their lifestyles

affect their travel choices and access.

2. Differences between the disaggregate methods used in the study and aggregate methods carried out as a comparative tool.

This will establish if the individual level studies are more accurate than aggregate measures, and if so, to what extent, with what outcomes?

What Next…?

Need to establish parameters to use within the functions.

Carry out the GIS techniques and modelling.

Analyse and evaluate the datasets – See what is possible.  

Large number of citizens in certain areas are not car users and as such find it hard to carry out fundamental tasks, such as taking children to school or going shopping. Thus creating and social exclusion transport poverty.

f f

Some Important Questions:

Can GIS model the movements of individuals based on their preferences, motivations and restrictions, and how these relate to transport?

Does a micro-level analysis offer a viable alternative to the current methods of study?  

One Size Doesn’t Fit All... Many relationships exist between different individuals preferences, capabilities and the levels of their t r a n s p o r t a c c e s s i b i l i t y .

Aggregate methods (groups, not individuals ) can miss important elements of a persons accessibility.

Study Aims:

Carry out an accessibility study of Merseyside at a disaggregate level.

Highlight inefficiencies in aggregate modelling

Show that individuals from the same areas have differing travel

patterns.

Methodology

Traffic signals are important in the safe use of

road space and efficient control of traffic in

congested urban transport networks.

Combining a traffic model and an optimisation

method, traffic signal control models devise

signal timings that meet certain objectives.

The current traffic signal control is based on the

average traffic condition, and does not account

for variability (or say ‘noise’) in traffic.

To develop a traffic signal timing model that

account for variability in traffic.

To consider Cross Entropy Method (CEM) with

micro-simulation in the model.

• To test the performance of the model in a realistic

network.

Background

3

Study 4 Scenarios Objectives 2

1

DRACULA micro-simulation modelling

Get detailed information (delay, driving behavior, etc.)

to appraise performance of each signal timing.

Cross Entropy Method (CEM)

Start of stage 1

Start of stage 2

1

2 3

4

5 6

Distribution function

Select the best 5% solutions and update

parameters of distribution through minimizing the

Kullback–Leibler distance, which is equivalent

to the program:

𝑀𝑎𝑥𝐷 𝜇, 𝜎 = Max ln𝑝 𝑥𝑖; 𝜇, 𝜎

𝑖

Where 𝑥𝑖 represents the best 5% solutions.

Stop the iteration until the new values of

parameters are equal (or close enough) to the

previous one.

Solutions

Appraise Solutions

Micro-simulation model

Rank and select Update

Best Solution

Convergence

?

Develop a Matlab code to implement the CEM

model, providing input solutions to and taking

results from DRACULA.

Test the integrated model on a number of

representative and realistic networks, and

compare the results with standard signal timings.

Test the performance on modelling different

options (e.g. different CEM updating methods,

number of simulation runs)

Compare and contrast the restricts, draw

conclusions and write report.

Jialiang Guo - ml12j5g@leeds.ac.uk

MSc (Eng) Transport Planning and Engineering

Supervisor: Ronghui Liu May 2014

µ

σ

Roundabout

Signal timing generation

Generate a big sample of signal timings (say

1000) from a given distribution function 𝑝 𝑥; 𝜇, 𝜎

• Road pricing (tolling) dates back to the 17th century introduced

after the turnpike Act – 1663 in UK and to 18th century in the USA;

• Was predominantly used to raise funds for construction and

maintenance of highways;

• In recent times, tolling has been used for numerous reasons;

• Implemented in Singapore since 1975 as a traffic management

tool;

• In London, it has been used since 2003 to reduce congestion and

protect the environment;

• Norway, Sweden and Malaysia use road tolling to raise funds for

road transport budget support.

Road Pricing: A Case of Competing Private Road Toll Operators

The study intends to:

• Study techniques of identifying Nash Equilibrium for multiple toll

operators;

• Examine toll levels that ensure private operators maximise

revenue and meet the Nash Equilibrium conditions;

• Establish how revenue maximising tolls compare with social

welfare maximising tolls.

By Jonah Mumbya | Supervisor – Mr. Andrew Koh | Second Reader – Dr. Chandra Balijepalli

Introduction

Objectives

Motivation

Effects of Increasing Congestion Environmental Costs of Traffic Government Budget Constraints

• Global costs of congestion are high and

projected to increase with increasing

traffic delays;

• In UK, congestion costs (due to delay)

stood at £20bn in 2000 and projected to

increase to £30bn by 2020;

• NTM projects traffic to grow by 43% as a

result of a 66% GDP growth from 2010-

2040 in England alone;

• This would lead to congestion increasing

by 114% and lost seconds per mile would

increase by 36% hence cost as travel

speeds would reduce by 8%.

• Transport is the third largest

contributor to global warming

just behind energy (electricity

and heating) and industry;

• In UK, Road transport

contributed over 27% to

Green House Gasses with

cars having 58% of this in

2009;

• With increasing motorisation,

this is likely to be the same or

worse with time.

• Governments are continually

getting constrained to finance

road infrastructure using

traditional budget

appropriations;

• Hence road users ought to

meet part of the road

infrastructure investment

costs/budgets;

• Road pricing supports about

32% of Norway’s national road

system budget and 46% of

Spain's road budget.

Methodology

a) Link Selection;

Link selection shall be based on the difference

between link marginal cost and average cost and the

level of congestion of the do-nothing scenario.

b) Determine Tolls;

Iteratively, tolls will be set until a Nash Equilibrium is

achieved for the competing toll operators.

c) Traffic Assignment;

Using SATURN, traffic shall be assigned to the

network based on Wardrop’s first equilibrium

principle.

d) Calculation of Revenue and Benefits.

Based on assigned link flows from SATURN,

revenues and social benefits will be calculated.

Test Network – Edinburgh

1

2

3

4 5

Select Links

[SATURN]Set Tolls

Assign Traffic to

the Network

[SATURN]

Is Nash

Equilibrium

Achieved?

Calculate

Revenue and

Social

Benefits

No

Yes

INVESTMENT DECISIONS FOR RESILIENT TRANSPORT INFRASTRUCTURE:

A CASE STUDY OF THE DAWLISH RAILWAY LINE COLLAPSE

Kwame Nimako: MSc Transport Planning and Engineering (2013-2014) Supervisors: Prof. Greg Marsden and Prof. Nigel Wright

1. BACKGROUND

• A good transport system promotes the movement of people,

goods and services from one point to another under normal

conditions (Amdal and Swigart, 2010). A nation’s economic

vitality to a large extent depends on its transport network

(Amdal and Swigart, 2010).

• The occurrences of natural disasters such as flooding, make

transport networks such as railway lines vulnerable (Doll et

al., 2013), thereby impacting negatively on train services.

• For the disruption at Dawlish, the Train Operating Companies

will be paid £16 million by Network Rail for lost revenue over

the period (BBC, 2014).

• As the frequency and magnitude of such disruptive events

become more probable in future due to climate change, the

cost of providing engineering interventions required for

reliable transport services increases significantly.

• Since most transport infrastructure are long term assets (Doll

et al., 2013), there is the need for adequate investment

decisions on cost effective strategies to be employed to

enhance their resilience over their life span.

2. AIM

The aim of this dissertation is to develop a methodology to be

utilised in making cost-effective investment decisions to

improve the resilience of railway lines to disruptions.

3. OBJECTIVES

To achieve this aim, the following objectives have been set:

i. Understanding how demand for transport changes during

a major flooding event

ii. Estimating the impacts of the resultant disruption on

users of the infrastructure

iii. Collecting estimates of alternative flood risk mitigation

investments

iv. Developing a methodology to assess the cost-

effectiveness of such investments under different future

scenarios of flood risk

Great Western Rail line - London-Exeter-Dawlish-Plymouth-Penzance. (Source: First Great Western network map)

Location of Dawlish and the Railway line Disruption (Source: Google.com)

4. PROPOSED METHODOLOGY

5. EXPECTED OUTCOME

It is anticipated that this study will produce an Investment-

Frequency Matrix based on current and future scenarios of

disruptions to be utilised to improve the resilience of railway

lines.

6. REFERENCES

• Amdal, J.R. and Swigart, S.L. 2010. Resilient Transportation Systems

in a Post-Disaster Environment: A Case Study of Opportunities

Realized and Missed in the Greater New Orleans Region, 2010.

• Doll, C. et al. 2013. Adapting rail and road networks to weather

extremes: case studies for southern Germany and Austria. Natural

Hazards. pp.1-23.

• British Broadcasting Corporation. 2014. Storm-hit Dawlish rail line

compensation payout revealed. [Online]. [Accessed 28 April 2014].

Available from:http://www.bbc.co.uk/news/uk-england-devon-

27055780.

University business travel choices and University business travel choices and University business travel choices and working practices

Kanintuch Siripaibool Supervisor: Ann Jopson (ts13ks@leeds.ac.uk)

working practicesKanintuch Siripaibool Supervisor: Ann Jopson (ts13ks@leeds.ac.uk)

ObjectivesBackground

Objectives

• Find out the chosen / preferred travel mode choices of Nowadays there is an increasing in number of business

• Find out the chosen / preferred travel mode choices of

traveling;trips particularly for academic staff in the UK traveling to

European cities and beyond. Most of staff traveling by

traveling;

• Compare the choices e.g. air vs rail between UK and European cities and beyond. Most of staff traveling by

train or air depending on their preferences and time used.

• Compare the choices e.g. air vs rail between UK and

European cities;train or air depending on their preferences and time used. European cities;

• Discover the advantages / disadvantages of each The aim is to understand the academic travelers preferred choices and the reasons behind that, the activities they

• Discover the advantages / disadvantages of each

choice;choices and the reasons behind that, the activities they are doing while traveling and also how to reduce • Determine the activities they do while traveling and the are doing while traveling and also how to reduce environmental impact e.g. carbon emission (Carbon Management Plan, 2011).

influence to the chosen mode.Management Plan, 2011).

Business Travel

VS

Business Travel

• Average annual trips of educational staff (all modes) = VS

• Average annual trips of educational staff (all modes) =

50 trips (Wardman et al., 2013);

• 82% of business travelers said they spent some/most

of time working while travelling (Lyons et al, 2008).

MethodologyIn-vehicle VOT for Trip > 50km Methodology

• Mixed method questionnaire used in the study (mainly 35

In-vehicle VOT for Trip > 50km

• Mixed method questionnaire used in the study (mainly

qualitative) via online;25

30

• Tick-box questions for the first part of questionnaire e.g. 20

25

Background, preferred choices;15

20

£/hr

• Open-ended questions for opinions about choices,

advantages / disadvantages, etc. and some follow10

15

advantages / disadvantages, etc. and some follow

interviews for in-depth questions;0

5

interviews for in-depth questions;

• Find out the repeated similarities / themes in data

0

Car Bus Rail Air • Find out the repeated similarities / themes in data

collected.

Car Bus Rail Air

Norway’s 1997 data collected.Norway’s 1997 data

University business travel choices and University business travel choices and University business travel choices and working practices

Kanintuch Siripaibool Supervisor: Ann Jopson (ts13ks@leeds.ac.uk)

working practicesKanintuch Siripaibool Supervisor: Ann Jopson (ts13ks@leeds.ac.uk)

Scope

Find out the chosen / preferred travel mode choices of

Scope

• Focus on trips involved with meetings/conferences, Find out the chosen / preferred travel mode choices of

• Focus on trips involved with meetings/conferences,

away from usual working place;

Compare the choices e.g. air vs rail between UK and • Target only ITS staff, sample size: 25 – 30;

Compare the choices e.g. air vs rail between UK and • Travel choices between Leeds and key European cities

Discover the advantages / disadvantages of each e.g. Paris, Brussels, Amsterdam;

Rail network in EuropeDiscover the advantages / disadvantages of each

Rail network in Europe

Determine the activities they do while traveling and the

influence to the chosen mode.

VSVS

Source:Wikipedia (2011)

Initial Findings method questionnaire used in the study (mainly • Trips less than 500km air travel rarely used while trips

method questionnaire used in the study (mainly

more than 1000km air travel is predominate (Borken-

Kleefeld et al., 2013);box questions for the first part of questionnaire e.g.

Kleefeld et al., 2013);

• Business travelers have high in-vehicle VOT than Background, preferred choices;

• Business travelers have high in-vehicle VOT than

commuting and leisure travelers (Wardman, 2008);ended questions for opinions about choices,

advantages / disadvantages, etc. and some follow-up commuting and leisure travelers (Wardman, 2008);

• Business travelers have high willingness-to-pay advantages / disadvantages, etc. and some follow-up

depth questions; • Business travelers have high willingness-to-pay

(Carlsson, 1999);depth questions;

Find out the repeated similarities / themes in data (Carlsson, 1999);

• Business travelers are mostly time-restricted, don’t strive Find out the repeated similarities / themes in data

• Business travelers are mostly time-restricted, don’t strive

for low price (Jung and Yoo, 2013).for low price (Jung and Yoo, 2013).

Pedestrian Safety for the Visually Impaired and Elderly: Case Study: Leeds

Background

Aim

Scope

Pedestrians are often referred to as

vulnerable road users, borne out of the fact

that they comprise over 20% of those killed on

the roads(WHO, 2013).

The elderly and visually impaired pedestrians

are more vulnerable in this context as they

find it difficult to meander on the roadway as

other pedestrians do. They usually fear the

risk of being run over by vehicles coupled with

being endangered by other roadway hazards

like tripping to a fall on barriers like litter bins,

concrete and sign post. This results in low

level of confidence and hence suppressed

mobility.

This research aims at restoring confidence to the

visually impaired and elderly pedestrians on using

the roadway so they can get out more.

The survey method will be used where qualitative data will be

obtained from an in-depth face to face interview of 30 to 50

groups of visually impaired and elderly pedestrians using open

ended questions, which will reflect the recipients’ feelings

about their safety as pedestrians. Finally data will be analysed

using content analysis of descriptive and interpretative

measures where data will be sorted and classified into

similarities and differences, and finally summarised and

tabulated.

Methodology

The research is focused on the suppressed mobility of visually impaired and older

pedestrians within Leeds. it will seek to identify what makes them vulnerable, as well as

infrastructure and facilities available to encourage movement leading to their restored

confidence and enhanced safety.

Objectives

• To understand the extent to which the

visually impaired and the older people’s

mobility is suppressed, as compared with

the population as a whole.

• To establish whether there is a link between

suppressed mobility and the level of

confidence in using the pedestrian and built

environment; and if so, the nature of that

link.

• To explore possible ways of building

confidence amongst the visually impaired

and older people, leading to their enhanced

mobility.

