Posters summarizing dissertation research projects to date, presented by MA and MSc students at the Institute for Transport Studies (ITS), University of Leeds, May 2014. on.fb.me/1oSvcMT www.its.leeds.ac.uk/courses/masters/dissertation

1. 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
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.
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
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
1,020.4
1,360.5
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

2. 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
5
10
15
20
25
0 0.5 1 1.5
NumberofPropertiesLost
(approx)
Thousands
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.

3. 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?

4. 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.

5. 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

6. 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 InterchangeSchematic shockwave caused by traffic accident

7. • 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-bikesales
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)

8. 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)
0 200 400 600 800 1000
0.0000.0010.0020.0030.0040.0050.006
Time (seconds)
NOx(g/s)
0 200 400 600 800 1000
01020304050
Time (seconds)
VehicleSpeed(kmh
1
)
Speed Profile NOX Profile

9. •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

10. DEVELOPING TRIP GENERATION MODELS: COMBINING SURVEY AND MOBILE PHONE DATA
Author: 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 OUTCOMESLITERATURE 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
𝑡 𝑘 = 𝛼 + � 𝛽𝑖 𝑋𝑖𝑖 + 𝜀
𝑛
𝑖=1
(𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑒𝑒. 𝑎𝑎, 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.
• 𝑡 𝑘 = Area−wide all−purpose trip rate
for TAZ k (dependent variable)
• 𝑋𝑖𝑖 = 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 𝒗 𝒌 = 𝜶 + 𝜷 𝟏 𝑿 𝟏 + 𝜺 d% 𝑿 𝟏
2 𝒗 𝒌 = 𝜶 + 𝜷 𝟏 𝑿 𝟏 + 𝜷 𝟐 𝑿 𝟐 + 𝜺 e% 𝑿 𝟐
. ... … … …
n 𝒗 𝒌 = 𝜶 + 𝜷 𝟏 𝑿 𝟏 + 𝜷 𝟐 𝑿 𝟐+. . +𝜷 𝒏 𝑿 𝒏 + 𝜺 f% 𝑿 𝒏
• 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.

11. • 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
estimationMax 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

12. 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.

13. 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

14. • 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

15. SOURCE: Public Transport Authority of Western Australia
Workplace test group: One40 William
PHOTO CREDIT:
Hassell
One40 William
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 QuestionsIntroduction
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)
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?
Infographics created by the researcher based on cited source information.

16. 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

17. 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
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.
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).

18. 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 collectionAnalyse
Manchester
Airport
Documents Archival
records
Interviews

19. Protest -characterised as
being non violent they involve
a collection of people who
come together to protest a
cultural, social, political or
economic issue (Oliver, et al.
2012).
Riot - Riots are one example
of anti-government
demonstrations which is a
spontaneous outburst of
violence from a large group of
people (Barkan. 2012)
Flashmob - Strangers meet at
a predetermined public
location, perform an unusual
behaviour, and then disperse
(Duran. 2006)
Mediated Crowd - A new
social phenomenon relating
to collective action which
emerges as a result of the
virtual arena of ‘’Web 2.0’’
and new mobile technologies
Web 2.0– Characterised as
being a interactive social
media and user generated
content allowing users to
exchange content
The mobile criminal: Protests, Riots & Flashmobs
Emma O’Malley
Supervisor: Frances Hodgson
Aim
Understand how the communication aspect of social media can influence the
organisation of Protests, Riots and Flashmobs, specifically those which occur on
transport networks or are in response to changes on the network.
Objectives
• How is transport used as the stage andor 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 and
recover?
Background
The world is made up of networks whether environmental, social or economic; this project
looks at the links between communication networks and transport networks, specifically at how
communication networks as part of new social media is used to support action which disrupts
or 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 Networks
The creation of the mediated crowd is an example of how public communication practices have
changed in the twenty-first century (Baker. 2012). Social Media allows everyone with access to
have a voice and removes physical and spatial barriers allowing communication with a vast
amount of people who may share the same mindset (Baker. 2012; Moler. 2013). This allows for
a new form of social organisation where people can create or connect with social movements
outside existing channels far quicker and easier than ever before (Bartlett. 2013).
Preparedness
It is important to understand how this new form of communication influences the organisation
these forms of protest, especially those which occur spontaneously and have large negative
impacts, 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 the
form of movement but also related to the ‘new mobilties perspective’ includes the movement
of 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
protest
Conduct questionnaires with people who have been involved with protests
and interviews with participants who took a leadership role in organising
protests such as Critical Mass or the London Die- In.
Major Case Study
Critical Mass is an urban sustainability and cycling movement where once a
month a large groups of cyclist ride through a city in rush hour in order to
increase the visibility of cycling (Carlosson. 2002). The event is decentralised
with no one leader, today the internet has allowed participation to increase,
continue and transfer to other cities in a cheap and quick way (Blickstein and
Hanson. 2001). Other Case Studies will include London Die-In, Plane Stupid, and
London 2011 Riots.
Glossary of Terms
Expected Outcomes
Identify to what extend new social media
affects the organisation of social protests
in the UK
Establish what measures can be taken to
reduce negative impacts of such protests
and whether integrating new social media
can help this aim

