1. Exploring the relationship
between material poverty and
the travel behaviours of low
income populations
Presentation to ITLS, University of Sydney
Karen Lucas
Institute for Transport Studies.
University of Leeds
11/11/14

2. Aims and objectives
1. To explore how far the social and economic
disadvantages of low income populations can be
used to explain inequalities in their travel
behaviours.
2. Conversely to identify the extent to which the low
levels of travel activity of individuals living on low
incomes contributes to their social and economic
disadvantage
3. To use these models to predict the likely effects of
different policy measures on changing these travel
behavioural outcomes

3. Study rationale
 Significant increase in policy interest in transport and social exclusion –
economic austerity plus widening transport inequality in most cities
 Social criteria are not well represented within the standard mathematical
models that still dominate transport policy decisions
 Lots of qualitative research on the topic in a variety of geographical
contexts and with different socially disadvantaged groups
 Good for understanding the problem but not for measuring its extent
and intensity
 Difficult to replicate and apply within policy decision-making
 Few quantitative studies currently exist – the ones that tend to use new
dedicated data surveys and/or non-standard modelling techniques

4. Conceptual framework

5. Methodology
1. Define indicators of travel behaviour and social
disadvantage based on evidence of previous
qualitative studies
2. Set up a disaggregate model of travel behaviours
based on UK National Trip End Model (NTEM) using
National Travel Survey data
3. Undertake a bespoke local survey of personal travel
behaviours with 3-day diary in 2 study areas
4. Recreate NTEM model at local level and combine with
GIS-based models of accessibility

6. Identifying model indices
TRAVEL BEHAVIOUR
 Number of trips
 Journey distance
 Journey duration
 Mode of travel
 Trip purposes
 Vehicle ownership
 Driver licence
 Cost of travel (relative to
income)
 Public transport availability
SOCIAL DISADVANTAGE
 Household income
 Personal income
 Employment status
 Gender, age, ethnicity
 Disability (physical & cognitive)
 Housing security (tenure)
 Socio-Economic Group (SEG)
 Health and wellbeing
 Educational attainment
 Financial security

7. Step1: recreating the UK national
trip-end model
 2002-2010 National Travel Survey (approx. 250,000 trips by 19,000
individuals in 8,000 households in each survey year)
 DfT’s National Trip End Model (NTEM)
 Creates 8 categories of home-based trip purposes and 7 non-home-based
 By gender, household structure, car ownership and area type plus a 6-way
person-type distinction :
 children, over 65s, employment status
 Base line models have the form
Y = person-type + fem.fem + area-type area-type.area-type + adults.Nadults + cars. Ncars

8. Observations from baseline
model
 Area constants show some variation but differences less than expected
(non modal variations)
 London has the lowest frequency while having one of the lowest trip distances
 Rural areas have the greatest average trip length.
 For person type effects
 Part time employees make greatest number of trips while children and retired people make
the least.
 Full time workers and students demonstrate highest values for trip length (modal effects
are represented by the fact that though full time employees travel further, students spend
more time per trips (lower speed rate).
 Baseline variables have consistent effects:
 2.5 more trips per additional car in the household (travelling in average 1.4 extra miles per
trip),
 1.4 trips per extra adult in the household (which leads to fewer trip distance but larger
duration)
 Women make slightly more but shorter trips

9. Step 2: Adding new variables of
social disadvantage
Variable name Type Description
Household characteristics
Household income Categorical 22 extended categories
Children in HH Dummy 1 if there are children in the household
Individual characteristics
Driving licence Dummy 1 if individual owns a driving license
Social disadvantages
Non-white Dummy 1 if non-white
Mobility difficulties Dummy 1 if individual has mobility difficulties
Single parent Dummy 1 if Single parent

10. Results of extended models: trip frequency
and distance
 Presence of children in household
 2 extra trip per week and 1.2 less miles per trip
 Non-white population
 2 trips less per week but with no distance effect
 Mobility difficulties
 2 trips less per week and 0.6 less miles per trip
 Single parents
 1 trip more per week and 0.9 miles less per trip

11. Income effects: journey purposes
(frequency)
3
3
2
2
1
1
0
-1
-1
Income Effects on trip Frequency
0 10 20 30 40 50 60 70 80 90 100 110
Additional Trips per Week
Household Income per annum (£ ,000)
All Commute Social VFR Shopping & PB EB Educ./escort

12. Income effects: journey purposes
(distance)
20
15
10
5
0
-5
-10
Income Effects on trip distance
0 10 20 30 40 50 60 70 80 90 100 110
Additional miles per Trip
Household Income per annum (£ ,000)
All Commute Social VFR Shopping & PB EB Educ./escort

13. Local area study: Merseyside
1. Undertake a bespoke local survey of personal travel behaviours
with 3-day travel diary in 2 deprived areas in the same city
i. Area 1 = high access to services and public transport
ii. Area 2 = low access to services and transport
iii. 700 individuals sampled (350 in each area)
iv. Questions on household composition, personal socio-demographic
characteristics, transport resources as per NTS
2. Recreate disaggregated NTEM econometric model of travel
behavours at local level
3. Combine with GIS-based to create accessibility matrices for and
GWLR models to test the effects of supply side issues– e.g. land
use, transport supply, built environment.
4. Agent-based micro simulation modelling to test the effect of
different policy scenarios.