By Jennifer Kuka Upuji

Student ID: 200749910

Supervisor: Mr Bryan Mathews

Implications

2 million people in the UK are living with sight

loss. These figures are expected to rise to over

2,250,000 in 2020 (Fight for Sight, 2014)

Zifotofsky et al,2009

http://Safetysigns-mn.com

Department for Transport,2013

Pedestrian Accidents- > 60yrs

Fhwa.dot.gov

www.bhatkallys.com

http://Clacksweb.org.uk

www.sensors/special_issues/vehicle-control

www.nei.nih.gov/eyedata/lowvision

http://srsc.org.sg

http://phillymotu.wordpress.com

Understanding travel behaviour to stadium and arena events : A Cardiff case study Laura Crank, MSc Transport Planning (FT)

Supervisor: Bryan Matthews

The importance of travelling to events

The car is the most popular mode of transport to stadium and arena events, and as the attendance of such events increases, it is noted, “…the demand for travel is heavily constrained both in time and space” (Robbins et al. 2007:303). High demand in a short space of time leads to congestion on the roads and overcrowding on public transport. Providing for such temporary ‘peak’ crowds would leave the additional infrastructure and services underused for the most part, which is economically unviable (ibid:304).

The story so far…

Obtained at least 150 responses between face-to-face and online surveys

Met with Cardiff Council’s Operations Manger of Major

Projects (Infrastructure) at one of the venues during an event. Managed to discuss issues at hand and how the council managed the city during large events

Methodology

Design survey taking into account aims and objectives. Test survey to ensure it is understandable and quick to complete

Sample a range of events to capture different socio-economic groups

Conduct face-to-face surveys at both Millennium

Stadium and Motorpoint Arena, and promote online survey to gain 200+ responses

Differentiate between results for each event,

establish differences in audiences, distances travelled, travel habits

Conclude what would need to be done to encourage modal shift to public transport

Aims and Objectives

To understand travel behaviour to events, developing on previous research at other stadiums and arenas

It is becoming a growing interest to understand the behaviour of spectators and to determine what changes would have to occur to the public transport system to increase attractiveness and modal share of public transport to events.

Research Questions What is the percentage of different modes of transport

used to access events in Cardiff?

What factors influence mode choice to events in Cardiff? Are there differences between mode choices to the two

venues? Are different event audiences more “sustainable” in

their travel choices, or willing to change their event travel habits?

What changes would have to be made to public transport

to increase its usage during events in Cardiff?

References Robbins, D. et al. 2007. Planning Transport with Special Events: A Conceptual

Framework and Future Agenda for Research. International Journal of Tourism Research. 9. Pp. 303-314.

Yeates, J. et al. 2009. Changing Travel Patterns of Arena Visitors: Transportation Demand Management for Urban Arenas and Stadia. [Online] Available at: http://www.ite.org/Membersonly/techconference/2009/CB09C3001.pdf [Accessed April 2014] Washington: Institute of Transport Engineers. Modal split before and after TDM measures

Location of venues and public transport stations in the vicinity of

the Cardiff region

A survey of stadium travel in New York found that after

transport demand measures (TDM) were implemented,

there was a decrease in car travel to events, whilst

public transport use increased.

BACKGROUND

Realistic driving behaviour models require detailed trajectory data for calibration; however, such data is very expensive to collect and process.

Spatial transferability of driving behaviour models leads to significant saving of costs and saves time for the new location.

RESEARCH QUESTIONS

What driving behaviour models could be developed for two contexts within the same geographical areas?

Which of the models would be recommended for transferability?

To use data from two sites to develop three different driving behaviour model structures.

To evaluate transferability of each of the three models using transferability scores.

To make recommendation on the best transferable driving behaviour model.

OBJECTIVES

STUDY LOCATION

SITE 2

Transferability of Gap-Acceptance Driving Behaviour Model Student: Ahabyona M. Evelyn. Supervisor: Dr. Charisma C. Choudhury. 2nd Reader: Dr. Ronghui Liu.

To test proximity effect on model transferability two are chosen from the same geographical area while the other data set is from a different geographical location.

Transferability of data sets will be analysed using the likelihood-ratio test methodology.

A micro-simulation tool (biogeme) will be used to predict the aggregate gap-acceptance behaviour of drivers.

METHODOLOGY POTENTIAL RISKS

Data being used may be out dated. Unrevealed factors such as bad weather conditions that

may have affected data collection. The data may not be detailed enough to capture all

aspects of gap-acceptance driving behaviour since it was collected for a different purpose.

A DRIVER CHANGING LANES

SITE 1

DATA COLLECTION

Understanding Free Bus Travel in West Yorkshire Using Smartcard Data

Context Methodology

• Temporal variation of trip frequency i.e. by time of day or day of the week.

• Influence of age on trip frequency.

• Spatial variation of trips frequency i.e. by location, with influence of income/level of deprivation.

Expected Findings

MSc Transport Planning Dissertation Mmoloki S. S. Baele e-mail: ts13msb@leeds.ac.uk

Scope and Objectives Scope

• The study covers concessionary bus travel for WY only.

• The focus is on after-scheme cross sectional travel data.

Objectives of Dissertation

• To determine the level of ENCTS pass use in West Yorkshire.

• To determine trip frequency patterns according to temporal, spatial and socio-demographic variations i.e. by day of the week, location, income group, age, gender and disability type.

• To evaluate value of smartcard data in determining travel patterns and the implications of the results on the policy for the ENCTS in West Yorkshire.

LITERATURE REVIEW

•Reviewing literature on concessionary bus travel, previous studies on smartcard data and complementary data sources.

•Determine the relevance of literature to the study.

DATA COLLECTION AND PREPARATION

•Deciding on relevant data types and variables.

•Extraction from WY Metro smartcard database – primary source.

•Downloading relevant complementary data (e.g. census and NTS data).

•Cleaning and organising data.

DATA ANALYSIS

•Defining trip data units of analysis and method of analysis from smartcard data.

•Determining trip frequency distribution and statistical analysis e.g. regression analysis.

•Comparison with other data sources (e.g. NTS).

INTERPRETATION OF RESULTS

• Identifying notable travel patterns.

•Evaluating the relevance of results to policy.

•Reflecting on the strengths and limitations of study and possible future areas of study.

West Yorkshire Concessionary Free Bus Travel

•West Yorkshire Passenger Transport Executive (WY Metro) issues smartcard type Concessionary Bus Travel passes as part of the English National Concessionary Travel Scheme (ENCTS).

Senior Pass

• Issued to permanent residents of West Yorkshire (WY); free off-peak bus travel for persons that have reached retirement age.

Blind and Disabled Passes

•Free travel on buses for blind persons at any time of day in West Yorkshire and off-peak throughout England and free, off-peak bus travel for disabled persons throughout England.

Companion Pass

•For persons accompanying eligible persons who are not able to travel alone.

67.3%

31.1%

1.6%

Proportion of Trips by Pass Type

Senior Disabled Companion

0

200

400

600

800

1000

1200

1400

Sun Mon Tue Wed Thu Fri Sat

No

. of

Trip

s

Average Weekly Trips - March 2014

Companion

Disabled

Senior

0 20 40 60 80 100 120

0.00

0.50

1.00

1.50

2.00

2.50

3.00

Passholders Age

Trip

s/p

ers

on

/we

ek

Mar’14 Average Trip Frequencies by Age

All pictures under WY Metro Copyright May 2014

2) Description of the Topic - Objectives

In the context of this survey, new stated preference data on mode choice are collected, with two components: one using current settings and one incorporating reducing car use & switching to public transport commuting instead. The work then investigates the performance of models estimated on the base scenario to predict the behaviour after the incentives have been brought in, and looks at the benefits that a treatment of attitudes brings in this context.

3) Scope of the Research

We investigate the response of the participants at 4 different scenarios -

policy interventions: •Increase in fuel price

•Increase in parking cost •Decrease in public transport cost (fare)

•Increase in the frequency of public transport

4) Methodology Statistical Model: Discrete Choice Modeling - Integrated

Choice & Latent Variable Model (ICVL)

We consider that the interviewees have 3 transportation options: drive & pay for parking, drive & search for free parking & use public transport. We give them eight current alternatives in which the attributes of the three options differ slightly. After that, we introduce 4 hypothetical policy interventions from their base scenario, where only one attribute changes every time. The individuals’ choices depend on the perceived utility of each option, which itself depends on the attributes of the specific alternative (access time, frequency, travel time, finding space, egress time, cost), the person’s socio-demographic characteristics (age, income, gender, education, area of residence, marital status), but also on the latent attitudes an of the individual (pro-intervention attitudes). Those attitudes explain his responses to the attitudinal questions (indicators I).

5) Data Collection: Survey - Stated Preference Data Collection – Devision of on-line Questionnaire

The questionnaire consists of four parts: • Existing Situation

•Stated Preference Part → Before/After Policy Intervention Choices

• Car/Public Transport Attributes & Attitudes • Socio - Demographic Characteristics

Sample: Staff of the University of Leeds, size of approximately 100 persons who commute by car.

On-line survey instead of face-to-face interviews

Indicators I → Responses to

attitudinal questions

Socio-demographic Characteristics of

interviewees

Utility of car & public transport

Attributes X of modes (car &

public transport)

Choice ↔ Probability of different scenarios

occurring

1) Introduction-Background

Nowadays, the challenge in transportation is to move to a more sustainable, environmentally friendly & economic way of commuting. To achieve this goal, incentives should be given to individuals in order to switch from car use to public transport. This dissertation aims to deepen in this issue, to investigate & quantify those incentives & examine all its parameters, as there are no retrospect surveys on this subject.

Marina T. Triampela, Stephane Hess

Institute for Transport Studies Faculty of Environment

1 INTRODUCTION

Transport Sector Contribute to GhG Emisssion According to the House of Commons Environmental Audit Committee,

“carbon emissions from transport since 1990 have moved spectacularly in the wrong direction – in marked contrast to other sectors”. Contribution of transport sector to total GhG emission increase to around 25%

Road transport carbon emissions proportion on the transport sector

emission is 75%-85%. Current CO2 emission of road transport are hovering at the same level as in 1990. Target of transport CO2 emission is 31% lower than 1990 base year by 2020 (CCC, 2013)

Road pricing could be alternative mitigation to reduce carbon emission further. But many of current schemes tend to focus on congestion as their primary objective rather than look at the joint problem of tolling for congestion and emission.

How to Calculate Carbon Emission? • DfT Methods (DfT, 2011) Where:

Ce= carbon emission, L=Fuel consumption, Ceβ= Carbon emission per litres burnt, V= Average speed and a,b,c,d= Parameters based on “New UK Road

Vehicle Emission Factors Database”

• Alternative Models (Shepherd, 2008) 1. Simple fixed rate Apply average speed of network into equation of complex emission model below, and get constant addition of CO2 per trips 2. Complex emission models

Where: g = CO2 emitted, V = average speed of link-based Then carbon emission can be monetized by multiply it with £70 per tonne of carbon emitted

Cordon Pricing

4 METHODOLOGY

Researcher: Naf’an Arifian (ml12n2aa@leeds.ac.uk) --MSc. Transport Planning

Supervisor: Simon Shepherd (S.P.Shepeherd@its.leeds.ac.uk)

Second Reader: Dave Milne (D.S.Milne@its/leeds.ac.uk)

How Effective are Cordon and Distance-Based Pricing at Reducing CO2 Emission?

2 OBJECTIVES 1. To investigate welfare benefits of road pricing schemes taking into

account CO2 emission and congestion 2. To investigate differentiation between simple fixed rate emission model

and complex emission model dependent-speed. 3. To investigate impacts of cordon pricing and distance based pricing

policies on reducing CO2 emission

Build Networks on SATURN

OD matrix, road networks data and demand elasticity

Develop Road Pricing Schemes

Charges all-links (first best pricing adjust to include CO2 emission cost), Cordon and

distance-based pricing by modified ‘generalized cost’ equation of SATURN

Run SATURN and Record Outputs

Link-based speed and flows

Calculate and Investigate

Emission cost of simple vs complex emission models, Congestion and emission cost of cordon vs distance-

based pricing

Comparison between Scenarios with First-best Pricing

In terms of congestion cost, emission cost and welfare benefit

What is Road Pricing? •Road pricing are direct charges for the use of roads, including road

tolls, distance or based time charges, congestion charges particularly to discourage use of certain class of vehicles, fuel sources or more polluting vehicles.

1. First-best pricing; charges on all links in order to achieve maximum social welfare

2. Second-best pricing; under constraints to find optimum toll in order to achieve maximum welfare

Ce= L*Ceβ

3 LITERATURE REVIEW

Calculate and Investigate

Welfare benefit of Cordon vs Distance-based pricing (by Plotting optimal toll take account (i) congestion,

(ii) congestion + CO2 emission simple fixed rate model and (iii) congestion + Co2 emission complex model)

Source: Shepherd (2008)

Source: DECC (2014)

Source: Shepherd et.al (2008)

1. Committee on Climate Change (CCC). 2013. Fourth carbon budget review –

technical report : Sectoral analysis of the cost-effective path to the 2050 target 2. Department for Transport (DfT). 2011. The greenhouse gasses sub-objective:

TAG 3.3.5 3. Department of Energy and Climate Change (DECC). 2014. Total greenhouse gass

emission from transport 4. Shepherd, S. 2008. The effect of complex models of externalities on estimated

optimal tolls 5. Shepherd, S., May. A., and Koh. A. 2008. How to design effective road pricing

cordons

References

www.kliaekspres.com

www.ktmkomuter.com.my

www.ktmb.com.my

www.prasarana.com.my

www.spad.gov.my

• Population ~29m • Capital: Kuala Lumpur • GDP per capita: ~10k USD • Age group [16-64] expanding Kuala Lumpur Metropolitan Area (KLMA)

www.wikimedia.com

• Rapid motorization rate – Car Ownership at ~320 cars / 1000 per. • Fuel subsidized – Oil producing country - Approved Price / Floating Price Mechanism : Pump price lower/equal than actual market value. • Train Services: Intercity and Urban (KLMA) available

Components Description

Key Question

Deduction of constant elasticity Method:

Urban: Trips Made

Intercity: Kilometer-Passenger

Urban: Trips Made

Intercity: Kilometer-Passenger

Online Survey - Considering type of trip, gender, income, location. Dispersion question: Given a situation is that the fuel price has gone very high and is no longer affordable. If you had to make a leisure trip (visiting family/friends, holiday etc) . If petrol prices are too high, how do you travel? A) Train B)Bus

Key Question: Deduction of the cross elasticity of rail demand with respect of pump car-fuel price.