20. 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

21. 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.
METHODOLOGY
EFFECT OF AXLE LOAD CONTROL REGIME
Purposive
Sampling
Technique
TARGET GROUP
1. Heads of Institutions /Senior
Officers (GPHA, GSA & HAULERS)
2. Transit Trucks only
GROUP 1
Personal Interviews
using Questionnaires
GROUP 2
Field Survey to Collect
Axle Weights
Secondary
Data & Design
Parameters of
Case Study
Design
Scenarios for
Sensitivity
Analysis
DATA ANALYSIS
Exploratory and
Confirmatory
ECONOMIC VIABILITY
HDM‐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 2012
Number of Weighed Trucks 14625 47480 49586 140311 194516
Number Overloaded 3773 7026 9452 34302 34245
Percentage 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

22. 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

23. 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.

24. 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. ∑ 𝑃𝑃𝑖𝑖 𝑋𝑋𝑖𝑖 ≤ 𝐼𝐼𝑖𝑖 ; 𝑋𝑋𝑖𝑖 ≥ 0
X2
X1
M/P0
A
B
M/P1
C
U1
U0
𝐶𝐶𝐶𝐶 = − � � 𝑋𝑋𝑖𝑖
𝑐𝑐
(𝑃𝑃𝑖𝑖 𝑈𝑈0) 𝑑𝑑𝑃𝑃𝑖𝑖
𝑖𝑖
𝑃𝑃𝑃
𝑃𝑃0
Neo-classical approach of welfare
Δ𝑀𝑀𝑀𝑀𝑀𝑀 = − � � 𝑋𝑋𝑖𝑖(𝑃𝑃, 𝐼𝐼) 𝑑𝑑𝑃𝑃𝑖𝑖
𝑖𝑖
𝑃𝑃𝑃
𝑃𝑃0
𝐶𝐶𝐶𝐶 = − ∫ ∑ 𝑋𝑋𝑖𝑖
𝑐𝑐
𝑃𝑃𝑖𝑖 𝑈𝑈0 𝑑𝑑𝑃𝑃𝑖𝑖𝑖𝑖
𝑃𝑃𝑃
𝑃𝑃0
= 𝑒𝑒 𝑃𝑃0
, 𝑈𝑈0 − 𝑒𝑒(𝑃𝑃1
, 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
𝑃𝑃𝑃𝑃𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡 = 𝑃𝑃𝑃𝑃(𝜀𝜀𝑏𝑏𝑏𝑏𝑏𝑏 − 𝜀𝜀𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡
< 𝑤𝑤𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡 − 𝑤𝑤𝑏𝑏𝑏𝑏𝑏𝑏)
𝑃𝑃𝑃𝑃𝑏𝑏𝑏𝑏𝑏𝑏 = 𝑃𝑃𝑃𝑃(𝜀𝜀𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡 − 𝜀𝜀𝑏𝑏𝑏𝑏𝑏𝑏
< 𝑤𝑤𝑏𝑏𝑏𝑏𝑏𝑏 − 𝑤𝑤𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡)
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:
Δ𝑀𝑀𝑀𝑀𝑀𝑀 = − ∫ ∑ 𝑋𝑋𝑖𝑖(𝑃𝑃, 𝐼𝐼) 𝑑𝑑𝑃𝑃𝑖𝑖𝑖𝑖 = −
𝑃𝑃𝑃
𝑃𝑃0 ∫ ∑ −
𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑
𝑑𝑑𝑃𝑃𝑖𝑖𝑖𝑖
𝑃𝑃𝑃
𝑃𝑃0
If marginal utility of income (denoted by λ) is
constant, then ∆MCS equals CV:
Δ𝑀𝑀𝑀𝑀𝑀𝑀 =
𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑
𝑣𝑣 𝑃𝑃1, 𝑦𝑦 − 𝑣𝑣 𝑃𝑃0, 𝑦𝑦 = 𝑒𝑒 𝑃𝑃0, 𝑈𝑈0 − 𝑒𝑒(𝑃𝑃1, 𝑈𝑈0)
The assumption of constant λ implies path-
independency in Marshallian demand, i.e. alike
welfare measure in Hickesian and Marshallian
approach.
Where: 𝜆𝜆 =
𝑑𝑑𝑉𝑉
𝑑𝑑𝑑𝑑
⟹
𝜕𝜕𝑥𝑥𝑥𝑥
𝜕𝜕𝑝𝑝𝑝𝑝
=
𝜕𝜕𝜕𝜕𝜕𝜕
𝜕𝜕𝜕𝜕𝜕𝜕
∀𝑖𝑖, 𝑗𝑗

25. 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_Boundaries
No. 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
“cheapest” route for 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
modelling 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.

26. 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
Study4 Scenarios
Objectives2
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
56
Distribution function
 Select the best 5% solutions and update
parameters of distribution through minimizing the
Kullback–Leibler distance, which is equivalent
to the program:
𝑀𝑎𝑥 𝐷 𝜇, 𝜎 = Max ln 𝑝 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

27. • 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

28. 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.