Exploring the relationship between material poverty and the travel behaviours of low income populations

Presentation to ITLS, University of Sydney

Karen LucasInstitute for Transport Studies.

University of Leeds11/11/14

Aims and objectives1. 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

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

Conceptual framework

Methodology1. 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

Identifying model indicesTRAVEL 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

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

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

Step 2: Adding new variables of social disadvantageVariable 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

Results of extended models: trip frequency and distance

Presence of children in household2 extra trip per week and 1.2 less miles per trip

Non-white population2 trips less per week but with no distance effect

Mobility difficulties2 trips less per week and 0.6 less miles per trip

Single parents1 trip more per week and 0.9 miles less per trip

Income effects: journey purposes (frequency)

0 10 20 30 40 50 60 70 80 90 100 110

-1

0

1

2

3Income Effects on trip Frequency

All Commute Social VFR

Shopping & PB EB Educ./escort

Household Income per annum (£ ,000)

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Income effects: journey purposes (distance)

0 10 20 30 40 50 60 70 80 90 100 110

-10

-5

0

5

10

15

20

Income Effects on trip distance

All Commute Social VFR

Shopping & PB EB Educ./escort

Household Income per annum (£ ,000)

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iles

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Local area study: Merseyside1. 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.

Case study areas

Deprivation

Car ownership

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.

Sample description502 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

Modelled indicators

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

Model results (time/distance)Mean trip time for the reference case is 26

minutes. No real district effectRetirees, 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 groupsShorter average trip distances in Anfield than

Leasowe by 0.68km - but the t statistic is only -0.759Being in an economic activity category other than

full time working more than halves trip distance.

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.

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.

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)

Indices of accessibility3 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

Next stepsGWLR 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