Area and Mode Area of study will be in Malaysia (East and West). Related modes: Train and Cars.

Impact of Car Petrol Prices on Rail Demand: The Case of Malaysia Nik Mohd Rafiq Bin Wan Ibrahim MSc. (Eng.) Transport Planning and Engineering ts13nmrb@leeds.ac.uk

Supervisor: Prof. Mark Wardman Institute for Transport, University of Leeds

Quick Overview: Deduction of the cross-elasticity of rail passenger demand in Malaysia with respects to car petrol prices.

TrainCar

rainRidershipT

arRidershipC

iceCarFuelCariceCarFuelRailV

V,Pr,Pr, ||

1. Situation Appraisal 3. Scope 5. Methodology / Data Collection

4. Literature 2. Motivation

iceCarFuelRail Pr,

iceCarFuelCar Pr,

rainRidershipTV

arRidershipCV

TrainCar,

Urban Rail (Kuala Lumpur

Metropolitan Area) Nationwide

Intercity Rail (Nationwide)

• Interesting to see in a fuel-subsidized country, where the actual transport fuel cost is not paid by the user, would there be any significant mode shift.

• As oppose to EU or UK, whereby taxation of fuel is a national income stream, the effects of increase modal-shift to trains (public transport in general) in countries where fuel are “costly” to the nation, would be a point of interest.

• Trending projects to improve in urban rail especially in KLMA and intercity rail services (Double Track, HSR)

• Subsidy will be removed?

Acutt and Dodgson 1996, Cross elasticities of demand for travel, Transport Policy Vol 2, pp. 271-277

Train Type Year Cross Elasticity w.r.t car-fuel price

InterCity, NSE, Regional

1992/93 0.041 – 0.094

Underground 1992/93 0.017

Train Type Year Cross Elasticity w.r.t car-cost

Inter Urban - 0.59

Urban - 0.25

Paullney et al 2006, The demand for public transport: The effects of fares. Quality service and car ownership, Transport Policy, 13(4), pp. 295-306

40.0

50.0

60.0

70.0

80.0

90.0

100.0

110.0

120.0

130.0

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

Consumer Price Index(2000 = 100) (SourceDOSM)

0

2000

4000

6000

8000

10000

12000

14000

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

Retail Fuel Sold [millionliters] (RON97 + RON95)(Source KPDNKK)

0

20000000

40000000

60000000

80000000

100000000

120000000

140000000

160000000

180000000

200000000

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

01

20

03

20

05

20

07

20

09

20

11

Passenger Ridership UrbanRail (Source: EPU)

0

200000000

400000000

600000000

800000000

1E+09

1.2E+09

1.4E+09

1.6E+09

1.8E+09

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

Railways, passengerscarried (passenger-km)[Source: DOSM]

MALAYSIA

Objective: To investigate the relationship of car fuel price to rail demand. Increasing of car fuel price (either due to global market or government’s removal of the subsidy) should hypothetically increase train patronage.

1. Literature Review on global PED

2. On-line Survey: Stated Preference

3. Regression from existing data:

Cross elasticity is calculated using an equation that consists of several components. These components are partly review from literature to compare other international values followed by a re-calculation using method described below. A crucial part of this study is an online survey to deduce the diversion factor; non-payable option of cost car-fuel is hypothesized.

(Source: DOSM) 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

19

80

19

84

19

88

19

92

19

96

20

00

20

04

20

08

20

12

Pumpprice forgasoline(US$ perliter)(Source:WorldBank)

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

0

500

1000

1500

2000

2500

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

Length of railway track (km)(Source: DOSM)Roads, total network (km)(Source: World Bank)

Trac

k (k

m)

Ro

ad N

etw

ork

(km

)

-

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

19

80

19

86

19

92

19

98

20

04

20

10

Cars

Motorcycle

Other Vehicles

Goods Vehicle

Bus

Taxi and PrivateHire

Mileage travelled Deduction from Retails Fuel Sold – considering fractioning to cars and yearly fuel consumption, calibrated with VehicleKilometerTravelled (recent Survey)

Pump Fuel Price Considering Consumer Price Index

Registered Vehicles over time

Background

How Good the SATURN Model Representing the Network Impact of Lendal Bridge Closing Trial?

Objective

Scope of Study • Study area confined by the

ring road (A64 and A1237) • Access crossing Ouse River

A. Rawcliffe Bridge B. Clifton Bridge C. Lendal Bridge D. Ouse Bridge E. Skeldergate Bridge F. A64

Data • Survey Manual Classified Counts Survey has been conducted every autumn (September, October, November) Record bridge crossings activity for 12 hours (07:00-19:00) Data available motorised vehicle, bicycles and pedestrian However exact survey location is unknown

• Automatic Traffic Count Data ATC location spread around the study area Record vehicle, cycle and car park

• Network model and Trip Matrix of York City • Journey time data from Trafficmaster • However, data only limited to traffic count and journey time without specific

detail to each vehicle (GPS or Number Plate)

• Lendal Bridge Closing Trial 27th August 2013 until 26th February 2014. 10:30 until 17:00 closure limited to cars, lorries and motorbike (www.york.gov.uk/)

• SATURN (Simulation and Assignment of Traffic to Urban Road Networks) Transportation network analysis program developed at Institute for

Transport Studies, University of Leeds Generalised Cost, 𝐶 = 𝛼𝑇 + 𝛽𝐷 (α = PPM and β = PPK) while T and D

denotes travel time and distance is the basic principle of route choice Combined assignment and simulation model

Methodology Data Description

Pujas Bakdirespati ts13plb@leeds.ac.uk (200792978) MSc. Transport Planning and Engineering

0100200300400500600

North 2012 North 2013

24 Hour Bridge Traffic Load Proportion

Lendal Bridge Traffic Flow

ATC Data Difference 2013 to 2012

Before the Closing

Difference

York Network

Supervisor: Dr. David Milne

21.91%

11.46%

7.29% 8.83%

13.74%

36.77% 22.08

%

12.00%

4.94%

7.87%

14.65%

38.44%

22.81%

12.51%

3.47% 8.15%

15.57%

37.49%

21.25%

11.42%

6.63%

7.56%

13.59%

39.54%

Rawcliffe Bridge

Clifton Bridge

Lendal Bridge

Ouse Bridge

Skeldergate Bridge

A64

2012

2013

Bridge Open

Bridge Closed

-16.00%

-11.00%

-6.00%

-1.00%

4.00%

9.00%

14.00%

19.00%

Direction 1 Direction 2

-16.00%

-11.00%

-6.00%

-1.00%

4.00%

9.00%

14.00%

19.00%

Direction 1 Direction 2

0100200300400500600

South 2012 South 2013

Lendal Bridge Select Link Assignment

11:00-17:00 Peak

• To compare route choice between the result of SATURN model and observation data, which in this case the comparison could be divided in two steps: Route choice before the closing of Lendal Bridge trial, where the SATURN model will be calibrated with journey time and flow

data in order to better reflect the condition Route choice after the closing of Lendal Bridge trial, where the SATURN model is slightly modified at Lendal bridge where it is

banned in two way with exemptions of bus, while during the closing condition could be interpreted as evolving condition throughout the time

• By using 𝛼

𝛽 indicates ratio of PPM and PPK without knowing the value of both which believed to be appropriate as variable to

conduct sensitivity testing to calibrate the model

Comparison of Traffic Flow and Travel Time

Between Forecast and Observation Data

The first step of research methodology will focused on the route choice at before closing condition with available SATURN model. Subsequently, will be compared with the real condition of route choice which acquire from link flows and travel time data to find a better value of ratio

𝛼

𝛽. There are possibilities of step repetition

as we would calculate the correct ratio for SATURN model.

Second step similar with the first step however, the SATURN model is completely new with slight modifications made at the network.

Consequently, the last step of research is to compare the route choice in both condition and made analysis on the main findings

There is possibility of data collection to support hypothesis in findings

Secondary Data

Step 1

York City

Network Before

the Trial

O-D Matrix of

York City

SATURN Software

Forecast of Traffic Flow Each Link

Step 2

York City

Network After

the Trial

O-D Matrix of

York City

SATURN Software

Forecast of Traffic Flow Each Link

Comparison of Traffic Flow and Travel Time

Between Forecast and Observation Data

Main Findings

Comparison of Route Choice Between

Forecast and Observation Data

DISSERTATION POSTER Topic Implementation of National Urban Transport Policy of India 2006:

Progress and Prospects in improving Public Transport, By: Paulose N Kuriakose 200745484Progress and Prospects in improving Public Transport, By: Paulose N Kuriakose 200745484

Ex-post versus Ex-ante Appraisal of High Speed Train Projects:

Case Study on Ex-post Analysis of Turkey HST Projects Seher Demirel Kutukcu, (MA) Transport Economics

Supervisor: Dr James Laird

Feasility studies of the projects

Passenger numbers, fares, time savings

Operation and maintanence costs

Before and after mode shares

Load factors, delays in service (Reliability)

Realized construction costs

Conventional Appraisal Topics: WebTag

Guidelines and Worldbank

(Elasticity of demand for Ankara-Konya: literature)

( Value of time for both projects: literature )

Wider Social Impacts: TAG unit A2-1 and

NCHRP spreadsheet tool

Impacts on GDP: Literature

Answering following questions:

Do HSR projects in Turkey provide social benefits?

Does it worth working on wider impacts considering their complexity?

Are there good applications in the world?

Is there any optimism bias in the case study Cost Benefit Analyses

(CBA) and what are the reasons for deviations?

Have case study project objectives been achieved? If not why not?

How can ex post cost benefit analyses (CBA) contribute to the

practice of ex ante cost-benefit analysis?

2. Background

1. Motivation Huge investments to HSR projects

Need for prioritization of projects

Problems in monetizing all benefits and costs

Increasing investments in railway sector in Turkey

No ex-post transport infrastructure appraisal in

Turkey yet

Utilization of EU funds require ex-post analysis

Appraisal systems: UK Transport (WebTag),

European Commission (DG Regio and DG

Mobility and Transport), EIB, Worldbank (for low

and mid-income countries)

No standard definition on HSR.

Available case studies in Turkey with accessible

data ( Ankara-Eskisehir and Ankara-Konya)

Ankara-Eskisehir

Allocations to Railways in Turkey (Million TL)

Mode Shares Before and After Ankara-Eskisehir HSR Project

4. Scope and Methodology

3. Objectives

Case Study HSR Lines and Connected Cities

Ankara-Eshisehir Realized Monthly Passenger Numbers

1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

Ankara-Konya Realized Monthly Passenger Numbers

5. Data

Prepared by : Gethin Shaji

Supervisor : Jeremy Shires

Co-Supervisor : Prof. Mark Wardman

BACKGROUND

• Desirable change in patronage can be achieved

through bus design changes.

• Like soft factors, impact of bus design on

patronage is hard to measure and quantify.

• Route 72 bus runs along Leeds-Bradford corridor.

• Other bus services along this corridor are:

(1) X6

(2) X11

(3) X14/14

(4) 508

• The Leeds City Region (LCR) generates 140,000

road trips daily.

• The modal split along this corridor is as follows:

Car – 68% Bus – 19% Train – 3%

• In Oct. 2012, Hyperlink bus service was introduced

along this corridor.

• Key features of the Hyperlink buses are:

(1) Tram like design

(2) Modern leather seats with new seating

layout

(3) Free Wi-Fi facility

(4) On-board Real Time Information

(5) On-board host based ticketing

ROUTE MAP

OBJECTIVES

• Examine and analyse ticket sales data to observe

any quantifiable relation with bus design

• Estimate the impact of bus design on

(1) Existing bus services

(2) Generational effect

(3) Passenger behaviour

• Observe relation between bus design improvement

and change in demand

METHODOLOGY

DATA COLLECTION

• Time period : 2011-2014

• The following buses are considered as they share

part of the Leeds-Bradford corridor:

(1) H72

(2) X6

(3) X11

(4) X14/14

(5) 508

• The following data is to be collected:

(1) Changes in endogenous variables ,

e.g. fares, frequencies, journey times etc.

(2) Changes in exogenous variables,

e.g. the economy etc.

(3) Passenger behaviour data from surveys

ISSUES AND EXPECTED FINDINGS

1 • Historical analysis of bus routes from 2011 along

the Route 72 corridor

2 • Analysis of ticket sales data for the buses

competing along this corridor before and after the introduction of Hyperlink service

3 • Identify different demand impacts :

(1) General trends

(2) Abstraction from other bus services

(3) Generation, e.g. new and existing bus users

4 • Analyse existing survey data and conduct new

passenger survey to help understand these trends and passenger behaviour

5 • Attribute changes in demand to changes in bus

design

• Issues :

(1) How comprehensive is the ticket data ?

(2) How stable have the bus services/routes been

since 2011 ?

(3) How detailed are existing passenger surveys ?

• Expected findings :

(1) Small rise in patronage through generation

(2) Small to medium rise in patronage through

abstraction

BUS DESIGN AND IMPACT ON DEMAND

Background

Objectives

Methodology

• Identify current modelling/microsimulation approaches toshared space particularly interactions between motorvehicles and pedestrians

• Assess a shared space option for the Shipley junction andimprove the parameters in the current vehicle andpedestrian microsimulation model with the help of collecteddata to test, verify and validate the model

• Assess the emission levels of the Shipley junction using theemissions model, and compare this with the signalisedoption for the Shipley junction

• Develop general guidance that can be provided for modellingShared Space

 

By Samuel Oswald, cn10so@leeds.ac.uk

Current ModelReferences

No Road Markings

Emissions modelling

The emissions model being used PHEM is a comprehensivepower instantaneous model with can simulate fuelconsumption and various emissions such as NOx, ParticulateMass (PM10), Carbon monoxide etc. for cars and light vehiclessecond-by-second.

The model requires 1Hz speed data, road gradient and thevehicle specification. In this case the speed data and vehiclespecifications will be taken form the AIMSUN model and roadgradient form Google Earth terrain data. The AIMSUN modelwill also be used to predict the proportion of emissionscontributed by each vehicle type (Tate, 2013).

Hans Monderman first pioneered the concept of shared space,whereby removing traffic lights, signs, crossings, road markingsand even curbs. Pedestrians, motorists and cyclists are requiredto negotiate their way through streets by reacting with oneanother (Projects for Public Spaces, 2002).

These types of schemes have been implemented worldwide,with schemes becoming increasingly popular in the UK.