14. Case study areas

15. Deprivation

16. Car ownership

17. Key research question
Do people not travel because of their income poverty or is their
transport poverty (at least partially) a cause of their social
disadvantage?
a. Personal constraints and circumstances, i.e. they do not
travel because they cannot afford to, or do not have the
opportunity, or ability to participate in activities;
b. The transport, i.e. they are unable to access transport or
the transport is unavailable to take them to the places they
need to go or at the times when they need to travel;
c. Land use system & location, i.e. they live in places where
they do not need to travel in order to access activities that
they wish to participate in.

18. Sample description
 502 achieved sample of in scope records
 230 Anfield; 272 Leasowe
 241 men 261 women
 All 16-65 years
 50% had combined h/h incomes under £20,000
 Only 18% had combined h/h incomes above £30,000
 50% no car, 38% 1 car h/h
 488 valid 1 day retrospective travel diary
 1286 total recorded weekday trips
 525 Anfiled; 871 Leasowe
 Only 182 returned a further 2 diary days – used 1 day diary
data only

19. Modelled indicators

20. Model results (trips)
 Small sample so low R2 values – refer to Beta
 Estimates effect of variable compared with constant
reference case
 References case = male, Leasowe district, working full
time who is the only adult in the household = 4.374 mean
trips per day
 Significant area effect – being from Anfield reduces the
average by 0.816 trips per day
 Part-time worker have 0.616 more trips than full-time –
all other categories have less trips than full-time

21. Model results (time/distance)
 Mean trip time for the reference case is 26 minutes.
 No real district effect
 Retirees, non-workers have ½ average travel times
compared to full-time workers (so being time rich does
not mean spending more time travelling)
 Mean trip distance is 6.7km –less than 60% of national
average for all groups
 Shorter average trip distances in Anfield than Leasowe by
0.68km - but the t statistic is only -0.759
 Being in an economic activity category other than full time
working more than halves trip distance.

22. Model results (average weekly
travel spend)
 Anfield residents spend £1.95 less than residents in
Leasowe, although the t statistic is under 2.
 With each car available to the household:
 Travel spend increases by £2.20 per week and the t
statistic is over 2.
 Average trip distance increases by over 1.1km also with a
t statistic over 2.
 There is a mean reduction in journey time of 2.2 minutes
though the t statistic is weaker.

23. Extended model (social)
 Gender - Beta value for female versus male base increases
for weekly travel spend from -£0.56 to -£1.21
 Presence of children in h/h does not appear to have a
significant impact on number of trips or average travel times.
 Presence of one or more children under 5 increases
average trip distances and weekly travel spend.
 Disability and single parenthood has no significant effects
(but very small samples)
 Education levels - a person without Level 2 equivalent
spends on average £3.47 per week less than those with.

24. Extended model (income)
 Very uneven results with no
clear picture emerging for trip
distances or durations
 Trip frequencies increase
significantly for £25,000 plus
 Lower income groups appear
to spend more per week on
travel (but very low t values)

25. Indices of accessibility
 3 off-the-shelf measures of accessibility
1. UK Index of Multiple Deprivation – mean road distance from
Lower Super Output Areas (LSOA) centroid to post office,
primary school, food shop and doctors
2. Proportion of people in each LSOA that can access eight
services (the four above plus; employment centres, Further
Education colleges, hospitals and town centres) by public
transport, walking and cycling
3. Proportion of the population in an Output Area with the
capacity to reach their current place of work using only walking
and cycling
 Conflicting results and inconclusive evidence across the 3
measures plus no t values were over 2

26. Next steps
 GWLR analysis with Liang and Corrine to look at effects of
transport supply, land use opportunities and built
environment.
 GIS-based public transport accessibility mapping LSOA
using TRACC (ACCESSION2) software
 Personal time-based measures of accessibility (with Tijs
Neutens at University of Ghent)
 Agent-based modelling (with Aruna Sivikumar Imperial
College and colleagues in Leeds School of Geography)
 Final policymakers’ dissemination meeting with Merseytravel
January 2015