In 2011 the Department for Transport issued a Local TransportNote to aid with the design of the these types of schemeshowever the guidance contains minimal advice onmicrosimulation/modelling. Yet clients are increasingly askingfor models to prove designs work.

There has also been relatively little research into whether thistype of scheme has different affects on emissions compared toother junctions types.

No Traffic SignsNo Raised Curbs

AN INVESTIGATION INTO THE CURRENT TECHNIQUES USED TO MICROSIMULATE SHAREDSPACES AND THE IMPACT OF SHARED SPACES ON EMISSION LEVELS

Resize currentmodel to the

corridor required

Validate theresized model bycomparing GEH

Calibration datafrom old model

Ensure GEHanalysis meetsDepartment for

Transportguidelines

Assess the vehicletrajectories anddynamics in the

model

Compare the twosets of data

Adjust the modelparameters to

mimic the real lifedata

Collectreallifetrajectoryanddynamicsdatausingvehicle

tracking

Clear model of allcurbs road

markings andcrossings

Evaluate howthese vehicle

movements canbe applied to thejunction layout

Study currentshared space toascertain vehiclemovements

Adjust the modelto representshared space

Calibrating the Base model

Vehicle Dynamics

Run AIMSUNwith 0.5 timestep withoutLEGION

Run PHEM model for shared space model

and signalised base model

Run AIMSUN with0.6 step with

LEGION Google EarthTerrain data

Interpolate dataso it is in 1Hzform for PHEM

Coding the Shared space option Figure 3: Time series plots of PHEM result for Euro 5 Bendy-Bus

Projects for Public space (2002) Hans Monderman, Availableonline: http://www.pps.org/reference/hans-monderman/

J. Tate (2013) Project Report: York Low Emission ZoneFeasibility Study – Vehicle Emission Modelling

Figure 2: Example of Shared space

Figure 1: Shipley AIMSUN with LEGION Base model

Price elasticity of demand in suburban railways in

India – A case study of four cities Syed Abdul Rahman, (MA) Transport Economics

Supervisor: Dr Narasimha Balijepalli

• Importance of Suburban railways in India in passenger

transportation.

• Significance of passenger demand forecasting in transport

planning, service provision and infrastructure development

• Key role of elasticity in forecasting demand

• Lack of economic rationale in policy decisions

Complete government ownership

High population growth

High growth in car-ownership

Fiscal pressure on Governments

Need to attract private investments

MRTS programs in the major cities

Dynamic pricing in Indian railways

4.Scope • Four biggest cities of India: Mumbai, Chennai,

Delhi and Kolkata

• Time series data of 25 years

5. Data • Panel data (4 cities, 25 years)

• Originating passengers, vehicle KM and fare per

passenger per KM from 1988 to 2012

• Data on population, income and fuel costs

6.Methodology Concept and different methods of elasticity

Decide on demand estimation models

Time series regression on each individual city

Linear regression on 4 cities using panel data

Comparison of individual estimates with

combined estimates and other studies

Evaluation of available forecasts of a city and

assessment of impacts on policies regarding

infrastructure development

7. Risks a) Omitted variables

b) Lack of studies on developing countries

c) Income Vs suburban rail journey

8.References Balcombe R. et al., (2004), The Demand for Public Transport: A

practical Guide, TRL Report, TRL593

3.Objectives/Output To estimate fare elasticity of suburban rail passengers

To compare the estimated elasticity with other studies.

To suggest possible uses of elasticity values (i.e. demand forecasting)

To critically assess perspective plan of one city

To highlight possible policy implications

To provide input to infrastructure planning

Demand model:

α = constant; β = fare elasticity; γ = elasticity of population, income and fuel costs; δ =

sensitivity of current demand with respect to demand in t-1

V = passenger km (dependent variable)

F = Fare

Z = Vector of population, income and fuel costs of the cities

i = city; and t = time

1. Motivation

2.Background

Mumbai Chennai

• The construction of roads could be traced

to 312 BC when Romans built roads to

connect major cities within their empire.

These roads were regularly maintained.

• Pavement rehabilitation and construction

are very expensive and do not create room

for trials.

• Pavement evaluation provides information

to ascertain the type of maintenance

activity to be carried out.

An Examination and Application of Flexible Road Pavement Evaluation Methods:

A review of the methods and application to roads in Sierra Leone

1. Background

2. Objectives • To identify relationships between the

evaluation methods of flexible pavement.

• To locate and determine the seriousness of

the pavement distresses on the selected

roads.

• To determine possible structural surveys

and/or laboratory tests to be carried out.

• To ascertain the ‘present serviceability

rating (PSR)’ and the ‘pavement condition

score (PCS)’ for the selected roads.

Transverse Crack Longitudinal Crack

Rutting Patches

Alligator Cracking

Pothole

7. Methodology

Pavement Distresses on roads

• The present serviceability index would give an indication

of the ride quality of the road.

• The present condition score would indicate the extent and severity of the distresses on the road.

4. Why determine ‘PSR’ and ‘PCS’ ?

5. Selected Roads

By Serrie Henry Willoughby MSc(Eng) Transport Planning and Engineering Mr. David Rockliff (Supervisor), Prof. Anthony Whiteing (2nd Reader )

• A comparison between the UK methods and that

specified in the oversees ‘Road Note 18’ for tropical

countries would be considered in this work.

• The PSR would be obtained from highways engineers,

commercial and private drivers.

3. Scope

The relationship between the different types of evaluation method shall be obtained by review of literature.

The positions of distresses shall be obtained by the use of a hand held GPS device and wheel meter.

The pavement serviceability index shall be assessed by

driving through the road and rating the ride quality on a scale

of 0 – 5: were ‘0’ would indicate very poor and ‘5’ would mean

very good.

The pavement condition rating would be obtained by noting

the extent and severity of distresses and calculating the

index using the equations in the FHWD manual as outlined

below:

PCR = 100 - [(100 - AC_INDEX) + (100 - LC_INDEX) + (100 - TC_INDEX) + (100 -PATCH_INDEX) + (100 - RUT_INDEX)] • Scale: POOR (<=60), FAIR (61 - 84), GOOD (85 - 94) , EXCELLENT (95 - 100

The selected roads are Old railway

line , Pademba road and Benjamin

lane in Freetown, Sierra Leone.

Original Pavement Design and Construction Records

Analysis Establish Probable

Causes of Pavement Distresses Observed Pavement

Distresses

Field Tests Surface Condition

Roughness Deflection

Frictional Resistance

Field Samples Laboratory

Evaluation Rehabilitation

Alternatives Based on Pavement Design

Principles

Background Maintenance Performance Geometrics

Environment Traffic

Economics

Thin/ Thick Overlay

Inlay

Recycling

Reconstruction

Pavement Evaluation

Performance Condition Safety

Structural Material

6. Evaluation for Rehabilitation

Location Referencing

The  hypotheses  

Open  access  operators  exhibit  the  same  cost  

characteris1cs  as  franchised  operators  

Open  access  operators  have  lower  input  costs  than  franchised  

operators  

Open  access  operators  have  lower  staff  costs  than  franchised  

operators  

Scale  and  density  effects  dominates  lower  input  costs  

The  background  

Data  

Open  access   Franchises  

Scale  effects?  

Density  effects  

Other  efficiency  gains?  

Lower  input  costs?  

The  objec?ve  The  paper  will  seek  to  answer  two  major  ques1ons:  •  Does  open  access  operators  face  a  different  cost  

func?on    from  that  of  franchised  operators?  and  

•  Does   other   efficiency   gains   and/or   lower   input  costs  outweigh  the  scale  and  density  effects?  

The   separa1on  of   rail   infrastructure   and   train  opera1ons,   is  making   the  passenger  rail  market  less  monopolis1c.  Governments  can  open  for  either  compe11on-­‐for-­‐the-­‐market   or   compe11on-­‐in-­‐the-­‐market.   The   former   is  through  franchise  or  tender  compe11ons  for  a  train  opera1on  monopoly.  In   the   laFer   model,   companies   run   services   on   their   own   risk   and   in  compe11on  with  each  other.    Research   on   franchised   passenger   rail   opera1ons   done   in   the   UK   by  Wheat  and  Smith   (2014)  has   shown   that   there  are   strong  economies  of  density   (number  of   trains   run  per  kilometre  of   track)  and   in  some  cases  significant  scale  effects  (route  network  size)  in  the  industry.  However,  this  research  did  not  account  for  open  access  opera1ons.    With  a  growing  market   for   such  opera1ons   in  Europe,   it   is   important   to  establish  whether  open  access  opera1ons  exhibit  different  characteris1cs  from  franchised  operators.  The  answer  may  provide  a  guide   to  whether  head  on  open  access  compe11on  will  bring  benefits,  or   if  we  are  beFer  off  following  the  UK  prac1ce  of  only  allowing  non-­‐abstrac1ve  services.    

United  Kingdom  –  non-­‐abstrac1ve  

services  since  2000  

Sweden  –  head  on  compe11on  since  

2010,  major  increase  expected  in  2015  

Czech  Republic  –  head  on  compe11on  

since  2011  

Interna1onal  open  access  opera1ons  liberalised  in  2010  

Austria  –  head  on  compe11on  since  

2011  

Italy  –  head  on  compe11on  since  2012  

Germany  –  head  on  compe11on  since  

2012\  

Dates   determined   by   start   of   first  services   as   recorded   by   Railway  GazeFe   Interna1onal   online.   Flags  sourced  from  na1onalflaggen.de  

Current  open  access  opera?ons  

Econometric  analysis  of  open  access  railway  operators’  compe??veness  

Data  • Collect  and  structure  data  for  use  in  model.  

• Need  to  be  to  the  same  format  as  data  used  in  earlier  research  to  ensure  comparability  

Model  • Using  hedonis9c  cost  model  for  heterogeneous  outputs  developed  by  Wheat  and  Smith  (2014)  

• This  will  provide  me  with  a  cost  func9on  for  open  access  operators  

Tes?ng  • Is  the  cost  func9on  for  open  access  operators  different  from  that  of  comparable  franchised  operators?  

• If  using  the  input/staff  costs  of  franchises,  would  the  costs  of  open  access  increase?  

• Does  the  lower  input  costs  make  up  for  the  change  in  scale  and  density  effects?  

The  Methodology  

Due   to   open   access   operators   only   being   responsible   for   a  minor   frac1on  of   the   traffic  on   the  Bri1sh  network,   there   is  a  risk   that   size   and   form   of   the   sample   will   limit   the  transferability   of   the   results.   However,   the   results   should  indicate   whether   there   are   structural   cost   advantages   to   be  exploited,  as  well  as  formalising  the  cost  structure  of  small  rail  operators  in  the  United  Kingdom.  

Risks  

By  Tørris  Rasmussen      Supervisor:  Phil  Wheat  

Output   metrics   such   as   train   hours,   train   kilometres   and   route  kilometres,  and  input  metrics  such  as  vehicles  per  train,  vehicle  type  and  top  speed  will  be  sourced  from  1metable  data  from  the  Department  for  Transport.  The  minimum  require  data  has  been  secured.    Cost   input   data   will   be   sourced   from   respec1ve   companies’   annual  accounts  posted    with  Companies  House  and  publicly  available.  

Alexandersson,  G.,  2010.  The  Accidental  Deregula9on  -­‐  Essays  on  Reforms  in  Swedish  Bus  and  Rail  Industries  1979-­‐2009.  Ph.D  thesis:  University  of  Stockholm.    Wheat,  P.  &  Smith,  A.,  2014  Forthcoming.  Do  the  usual  results  of  railway  returns  to  scale  and  density  hold  in  the  case  of  heterogeneity  in  outputs:  A  hedonic  cost  func9on  approach.  Journal  of  Transport  Economics  and  Policy    MVA   Consultancy,   2011.   Modelling   the   Impacts   of   Increased   On-­‐Rail   Compe99on   Through  Open  Access  Opera9on,  London:  MVA  Consultancy.  

Major  sources  consulted  

Sustainable Aviation: Evolution of China’s Aviation Industry (Markets and Fleet) STUDENT: Wendy Dzifa Wemakor

SUPERVISORS: Professor Andrew L. Heyes & Dr Zia Wadud

Introduction

Currently, China’s domestic fleet is made up of two aircraft types , turboprops and regional jets. There are a total of 725 flight routes of which 655 flights are served by regional jets and 70 by turboprops (Ryerson and Ge,2014). As cities and personal wealth increases, so will the intensity of human interactions with the result that there will be and increase in demand for air travel. The aircraft will have to grow and evolve to meet the need.

Objectives China’s aviation industry is a significant contributor to its economy. In 2010, the industry contributed about 1% of total GDP and 296 million passengers and 11 million metric tons of freight travelled to, within and from China (IATA,2012).Tremendous growth in China’s aviation industry is expected by 2020 and will take the highest value of aircraft deliveries in the period as predicted by Boeing and Airbus, the world’s largest manufacturers of aircrafts.

To develop a representative model of the development of air travel in China in order to • assess the world fraction of China’s aviation

market and how it will change over the next two decades.

• determine the optimum aircraft fleet to meet China’s air travel demand in the most energy efficient way.

• compare air travel between China’s cities with the High Speed Rail from an energy perspective

The demand Dij between cities i and j is calculated using a simple one-equation gravity model of the form

𝐷𝑒𝑚𝑎𝑛𝑑 𝐷𝑖𝑗

= 𝐾 ∗ 𝑝𝑜𝑝𝑖𝑝𝑜𝑝𝑗𝛼∗ 𝐺𝐷𝑃𝑖𝐺𝐷𝑃𝑗

𝛽∗ 𝑐𝑜𝑠𝑡𝑖𝑗

𝛾

The forecast will help determine fleet type to meet demand from an energy perspective.

Methodology

Analysis of Air Travel Demand Model

Optimization of Fleet Type

Comparison of Results

Air Travel and HSR

Discussions and Conclusion

Gravity Demand Model

Optimization of Fleet to Meet China’s Demand

Expected Output

Regional Jet / HSR/ Turboprop?

It is hoped the study will allow conclude whether or not aviation is the optimum way to link China’s vast growing cities and how it can be integrated with other transport modes like the high speed rail to form a sustainable transport system

What w

ill it be?

2013/14

Figure 1: Aircraft demand in global aviation market (Airbus, 2012)

Reference • Airbus. 2012. Global Market Forecast 2012-2031. • International Air Transport Association. 2012. Special

report: Chinese aviation. A New Era in Aviation. • Ryerson, M. S. and Ge, X. 2014. The role of turboprops

in China’s growing aviation system. Journal of Transport Geography.

How Advanced Ticket Purchase Influences

Yield Management In Rail Market?

Yield management originated with the deregulation of the US

airline industry in the late 1970s.

Railroads have offered a limited number of tickets online at a

discounted price in advance.

•The PEP system offered base fares as well as early booking

discounts of 40%, 25%, or 10%.

BACKGROUND

To investigate yield management in rail market;

To investigate what impact risk has on the decision whether to

purchase a ticket in advance or not;

To help understand how to maximise the revenue of rail

companies and how this conflicts with the benefits for

passengers;

To calibrate the existing PRAISE model in modelling advanced

ticket purchase to be more realistic.

OBJECTIVES

METHODOLOGY

Analyse the Value of

Advanced Ticket Purchase Analyse the disadvantages

Reveal economic benefits

Collect the ticket prices online for

One and Two months in advance

Existing PRAISE Model:

Demand Model Structure 𝑷𝒕𝒊𝒄𝒌𝒆𝒕 = 𝑷′𝒓𝒂𝒊𝒍𝑷𝒕𝒊𝒄𝒌𝒆𝒕|𝒓𝒂𝒊𝒍

Multinomial Logit Model

𝑃𝑡𝑖𝑐𝑘𝑒𝑡|𝑟𝑎𝑖𝑙 =exp(𝑼𝒕𝒊𝒄𝒌𝒆𝒕)

exp(𝑈𝑡𝑖𝑐𝑘𝑒𝑡′)𝑡𝑖𝑐𝑘𝑒𝑡′∈𝑁

Incremental Logit model

𝑃′𝑟𝑎𝑖𝑙=𝑃𝑟𝑎𝑖𝑙exp(∆𝑈𝑟𝑎𝑖𝑙)

1−𝑃𝑟𝑎𝑖𝑙 +𝑃𝑟𝑎𝑖𝑙exp(∆𝑈𝑟𝑎𝑖𝑙)

Upper Level

Lower Level

Fare

Function

𝑵𝒆𝒘 𝑼𝒅𝒂𝒕𝒆

𝑮𝑪𝒅𝒂𝒕𝒆=𝑭𝒅𝒂𝒕𝒆 + 𝒗𝒐𝒕 ∗ 𝑮𝑱𝑻𝒅𝒂𝒕𝒆 + 𝑨𝑷

𝑨𝑷—Advanced Purchase Penalty

𝑭𝒅𝒂𝒕𝒆—Fare Function deduced by data

A Route on High Speed Rail Network in an European Country

Data Collection

Calibrate PRAISE model 𝑷𝒅𝒂𝒕𝒆 = 𝑷′𝒓𝒂𝒊𝒍𝑷𝒅𝒂𝒕𝒆|𝒓𝒂𝒊𝒍

CASE STUDY

• Devise Scenarios involving different levels of fare adjustment to analyse different

yield management systems;

• Calculate the Revenues and Demands for different Scenarios;

• Compare with the Base Scenario (current fares) to put forward Price Suggestions.

Xinghua Zhang -MSc (Eng) Transport Planning and Engineering Supervisor : Daniel Johnson

E-mail: ml12xz@ leeds.ac.uk Institute for Transport Studies University of Leeds 05 - 2014

Yodya Yola PratiwiSpeed Limits for UK Motorways:a case study of increase the speed limit

Yodya Yola PratiwiSpeed Limits for UK Motorways:a case study of increase the speed limit

Yodya Yola PratiwiSpeed Limits for UK Motorways:a case study of increase the speed limit

Yodya Yola PratiwiSpeed Limits for UK Motorways:a case study of increase the speed limit

Yodya Yola PratiwiSpeed Limits for UK Motorways:a case study of increase the speed limit

Yodya Yola PratiwiSpeed Limits for UK Motorways:a case study of increase the speed limit

Yodya Yola PratiwiSpeed Limits for UK Motorways:a case study of increase the speed limit

Yodya Yola PratiwiSpeed Limits for UK Motorways:a case study of increase the speed limit

Yodya Yola PratiwiSpeed Limits for UK Motorways:a case study of increase the speed limit

Yodya Yola PratiwiSpeed Limits for UK Motorways:a case study of increase the speed limit

Yodya Yola PratiwiSpeed Limits for UK Motorways:a case study of increase the speed limit

Electric Bikes: a solution to hills? Lessons from case of China

.

In the chart, the growth rate of E-bikes was predicted to

show a sharply increase in Western Europe while the

others remain stable from 2013 to 2020.

Easily found in the terrain map of UK, the most land of

UK is covered by rugged hills and low mountains,

especially from the middle to the north.

Goodman (2010) has pointed out that E-

bike is a kind of tool which take hills out of

riding. Nowadays, China have the most

amount of E-bikes users which increased

from several thousands to more than 10

million from 1998 to 2005. Although it can

be say that electric bike is one of the best

choices for low income people to travel in

the developing country, the impact of these

much electric bikes is huge and most of the

impacts are negative (Weinert and Ma et al.,

2007, pp. 62--68).

Find out the potential users of E-bikes in

UK, range from different age and gender.

In order to improve the market of E-bikes

in UK, the appropriate price and what will

the consumers consider when deciding to

buy a E-bike should be identified.

The usage of E-bikes.

UK

China

Zhixi Li – ts13zl@leeds.ac.uk Msc Transport Planning Supervisor: Frances Hodgson

ITS University of Leeds

Background 1 Objectives 2

Methodology 3

Study of UK

Questionnaire

Classification of Respondents 1 Gender

2 Age

Case Study of China to inform UK

Focus Group

Literature Review

1 Government publication 2 News Report

Potential outcomes 5

Scope 4

UK

Age Gender Standard

20-40 Male • 60 people will be surveyed for each age group, and 30 for each gender.

• Online surveys and practical surveys will be done on the same time for all the age group

Female

40-60 Male

Female

60-80 Male

Female

China (second tier city)

Drivers

Pedestrians and cyclists

China began to produce E-bikes since 1998 and have

the most amount of E-bikes users, increased from

several thousands to more than 10 million in 2005.

Lacking of appropriate improvement strategies and

regulations leads to a lot of traffic congestions and

accidents.

Introduction

E-bike is a kind of tool which take hills out of riding

(Goodman, 2010).

UK government defined Electric bikes as “electrically

assisted pedal cycles”(EAPCs) which should be 2-

wheeled bicycles, tandems or tricycles with a battery

pack and a motor to transfer power.

The definition of E-bikes in China has a greater range,

included E-bikes, E-scooters, Mobility Scooters.

Background of theses two countries

The research will seek to identify the

advantages and disadvantages resulted

from using E-bikes to the citizens of China,

while the reason why E-bikes are so

popular in China will be found.

By review the documents related to E-

bikes in china, find out the potential

problems to the whole transport system,

and make suggestion for an appropriate

improvement strategies and policies.

Most of the young people might use E-

bikes for leisure, therefore E-bikes may be

more popular for elder people, the survey

will help to find the reason of it

Price and safety reasons may be the most

important thing to concern

After the case study of China, Appropriate

policies and improvement strategies

should be determinate and published, in

order to make suggestions for UK

Infrastructure and related facilities would

be well designed and built up before E-

bikes plan being practiced

DOES PAINTING THE TOWN RED WORK?

The study area will be the city of Leeds, which means that the cycling

lane sections that can be potentially analysed will lay inside Leeds

boundaries and the proposed questionnaires can only be answered by

Leeds residents or people from other cities who perform any kinds of

activities here.

As we cannot make observations in all cycle lane sections of the city, we

have set the criteria for the selection, prevailing those locations where

lane violation is more probable, as junctions, high dense traffic roads or

areas where economic activities attract an important number of vehicles,

overloading parking facilities.

AN APPROACH TO THE LEEDS CITY CASE

City Councils all over the world paint their cycle lanes in vivid colours, following the belief that this

is the best option to make them visible for other road users and to provide a feeling of safety to

cyclists. But…is it actually useful? What are the implications of this measure? Are there any

alternatives? We will try to give an answer studying Leeds road users’ behaviours and attitudes.

road safety? cycle lanes levels of use? local budgets?

METHODOLOGY AND DATA COLLECTION

We will carry out observations at some selected points in order to collect data

about cycle lane violations in painted and non-painted sections and which

reasons cause them, but it is also intended to study cyclists’ behaviour (e.g.

if they look comfortable in these lanes or if they do not used them and prefer

general circulation lanes). Previously, we need to design a predefined form

to gather the same kind of information at all the observations

It is the first stage of the investigation. We are checking out if some kind of

research related to this topic has been carried out in Leeds or in other

cities, as it will help us to set the methodology and make comparisons

between our further results and those existing ones. We will look for general

arguments in favour and against painting cycle lanes from different points of

view: (technical, economical and psychological). Furthermore, alternatives to

painting cycle lanes will be explored, in order to make if those experiences

have been successful and can be transferable to Leeds roads

We want to know if painting the

cycling lanes makes them actually

safer, that is, the probability of a lane

violation, and thus, a possible crash

between a cyclist and other road user

are smaller when the cycling lane is

painted.

Are there any technical problems

derived from a painted surface? We

make ourselves this question as we want

to discover if the used paint matches the

safety standards to avoid accidents when

weather conditions are not favourable.

Do painted lanes encourage people to use

them? Is it because they perceive them as safer,

more attractive, or as an effective tool to reduce

their travel times/budgets?

Are there other factors that affect cycle lane

usage apart from the paint? Other aspects as

complete segregation from general circulation or

shared lanes with buses can be also decisive.

Are there effective alternative measures/devices

with the same visual effect of paint? We want to

explore other beneficial alternatives for all road

users.

Painting and maintaining

cycling lanes has a price. And we want to know if Leeds

City Council would be able to

save a significant amount of

money running a different

painting-lane policy.

But what about the price

of alternative measures? By knowing it, we could make

a trade-off between them and

the benefits they can

potentially provide.

David Rodriguez Martin Year 2013/2014

MSc Transport Planning Dissertation

WHA

T AR

E TH

E

BIKE

LANE

Questionnaires are also a key part of the data collection,

because we are using them to reach a better understanding of

opinions, behaviours and perceptions. We need data from all

road users (cyclists, motorcyclists, car drivers and pedestrians)

as they will have different views on the topic. Different

questionnaires for every road user group have been designed and

sent to people. This stage will not finish until mid June, as the

more people decide to participate, the bigger will be our sample

and then more representative our results. It is also important to

codify all possible answers to make the analysis stage easier.

SCOPE OF

IMPA

CTS

ON…

THE STUDY

Questionnaires Direct observation

Literature review

BIKE

LANE

All the data collected will be analysed and the results will be expressed

through texts, charts and tables. Statistical operations will be performed also

at this stage. Then, having understood all the data collected and, taking the

previous literature into account, we will reach to a conclusion, trying to

answer the research questions.

Analysis of results and conclusions

UNIVERSITY OF LEEDS

Source: www.ecomovilidad.net

Source: www.zicla.com

ECONOMY ENVIRONMENT

+ Reducing the cost of import fossil fuels + Gaining profit by export biofuels + Reuse abandoned agricultural land − Low energy density of biofuels, lead to more

distances, travel times and labour costs. − Feedstock processing costs

+ Less GHG emission + Carbon negative renewable energy − Extra trips/distances are needed for collecting

feedstock cause greater air pollution − The changes of Land use cause emissions − Decrease biodiversity − Decrease soil, water quality

SOCIETY ENERGY SECURITY

+ Increase employments + The fuel prices become more reasonable − Food security concerns − Poor quality of biofuels decrease the vehicles

performances − The hi-tech vehicles are not affordable for the

whole public, increase the inequality.

+ As an alternative energy source to enhance energy security

− The failure of implement biofuels can weaken energy security

− Hard to forecast if biofuels face the same situation like oil crisis in the future

“4 A

SPEC

TS” OF IM

PAC

TS A

IMS &

OB

JECTIV

ES M

ETHO

DO

LOG

Y

Rose I-Hsuan Lien (ts13ihl@leeds.ac.uk), Supervised by Dr Anthony Whiteing

iofuel’s issue used to focus on its energy efficiency and the food crisis it might cause. However, how to optimise the supply chain still lack of discussion at present. To illustrate, negative impacts of biofuel supply chain not only need to be recognised but be solved through sustainable ways. The main missions of this supply chain management are to ensure the costs are competitive and the supplies are continuous (Gold and Seuring, 2011; Hess et al., 2007; Sims and Venturi, 2004). The demands of biofuel in Taiwan are increasing. Since the usages of biofuels in Taiwan are still developing, it is an advantage for Taiwan to implement the sustainable strategies into the supply chain.

Harvest & collect

·Feedstock type

·Harvest frequency

·Topography

·Post-harvest process place (bail and chip)

Storage

·Demands & supplies

·Storage facilities

·At farm or storage terminal

·Degradation & dry matter loss

Pre-treatment · Pre-treatment with storage

· Mode: drying, pelletisation

Energy conversion

· Place for refine and blend

Gas station

· The facilities to store biofuels

Consumer

· Vehicle performance

· Vehicle choices

Transp

ort: M

od

e ch

oice

, law &

infrastru

cture

, veh

icles cap

acity

Reference: ①Green Energy Industry Information Net, (2014). Introduction of biofuels Industry. Taiwangreenenergy.org.tw. Available at: http://www.taiwangreenenergy.org.tw/Domain/domain-4.aspx ②Hess, J.R., Wright, C.T., Kenney, K.L., 2007. Cellulosic biomass feedstocks and logistics for ethanol production. Biofuels, Bioproducts and Biorefining 1 (3), 181e190. ③Gold, S. and Seuring, S. 2011. Supply chain and logistics issues of bio-energy production. Journal of Cleaner Production, 19 (1), pp. 32--42. ④IEA. 2013. Tracking Clean Energy Progress 2013. [e-book] Paris: Internation Energy Agency. Available through: Internation Energy Agency http://www.iea.org/publications/TCEP_web.pdf. ⑤Sims, R.E.H., Venturi, P., 2004. All-year-round harvesting of short rotation coppice eucalyptus compared with the delivered costs of biomass from more conven- tional short season, harvesting systems. Biomass and Bioenergy 26 (1), 27e37.

B

1. Case studies: Studying biofuel promoting schemes in developed/emerging/developing countries to compare and organise the implementations under different circumstances. 2. Interviews: To apply potential options to Taiwan, the locals’ opinions and experiences are important. Thus the options will be provided to experts in different areas - academic, planner, government, sustainable workers in Taiwan, and interview them to know the attitudes and possibilities to implement these options.

PROBLEM → APPROACH → SOLUTION → ADAPTION → APPLICATION Understand biofuels implementation

Understand operation processes

Develop sustainable Instruments

Develop options for Taiwan

Summarise the feasibilities of options

①Policies& infrastructures

②Impacts in “4 aspects”

③Future goals & trends

①”Who” drive &“who” are involved

② Operation processes

③Differences between nations

①Instruments integration ②Factors of instrument

choices ③Underlying barriers and

conflicts ④Strengths &

weaknesses in “4 aspects”

①Develop options

②Stakeholders’ viewpoints

①The most and the least feasible options ②Influents of choices ③Conflicts of influents

between different stakeholders

④Improve options: negative↓, positive↑

(Gold and Seuring, 2011)

Case in Taiwan: Bioethanol: (Import only, no local factories (high investment risk)) •07/2009:Taipei&Kaohsiung Metro Area Ethanol Promotion Project, total 14 gas stations supply E3. •2012: 210 kilolitres used.

Biodiesel: (Imported palm oils and local cooking waste oils) 4 stages implement plan: 1.11/2006-06/2008: Green Bus Project, encouraged

buses use B1-B5 biodiesel 2.07/2007-06/2008: Green County Project (B1), in two

counties, the oil companies in these areas were

chosen to blend biodiesels and to sell in the gas station there. B100 biodiesel were refined by Taiwan factories , also energy crops were allocated to plant in fallow farmlands.

3.07/2008: Full implement B1 4.06/2010: Full implement B2 • 2012: 100,000 kilolitres used. • 2014: Poor quality of biodiesels influenced vehicles

performance, government plan to pause B2 projects in June.

• 2016: Full implement B5, estimated 250,000 kilolitres biodiesel required.

(Green Energy Industry Information Net, 2014)

OPERATION ISSUES

Global biofuel productions projections and target.

(Source: IEA, 2013)

Analysing of the Relationship Between Accessibility and Customer Satisfaction for the Evaluation of Transport Plans

Ioanna Moscholidou, MSc Sustainability

Supervisor: Astrid Gühnemann THE CASE OF WEST YORKSHIRE

Evaluation plays a key role in transport planning as it allows the

timely identification of strengths and weaknesses and the

readjustment of policies and measures (Burggraf and Gühnemann,

2014).

The evaluation of accessibility is an area that attracts increasing

attention in transport planning. However the challenge of

identifying the correct measures for each situation remains. It is

suggested that for meaningful assessment accessibility levels

should be disaggregated for different modes and social groups and

linked to other indicators to allow contextualisation (Geurs and van

Wee, 2004).

In existing research the links between the public transport supply

and the perceived service quality have not been clearly

established. It is suggested that the subjective views of customers

are highly context-dependent and not only do they reflect what

they get but also how they get it and who they are (Friman and

Fellesson, 2009).

The theoretical background

The methodology

The objectives

Source: Metro, 2014 Source: Vector Research, 2012

6.9 satisfaction

with rail

services

7.2 satisfaction

with bus

services

67% of the population has access to

workplace within 30 minutes using public transport

The 2011 baseline

Access to employment

% of working population able to access key

employment centres within 30 minutes using

the core public transport network.

Satisfaction with transport

The indicator combines satisfaction scores

across modes and assets. Scored out of 10.

The Local Transport Plan Targets

75%

67%

7.0+

6.6

The West Yorkshire context

Metro, the West Yorkshire passenger transport executive,

implemented its 3rd Local Transport Plan (LTP) in 2011. The

plan covers the period until 2026 and the first phase of

evaluation ended in April 2014. Over the first three years the

transport authority faces the challenge of maintaining the

high quality of services despite the public spending cuts.

List of references:

Burggraf, K. and Gühnemann, A. 2014. Why is monitoring and evaluation a challenge in sustainable urban mobility

planning? Report for the CH4LLENGE Project. Available from: http://www.sump-challenges.eu/content/monitoring-and-

evaluation (last accessed 25/04/2014)

Friman, M. and Fellesson, M. 2009. Service supply and customer satisfaction in public transportation: The quality

paradox. Journal of Public Transportation. 12(4), 57-69.

Geurs K.T. and Bert van Wee, B. 2004. Accessibility evaluation of land-use and transport strategies: review and

research directions. Journal of Transport Geography. 12, 127–140.

Vector Research. 2013. Final Report- Tracker Survey 2011 for Metro.

1. To evaluate their performance of LTP3 in terms of accessibility

and customer satisfaction.

2. To identify any correlation between the accessibility and

satisfaction and provide an explanation for the underlying

factors of the result.

3. To provide an insight on how the ex-post evaluation results

can contribute to the improvement of ex-ante appraisal.

1. Four accessibility and four customer satisfaction indicators will

be evaluated against the targets set by the LTP in a checklist

format for a 3-year period (2011-2013).

The analysis will be done using ACCESSION and ArcGIS.

2. The accessibility indicators will be correlated with overall

satisfaction and a corresponding satisfaction indicator using

spatial regression and hypothesis testing.

The analysis will be done using the R software.

Hypothesis: Accessibility and satisfaction are not correlated.

3. The correlation results will be analysed using contextual data

from 2011 Census and Metro surveys in order to provide a

clearer view of the West Yorkshire background.

Accessibility (data from Metro)

• % of working population with access employment within 30

minutes using public transport

• % of population within a zone of 200m around public

transport stops/stations

• % of population with access the urban centres within 30

minutes using public transport

• % of population within a zone of 200m around public

transport accessible stops/stations

Customer Satisfaction (data from Passenger Focus)

• Overall satisfaction

• Satisfaction with distance of the stop/station from the journey

start

• Satisfaction with convenience/accessibility of stop/station

location

• Satisfaction with on-vehicle journey time

The indicators

0 10

UNIVERSITY OF LEEDS

Institute for Transport Studies

Playing with transport How to make your commute greener and have fun at the same time.

Objectives Methodology

This dissertation will try to

understand if there is a

difference in mode choice

when people are given

incentives to use modes of

transport that are friendlier

with the environment.

The incentives investigated

will be based on gamification

where an aim is set and

participants compete to win by

making decisions that impact

their behaviour.

Data analysis and expected findings

A 2 week field study will be carried out to assess the impact that providing incentives

has on commuting behaviour.

Participants will be

asked to install an app

on their phones and use

it for 2 weeks.

During the first week, the

app will record information

on participants’ trips,

including distance travelled,

mode choice, route choice

(coordinates) and duration.

During the second week, participants

will receive points based on their use of

different modes. Information on their

ranking amongst the group will be

disclosed. Points will be converted into

raffle tickets for a chance to win prizes.

Background

With urban pollution on the rise, policies that incentivise

modal switch towards modes of transport that are better for

the environment are needed.

Gamification, or the art of mixing games and real life, has

shown to be an effective way of modifying behaviour to

achieve a specific goal(1); whether it is user engagement,

learning, or even physical exercise.

Gamification can be used as a way to reward positive

behaviour rather than punish negative behaviour, and as

such may be well perceived by the public and prove more

effective than punitive policies such as congestion charging.

J. SEBASTIAN CASTELLANOS

Supervisor: Frances Hodgson

Bibliography and further reading

1. Muntean, C. I., 2011, Raising engagement in e-learning through gamification, 6th International

Conference on Virtual Learning.

2. McCallum, S., 2012, Gamification and serious games for personalized health, 9th International

Conference on Wearable Micro and Nano Technologies for Personalized Health.

3. Brazil, W. and Caulfield, B., 2013, Does green make a difference: The potential role of smartphone

technology in transport behaviour, Transportation Research Part C: Emerging Technologies, Vol.

37, pp. 93–101.

4. Dick, E., Knockaert, J., and Verhoef, E., 2010, Using incentives as traffic management tool:

empirical results of the “peak avoidance” experiment, Transportation Letters: The International

Journal of Transportation Research Vol. 2, pp 39-51.

5. Fan, Y., Chen, Q., Douma, F., Liao, C., 2012., Smartphone-Based Travel Experience Sampling and

Behaviour Intervention among Young Adults, Intelligent Transport Systems Institute, University of

Minnesota.

6. Schlossberg, M., Evers, C., Kato, K., and Brehm, C., 2012, Active Transportation, Citizen

Engagement and Livability: Coupling Citizens and Smartphones to Make the Change, URISA

Journal, Vol. 25, No. 2.

The app

Participants click on “start

trip” whenever they want to

record a trip.

While travelling, participants

take a picture to confirm the

mode they’re using and hence

become eligible for points

When the trip is finished,

participants click on stop and

choose the mode they used.

The information is sent to a

server where it is collected

Sample trips

Output files

With the data collected during the first

week of the study, a baseline scenario

will be set up and the modal split of

the sample will be determined.

During the second week, changes in

modal split will be observed and a

hypothesis test will be carried out with

H0 being there is no change in modal

split when incentives are in place, and

H1 being there is a significant change

in modal split, specifically towards

higher rewarded modes.

Field study

The field study will be carried out in

Bogotá (Colombia), with students

between the ages of 17 and 24. The

sample size will be between 30 and

40 participants.

Data analysis

Mode Points

Car 0

Public bus 2

BRT 4

Bicycle 6

Walking 6

Noisy optimisation: Stochastic optimisation of traffic signal networks using a trust region method

Jack Robinson, MSc (Eng) Transport Planning and Engineering candidate, Institute for Transport Studies, University of Leeds

For a network of traffic signals

with a given common cycle time,

which set of green times and

offsets minimises the total travel

time of vehicles through the

network?

Problem formulation I

• The problem is too difficult to

solve analytically for general

networks

• The objective function may

contain multiple local minima,

making hill-climbing methods

unreliable

• Stochastic effects from

simulation further distort the

objective function

Set parameters, and initial timings and trust region

settings

Simulate network in DRACULA for various timings within the trust

region

Fit model function to simulation results

Find solution to minimise model function

Update trust region centre and size

Output solution

Loop

until so

lution c

onve

rges

• A framework for derivative-

free optimisation, useful for

complicated surfaces

• An easy-to-optimise model

function is fitted to the

objective function in a ‘trust

region’ around the current best

solution

• For this project, quadratic

model functions are used, and

the trust regions are balls

Schematic

contour plot of

trust region

model function

True function:

Model function:

Trust region:

Compare resilience and speed of

the algorithm under different

parameters, networks and

starting conditions

• The algorithm is being tested

for a simple two-node network

• Testing will continue on larger

networks, and the algorithm

will be refined

• Is the algorithm adversely

affected by the randomness of

simulation? Use of the simpler,

deterministic cell transmission

model could be compared

Photo by Edwin Mak

10 20 30 40 50 600

0.2

0.4

0.6

0.8

1

Total veh-hours in network

Em

piric

al cum

ula

tive p

robability d

istr

ibution

Empirical cumulative distribution plot of a test run of the

trust region algorithm on the two-node network

after 0 iterations

after 1 iteration

after 2 iterations

after 3 iterations

after 4 iterations

after 6 iterations

known solution

Issues 2

Algorithm structure 3

Trust region methods 4

Analysis 5

Progress and plans 6

Block signalling The concept of block signalling is simple. The railway line is split into blocks of varying lengths, controlled by a signal at the entrance to each block. Once a train is within a particular block, no other train can enter the block until it is clear. This was introduced as a safety precaution in the late 1880’s and is still used in places on the network today.

Moving block signalling Much like block signalling, moving block signalling uses the ideas of blocks as well. The difference is that the blocks re fixed to each individual train. The block is the length of the train, plus the distance that the train would need to stop in case of emergency. This allows trains to run a lot closer together in a safe manner.

Premise A hot topic today surrounding railways is the issue of capacity. With demand for rail services increasing the railways are becoming busier. In terms of the environment there is also call for freight to be transported via railways instead of roads and so this puts further strain on the railway network. HS2 is a solution to this increase in demand but since its completion is 20 years away, there needs to be a solution now. Moving block signalling is one of those solutions and this project will look at what sort of capacity gain the railway could experience by changing its approach to signalling.

Modelling Using the specifications from real locomotives that are operating on the East Coast Mainline, the length of each block can be calculated. At stage one, the new capacity can be calculated under the assumption that all trains run at the same speed. At stage two, the varying speeds of trains, freight vs passenger, can be taken into account using duel lines to facilitate an optimal solution for capacity.

Outcomes and Analysis After the modelling stage is complete there will be an optimal solution in terms of the maximum capacity of railways. This will be reviewed in terms of the modal shift of freight and the environmental benefit as well as the passenger rail demand benefit. The trade offs will also be reviewed to see if there are any negative effects of moving block signalling.

A multi-national analysis of the value of travel time: the study case of UK and DK

UNIVERSITY OF LEEDS Institute for Transport Studies

Background and motivation

The value of travel time is the key parameter in transport economics1. Its definition plays a major role in:

• Investment appraisals • Travel demand forecasting

The ample variety of results across models and studies is a matter of concern.

In the UK, estimated values of the VTT differ up to 69% within the same data set2. • What is the nature of the multiplicity of results? • Are complex models diminishing our ability to

obtain «robust» empirical evidence on the willingness to pay?

• What is the role of the data set?

Is model selection to blame for the differences?

Evidence in Australia and NZ suggest that "as models become more complex, there is greater variability in the mean estimates of VTTS between data sets"3.

Source: Hensher, et. al (2012)

Leonardo Ortiz Olivares Prof. Stephane Hess (Supervisor)

Objectives

• To explore if significant differences exist on the estimation of the mean VTT across different choice models within the same data set for the UK and DK cases.

• To provide some insight on the implications of model structure selection (contrasting models).

A first look at the evidence

Source: UK data from Tjiong (2013) and Mackie (2003); DK data from Fosgerau (2006)

4.22

7.13

1.03 1.49

MNL MMNL

UK (p/min) DK (DDK/min)

Methodology

Multinomial Logit Model (MNL), Mixed MNL (MMNL) and Latent Class Model with identical set of attributes and functional form will be developed to calculate comparable VTT across models.

To determine the influence of the data or functional form in the VTT estimated values, a multivariate regression will also be estimated:

where i indicates the estimated model and all independent variables are specified as dummy (1,0).

Data

British, Danish and Dutch national VOT studies have been built on similar time-cost experiments. Access to similar data sets in design allows to contrast results within model form across comparable data sets. • Orthogonal survey designs • Two unlabelled alternatives • Non-business trips included • Similar socio-economic variables

References

1FOSGERAU, M. 2006. Investigating the distribution of the value of travel time savings. Transportation Research Part B: Methodological, 40, 688-707. 2TJIONG, L. K. J. 2013. Re-estimating UK value of time using advanced models. MSc Transport Planning, University of Leeds. 3HENSHER, D. A., ROSE, J. M. & LI, Z. 2012. Does the choice model method and/or the data matter? Transportation, 39, 351-385. MACKIE, P., WARDMAN, M., FOWKES, A., WHELAN, G., NELLTHORP, J. & BATES, J. 2003. Values of travel time savings UK.

𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖 = 𝐶𝐶 + 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖 + 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖 + 𝑀𝑀𝐶𝐶𝑖𝑖 + 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑈𝑈𝑈𝑈 + 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑈𝑈

Investigating and Calibrating the dynamics of vehicles in

Traffic Micro-simulations ModelsMohammed Yazan Madi - Supervisor: Dr. James Tate

University of Leedsts13mym@leeds.ac.uk

Ob

jecti

ve

s

Transportation sector is a significant contributor to air quality

problems in the United Kingdome .

Traffic micro-simulation models are used to generate second-by-

second speed trajectory information for use by instantaneous

emission models to evaluate the environmental impact of real-time

Backg

rou

nd

Develop a calibration process of micro simulation models to ensure

vehicular behavior from simulation is closer to the behavior observed in the

field, which improve second by second vehicle activity and emissions

estimates.

Validate micro-simulation vehicles' dynamics with field second by second

trajectories data to explain any trends or unique observations in emissions

The following data will be collected :

Data Collection

Ob

jecti

ve

s

Researc

h M

eth

od

olo

gy

emission models to evaluate the environmental impact of real-time

transport policies.

Vehicle dynamics are explained by the driving velocity and

acceleration / deceleration of vehicles.

It is significant that the vehicle dynamics replicate on-road

behavior, so the emission model’s simulations of engine power

demands and resultant fuel consumption/ emissions are reliable.

For environmental models to be reliable, vehicle dynamics in

traffic micro-simulation models need to replicate on-road behavior.

Backg

rou

nd

Case study Location Micro-simulation Model

trajectories data to explain any trends or unique observations in emissions

estimates from the simulation and the real-world.

The motivation of this dissertation is to apply these advanced models to

create a state-of-art traffic micro-simulation models that are real-world

integrated and can evaluate the environmental impacts of different traffic

management strategies.

Research Hypothesis: Parameters of internal behavioral models within

microscopic simulation packages need calibration and validation to better

replicate vehicle activity on roads.

Researc

h M

eth

od

olo

gy

sim

ula

tio

n M

od

el

Headingley, Leeds, UK.

Micro-simulation model calibration and validation process framework :

Calibration and Validation Process Approach

Vehicle Movements in AIMSUN

RaceLogic VBOX II GPS engine and data logger

is used to obtain dynamic characteristics data

of vehicle (speed, acceleration) and

geographical position.

AIMSUN microscopic traffic simulator will be

used to model the Headingley corridor traffic

in Leeds to measure second-by-second speed

trajectory information.

AIMSUN Specifications

List your information on these lines.

Mic

ro-s

imu

lati

on

Mo

del

Traffic-emission Modeling Framework

Micro-simulation model calibration and validation process framework :

AIMSUN

Micro-

simulation

Model

AIMSUN Micro-simulation Model Calibration

using key input data , and simulation model parameters (observed aggregated traffic flow

and average travel speed)

AIMSUN

Micro-simulation

Model Validation

using activity data

AIMSUN

Calibrated

Model Run

Demand/ Flows

(DMRB procedure,

GEH stat)

Journey times

(DMRB

criteria)

Vehicle

Trajectories

Vehicle speed

and

acceleration

Vehicle Movements in AIMSUN

Researc

h M

od

el

The traffic model captures the second-by-second speed and acceleration of

individual vehicles travelling in a road network based on:

Vehicles individual driving style.

Vehicle mechanics.

Vehicles interaction with other traffic and with traffic control in the network.

During simulation each vehicle is moved

along their chosen route from origin to

destination. Their speeds and positions

are updated according to car-following,

lane-changing and gap acceptance rules,

and traffic regulations at intersections..

Example

Data

An

aly

sis

Researc

h A

pp

licati

on

• AIMSUN real–world

integrated micro-simulation model.

TRAFFIC MICROSIMULATION

• Instantaneous

emission model PHEM 11.

VEHICLE EMISSION MODEL

• Road section, time-of-

day, vehicle sub-category or an individual vehicle

trajectory.

RESULTS

Vehicle

Trajectory

Data

Disaggregate

Emission Data

VEHICLE TYPE

(Car, LGV, HGV)

VEHICLE SUB-

CATEGORY

(Euro, Fuel type)

Frequency distribution of Observed and Modelled passenger car speed and

acceleration distributions (hexagonal binning). Source (Tate, 2013)ANPR

SURVEY

Tate, J. 2013. York Low Emission Zone Feasibility Study- Vehicle Emission Modelling. Institute for Transport Studies, University of Leeds.

Source (Tate, 2013)

The Importance of Eco-DrivingIn the last decades, engine technology and vehicle performance has improved, while mostdrivers have not adapted their driving style.1 Eco-driving represents a driving culture whichsuits modern engines and makes best use of advanced vehicle technologies, such asmaintaining a steady speed at low rpm, shifting gear early and rolling in neutral. The benefitsof adopting such behaviour include;

Lower greenhouse gas emissionsIt is widely accepted that greenhouse gas emissions contribute to climate change. Eco-drivingcan be embraced as a simple and effective environmentally friendly initiative that can lowervehicle emissions by 20g per kilometre.2

Save cost on fuel By adopting eco-driving practices, fuel efficiency can be improved by 15% or more lowering the demand for fuel.3

Source (Fiat, 2010, p. 25)

Eco-driving: who does it and why ?Identifying the motivational factors

References

1 Eco Drive (2014). What is eco-driving. [Online]. [Accessed 7th April 2014]. Available at: http://www.ecodrive.org/en/what_is_ecodriving-/2 Fiat. (2010). Eco-driving Uncovered. [Online]. Accessed 7th April 2014]. Available at: http://www.fiat.co.uk/uploadedFiles/Fiatcouk/Stand_Alone_Sites/EcoDrive2010/ECO-DRIVING_UNCOVERED_full_report_2010_UK.pdf3 Baltutis, J. (2010). Benefits of Eco-Driving. [Online]. (Accessed 7th April 2014]. Available at: http://www.unep.org/transport/PDFs/Ecodriving/Ecodriving_pwpt.pdf4 Mensing, F et al. (2014). Eco-driving: An economic or ecologic driving style? Transportation Research Part C, 38, 110 – 121.5 Powell, D. (2008). Extreme Driver – with a difference. New Scientist. 200, pp. 42 -43 6 DfT. (2011). Eco-driving: Factors that determine post test take up. [Online]. [Accessed 7th April 2014]. Available at: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/142536/Eco_safe_driving.pdf7 Delhomme, P et al. (2013). Self-reported frequency and perceived difficulty of adopting eco-friendly driving behaviour according to gender, age and environmental concern. Transportation Research Part D, 20. pp. 55 – 58.

Savings per car life cycle

Fuel Consumption

CO 2

EmissionsSavings

Average Driver

-6% -1,088kg £480

Top 10% -16% -2,895kg £1,260

Literature Review

Motivational factors - Economic 4

- Environmental concern 4

- Embracing the challenge 5

- Road safety 6

Barriers to adoption- Perceived difficulty 7

- Lack of awareness 6

- Technological innovation is more interesting 2

- Lack of tuition in driving schools 6

Group most likely to eco-drive- Middle aged females with a high environmental concern 7

Group least likely to eco-drive- Young males and females 7

Michael Wilson

Supervisor: Dr Daryl HibberdInstitute for

Transport Studies

MethodologyStep 1 – Develop an online scenario based questionnaire divided into 4 sections

Section 1 – Personal Characteristics:Identify the key variables such as age, gender,income and environmental concern.

Section 2 – Strategic Decision ScenariosExamine the motivation for the selection of vehicle type,checking tyre pressure and fuel choice.

Section 3 – Tactical Decision ScenariosExamine the motivation for route choice regarding congestion , road terrain and whetherindividuals consider removing excess vehicle weight.

Section 4 – Operational Decision Scenarios Examine the motivation for driver speed, driving style, air conditioning use and idling.

Example QuestionYou are given the choice of two routes to the same destination. Route 1 will take 1 hour and emit 5% more CO2.Route 2 will take 10 minutes longer, emitting 5% less CO2.Which do you choose?

Step 2 – Obtain 100+ respondents to achieve a representative sample of the population. Respondents will be private vehicle users with a valid driving license.

Step 3 – Analyse results by performing a regression analysis between variables in SPSS to answer research questions.

Possible Further Research

Examine how eco-driving campaigns can target driver groups through motivational factors and removal of barriers

Attempt to quantify the environmental impact if eco-driving was implemented as a soft policy measurement

Undertake a cost-benefit analysis to calculate the NPV to the driver of eco-driving

Aims and ObjectivesThe aim of this project is to explore the relationship between individual characteristics, eco-driving behaviours and motivational factors. The following objectives have been set toanswer the overarching question of who eco-drives and why?

Identify motivations behind eco-driving

Identify which drivers are most likely to undertake eco-driving behaviour

Identify whether individuals are conscious of eco-driving

Identify the potential barriers to eco-driving

HypothesesThe following hypotheses will be tested in order to provide a structure and focus to answer the project objectives.

The strongest motivation to eco-drive is fuel conservation

Lower income groups are more likely to eco-drive because of economic influence

Individuals who travel a greater distance are more likely to eco-drive because of the experience they have gained

The greatest barrier is awareness of eco-driving practices

How does stated preference design affect the valuation of ‘soft’ factors?

Introduction Although there is a wide literature about the valuation of time, congestion and other significant issues such as price elasticities, there is a lack of confidence in the valuation of soft factors.

Soft factors are seen as attributes of secondary importance in demand choice but still effect demand. Soft factors for train passengers can be split into two main groups;

• On-board: Seat comfort, noise, information, security etc.

• Off-board: shelters, seating, CCTV, passenger lounge, heating, lighting etc.

It is important to value these attributes correctly to make sure that there is not an over or under valuation, resulting in train operating companies adapting their rolling stock or railway stations based on implausible fare augmentations.

Objectives The objective of this dissertation is to analyse how different stated preference designs affect the choices that respondents make. There is wide spread tolerance of implementing stated preference surveys that are designed in such a way that it is easy for respondents to judge the aims of the surveys. This opens the survey design up to respondents’ strategic bias.

• Does the design of a stated preference survey affect the valuations of the soft factors;

• Identical valuations- zero strategic bias

• Different valuations- strategic bias may be present where respondents can ‘play’ the survey

Literature Review

• Values of attributes can be 3 times higher when the purpose of the study is transparent, and thus there can be strategic bias (Wardman and Whelan 2001) • Direct valuations provided for soft variables are often unconvincing (Bates 1994). • Soft factor values across 18 different UK studies appear to be too large and have a lot of variation (Wardman and Whelan 2001). • By adding up to 10 soft factors, passengers state they are willing to pay double the original fare. This is very unrealistic in real life (Bates 1994). • It is necessary to implement an upper valuation cap for a bundle of soft factor improvements to stop it becoming unattainably high (Steer Davis Gleave 1990). • Direct valuations of soft factors vary with the number of factors included in the survey (Bates 1994).

Methodology and Data Collection

• Eight different designs will be made using Biogeme;

• Three transparent survey designs and five mixed designs.

• Data will be collected in face to face interviews on First Transpennine Express services.

• It is hoped that there will be 1000 respondents over a five day surveying period.

• Surveys will be initially done on paper, with the choices being analysed using a range of computer packages.

Key Questions To Be Answered

• Does a transparent survey design give higher soft factor valuations?

• Does the ordering of positive and negative attributes in the choice question affect the soft factor valuations?

• Are there different soft factor valuations when using fare and time as numeraires as opposed to only using one numeraire per survey design?

Attributes Alternative One Alternative Two

Seats Current condition Reupholstered

Air Conditioning Current availability Individual air conditioning

Noise As now Quieter service

Ease of access onto train As now (steps) Train door level with platform

Time 30 minutes 45 minutes

Choice

Attributes Alternative One Alternative Two

Seats Current condition Reupholstered

Air Conditioning Individual air conditioning Current availability

Noise As now Quieter service

Ease of access onto train Train door level with

platform

As now (steps)

Time 45minutes 30minutes

Choice

Other Design Combinations 1. Time: Grouped positive and negative attributes 2. Fare: Transparent 3. Fare: Grouped positive and negative attributes 4. Fare: Each row opposite to the previous 5. Time and cost: Transparent 6. Time and cost: Each row opposite to previous

References • BATES J, 1994, Reflections on Stated Preference: Theory and practice, Chapter 6 of Travel behaviour research: updating the state of play, pp.89-103 • STEER, DAVIES, GLEAVE, 1990, The effects of quality improvements to public transport, Wellington regional council • WARDMAN, M., WHELAN G., 2001, Valuation of improves railway rolling stock: A review of the literature and new evidence, Transport Reviews, Vol 21, No.4, pp415-447

COMPARISON BETWEEN CAR OWNERSHIP

CHARACTERISTICS IN UK & JAPAN

The difference in socio-economic profile between UK & Japan

potentially has an effect on car ownership characteristics

between the two countries at household level. It is interesting to

look at some attributes which is significant to car ownership level

in the two countries.

The study is conducted to develop a model based on basic

regression analysis and discrete choice principles, which is an

advanced regression technique, to explain the relationship

between socio-economic attributes and car ownership as

dependent variable.

UK Socio-economic data for UK is taken

from UK National Census data as

distributed by UK Data Archive. Full

census in the UK is conducted every 10

years by Census Division of Office for

National Statistics.

JAPAN Information on Japanese socio-

economic profile is provided through

Japanese General Social Survey which

was conducted in 2005. This survey

was designed and conducted by Osaka

University & Tokyo University.

ABOUT THE DATASETS

Some other published statistics are also used in this dissertation, including

National Travel Survey, World Bank Statistics and Organisation for Economic

Cooperation and Development (OECD).

KEY STATISTICS SUMMARY

CHOICE MODELLING BASIC REGRESSION The analysis is still being carried out for both datasets. Given in the table below is the most recent estimation for JGSS data

425 cars/ 1000 people

441 cars/ 1000 people

Based on World Bank Statistics, there are 425

cars/1000 people in UK in 2001, and 441

cars/1000people in Japan in 2005..

$$$$$$ $$$$$

US $ 27,926 Annual income per head

US $ 25,616 Annual income per head

According to Organisation for Economic Co-operation and Development

(OECD) published statistics in 2011, annual income per head in UK is

3% higher than in Japan.

78.4% employed

61.2% employed

It is revealed that 78.4% of respondents in UK were in employment,

while 2.2% did not have a job. Students were 0.5%. Whilst in Japan,

61.2% of the respondents were employed, and 37.1% were

unemployed .

UK NATIONAL CENSUS DATA 2001 Sample Size 2,964,871 Respondents’ age ranging between 0 – 85+ Car ownership is given for 0, 1, and 2+ cars Main commuting mode is given Employment status is given with 8 class NSEC system Income information will be imputed from other source (National Travel Survey, 2001)

Communal

19.4%

+

41.3%

37.4%

1.8%

Don’t have cars

CAR OWNERSHIP

AGE

41% of the respondent owns

a car and 37% owns more

than 2 cars. Whilst 19% do

not own cars, and the

remainder use cars in

communal, this includes car

sharing.

MAIN COMMUTING

MODE The main commuting mode in

UK was dominated by car with

the proportion of 61%, while

walking takes 12% in 2001

CAR OWNERSHIP

The minimum cars owned is determined by the total number of car types chosen by respondents, while

the maximum car owned takes the number of adult into account. In Japan, the car license is issued to

individuals aged over 18 .

Only respondents 20 years old and older who participated in the survey

AGE

COMMUTING MODE In this survey, the respondents are

allowed to choose more than one

mode. Car is dominating as chosen by

543 respondents, 95% of which

choose car only.

Dissertation by:

Rendy Prakoso MSc(Eng) Transport Planning & Engineering

Postgraduate Student

ts13rwp@leeds.ac.uk

Supervisor:

Professor Stephane Hess University of Leeds, UK

Professor Nobuhiro Sanko Kobe University, Japan

COMMUTING

TIME (MIN)

JAPAN GENERAL SOCIAL SURVEY

2005

Sample Size 1,872

Respondents’ age ranging between 20-89,

Car ownership is determined by type of car and age of respondent (licence issued for age 18 and over)

Household members age and sex information are included

Income is given in 19 categories, with 714 missing (38% of the data)

ACKNOWLEDGEMENT

Dependent variable =

car ownership

Independent variable =

age, sex, income, employment,

commuting mode, commuting

costs (time/distance)

Utility function

U n,j = V n,j + εn,j

j = different levels of car

ownership

Vn,j = f (β, x n,j)

Logit model,

choice

probabilities

The Japanese General Social Surveys (JGSS) are designed and carried out at the Institute of Regional Studies at Osaka University of

Commerce in collaboration with the Institute of Social Science at the University of Tokyo under the direction of Ichiro TANIOKA, Michio

NITTA, Noriko IWAI and Tokio YASUDA. The project is financially assisted by Gakujutsu Frontier Grant from the Japanese Ministry of

Education, Culture, Sports, Science and Technology for 1999-2008 academic years, and the datasets are compiled and distributed by SSJ

Data Archive, Information Center for Social Science Research on Japan, Institute of Social Science, the University of Tokyo.

β

β

β

β

β

β

β

β

β

β

β

Objectives • To examine whether

transport infrastructure investment causes economic growth and vice versa,

• To examine whether transport infrastructure investment causes employment and vice versa,

• To examine whether transport infrastructure investment causes labour productivity and vice versa

-6.0-4.0-2.00.02.04.06.08.0

10.012.014.0

1980 1990 2000 2010 2020

GDP

Year

Annual GDP Growth (%)

Expectations There is a positive dual relationship between transport infrastructure investment, economic growth, employment and productivity. Conclusion, Transport investment is a necessary but not a sufficient condition for economic growth

Hypothesis There is: • Unidirectional Granger-

causality from TINFI to GDP.

• Unidirectional Granger-causality from GDP to TINFI.

• Bidirectional (or feedback causality).

• Independence between TINFI and GDP. The same procedure is repeated for each of the other variables

Problem

Poor Transport infrastructure constrains growth. Less than 3 percent of GDP has been invested in transport over the years.

Roads subsector carries 96.4 per cent of the total freight yet only 16 percent of it is paved,

only 68 percent is in good condition. Only 26 per cent of the rail network is functional.

Only one wagon ferry vessel on L. Victoria and only one functional international

airport

Transport Infrastructure Investment and Economic Growth: The Case of Uganda

Richard Sendi MA Transport Economics Student 2013/14

Supervisor: Jeffrey Turner

Background Transport is the pivot around which the wheel of every economy revolves. It enables growth as it stimulates investment, lowers costs of doing business, opens up new opportunities, improves productivity and access to social services. Transport investment and economic growth do influence one another

Methodology A Granger (1969) causality approach will be used to determine the relationship between transport investment, economic growth, employment and productivity. (𝐺𝐺𝐺𝑔)𝑡 =𝛼 + ∑ 𝛽𝑖(𝐺𝐺𝐺𝑔𝑚

𝑖=1 )𝑡−𝑖 +∑ 𝛶𝑗𝑛𝑗−1 (𝑇𝐼𝐼𝐼𝐼𝑔)𝑡−𝑗+ û𝑡

(𝑇𝐼𝐼𝐼𝐼𝑔)𝑡 =µ + ∑ 𝜌𝑖(𝐺𝐺𝐺𝑔𝑚

𝑖=1 )𝑡−𝑖 +∑ 𝛹𝑗𝑛𝑗−1 (𝑇𝐼𝐼𝐼𝐼𝑔)𝑡−𝑗+ ě𝑡

Data from World Bank, and Uganda Bureau of Statistics

Ghost islands take the form of a dedicated traffic lane for vehicles turning right from the major road, at junctions under priority control, with non-physical separation provided by road markings to allocate road space. Priority T-junctions on single carriageway trunk roads in the UK with a

daily average of more than 500 vehicle movements on the minor road are required to have a ghost island, as defined by the

mandatory ‘Design Manual for Roads & Bridges’ (DMRB, TD 42/95). However, more recent guidance for non-trunk roads environments acknowledges that priority T-junctions without a ghost island "will often be able to cater for higher levels of turning traffic without resulting in significant congestion” (Manual for Streets 2).

There is currently no national design guidance in the UK regarding the provision of ghost islands at junctions on the local highway network.

This study will investigate, from the context of local highway networks, the two factors that design guidance indicates are the main considerations when deciding whether a ghost island is required: junction capacity and road safety.

STATS19 road safety records for priority T-junctions with and without ghost islands will be statistically analysed, to identify any differences in road safety performance through assessment of relevant collision factors, such as vehicle movements, road user groups and contributory factors. It is acknowledged that the combination of usage, behaviour, geometry and environment is unique at all junctions, therefore road safety data will be analysed for junctions that are as comparable as possible, particularly with respect to: Traffic flow patterns, including major and minor road flow volumes, distribution ratios, proportions of large vehicles, and daily/hourly flow profiles; Route type (e.g. urban arterial, rural minor, inter-urban); and Use by other modes, such as pedestrians and cyclists. The research will look to cover a range of traffic flow volumes (low/medium/high).

When should priority T-junctions include ghost island provision? Steven Windass (Supervisor: Haibo Chen)

The impact of ghost island provision on junction capacity, vehicle delay and congestion will be assessed utilising predictive modelling software (PICADY and the underlying empirical formulae). Multiple combinations of junction layout and traffic flow patterns will be tested for scenarios with and without a ghost island, based on variables typical for urban priority T-junctions in the UK.

Through consideration of the two branches of analysis and their relative importance, it is intended to define an indicative traffic flow threshold(s), or choice matrix, to inform the decision of whether to provide a ghost island at priority T-junctions on non-trunk roads. The impacts with respect to junction capacity and road safety will be assessed jointly through Cost-Benefit Analysis to understand the Net Present Value of ghost islands, considering the cost of traffic delay and Personal Injury Collisions (PICs) against the relative cost of ghost island construction at existing and proposed junctions. It is acknowledged within various highway design guides that the decision on whether to provide a ghost island should be based on consideration of all pertinent factors, not just capacity and road safety. Therefore, this study is intended to provide practitioners with research to inform the decision, particularly at preliminary design stages, but not to replace analysis of site-specific circumstances.

Priority T-Junction Traffic Streams:

PICADY Modelling

q = stream traffic flow qc-b = traffic turning from Arm C to Arm B

Great Britain, 2012: Road Length by Type

Trunk Roads 7,508 miles 3%

Local Highway Network 237,865 miles 97%

TOTAL 245,373 miles

6.07.08.09.0

10.011.0

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

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20

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12

Occ

up

an

cy(b

us

pass

en

ger

km

s p

er

bu

s se

rvic

e k

m)

Year

Average bus occupancy in Greater Manchester

Growth in bus occupancy in Greater ManchesterDissertation for Master of Transport Planning 2014

Student: Vi Ong 200813163 Supervisor: Dr. Jeremy Toner

Research QuestionsèSocial, economic, environmental or policy

determinants of occupancy rate changesservices in Greater Manchester

èPossible future patterns of change in bus passenger occupancy or loading rates in Greater Manchester

on local bus

Research ContextèDeregulation of local bus services in October 1986

èEnsuing ‘bus wars’ result in bus service provision (mileage/kilometreage) peaking at 40% above pre-deregulation level. Yet passenger use (patronage) over the same period declines by 30%.

èSince 1992, bus usage (in passenger kms) has fallen by 17%, yet bus occupancy (passenger kms per service km) has increased by 19%.

Project ContextThis topic was proposed by Transport for Greater Manchester (TfGM). TfGM is responsible for the delivery of transport infrastructure and services in Greater Manchester, and the implementation of transport policy set by the Greater Manchester Combined Authority.

This dissertation is intended to provide greater assurance for TfGM in the assumptions applied during planning for:èstop and terminal capacity at locations with high bus

volumes; andèthroughput capacity on corridors or roads with high bus

volumes in Greater Manchester.

DataManchester-specific historic and current data, relating to patronage and service provision, will be supplied by Transport for Greater Manchester (TfGM). Exclusive TfGM data sets to be utilised include the:èContinuous Passenger Survey (CPS); andèManual Bus Survey.

Other data sources to be utilised in comparisons with other case studies include reports from other transport authorities or departments, academic journals or papers, and trade/industry journals.

RisksThe project is heavily reliant on the use of secondary data. Issues may arise as a result of:èInconsistency in methodology for collection or

aggregation of data between different datasets or sources;èCommercial-in-confidence data that cannot be published

in the dissertation; orèUnavailability of data or insignificant sample sizes for

particular metrics.These risks can be mitigated by applying sensitivity testing with externally-sourced data where Manchester-specific data is not available.

Other risks pertain to time and resource availability.

Vi Ong, 2014. Piccadilly Gardens Bus and Metrolink stations, Manchester

MethodologyèReview of local bus public transport services

èLiterature review of similar studies to inform the development of potential hypotheses and variables to be tested and compared

èStatistics review from publicly-accessible sources such as Office for National Statistics, Department for Transport, TfGM and academic literature

èWorking to utilise exclusive TfGM in-house data resources

èDis-aggregate and aggregate statistics from different sources to provide common basis for comparison (e.g. location, time, etc.)

èComparisons to identify trends, including statistical analysis

200250300350400450500550600

405060708090

100110120

1975-1

976

1977-1

978

1979-1

980

1981-1

982

1983-1

984

1985-1

986

1987-1

988

1989-1

990

1991-1

992

1993-1

994

1995-1

996

1997-1

998

1999-2

000

2001-2

002

2003-2

004

2005-2

006

2007-2

008

2009-2

010

2011-2

012 P

atr

on

ag

e

(mill

ion

pass

en

ger

trip

s p

er

year)

Serv

ice p

rovi

sio

n

(mill

ion

bu

s m

iles

op

era

ted

per

year)

Financial Year

Service provision and patronage and in Greater Manchester

Service provision Patronage (unadjusted) Patronage (adjusted)

Data reproduced with permission from TfGM 2014, Email to Vi Ong, 23 April.

‘Adjusted’ patronage data is due to change in methodology adopted by TfGM.

There are 2 main objectives of this study• Understand how to encourage old people in

Taiwan to use electric bikes instead ofnormal scooter

• Analyze how to encourage old people whoonly walk or use public transportation to useelectric bikes, to increase their accessibility

Objectives

Analysis of existing dataTo know Taiwanese’s attitude toward electric bikes, i.e. How do they think of the advantages and disadvantages of electric bikes in general

Empirical work• Sample: old people in Taiwan’s suburban

• Questionnaire:

Older people’s travel behaviour

The main questions would include: Subject’sage and gender, how often do they travel,what vehicles do they use…

The factors that affect older people’swillingness

What factors affect the willingness of them tochange their habit more

Data analysis• Cross-analyse the data.

Summary and conclusionWith the data analyzed, we can conclude whatare the more practical ways to encourage olderpeople to use electric bikes

MethodologyAnalysis of existing data

To know Taiwanese’s attitude toward electric bikes, i.e. How do they think of the advantages and disadvantages of electric bikes in general

Empirical work• Sample: old people in Taiwan’s suburban

• Questionnaire:

Older people’s travel behaviour

The main questions would include: Subject’sage and gender, how often do they travel,what vehicles do they use…

The factors that affect older people’swillingness

What factors affect the willingness of them tochange their habit more

Data analysis• Cross-analyse the data.

Summary and conclusionWith the data analyzed, we can conclude whatare the more practical ways to encourage olderpeople to use electric bikes

Methodology

Taiwan at a glance Capital: Taipei

Population: 2.3million (2014)

Rural population: 5.2% (2012)

Total land area: 36,193 km²

GDP total: $517.019 billion (2013)

GDP per capita: $22,002 (2013)

In Taiwan,• The number of scooters tripled the number

of cars

• Lots of older people uses scooters

• This indicates that there is a big potential forscooter users to change to use electric bikes

In recent years, the number of electric scootersin Taiwan has increase dramatically, but thenumber of electric bikes has decrease

Background

Private passenger Cars

Motorcycle and scooters

Total registered number

5,909,115 15,139,628

Total people over 60 with license

1,739,658 2,063,130

How to encourage older people to use electric bikes in Taiwan?Yu-Hsuan Liu , Supervisor: Frances Hodgson

There are two expected outcome of the study:

• Identify the factors that would encourageolder people to use electric bikes

• Provide reference for future policy changes

Outcome

0

5000

10000

15000

20000

25000

30000

2006 2007 2008 2009 2010 2011

Electric bikes Electric scooters

Source: Data used from Taiwan Industrial Development Bureau - Electric scooter industry department Website (2014)

The advantages of electric bikes

• Help older people exercise

• Can use electric power when facing hills

• No emission

• Less costly

Electric bikes advantages