Worcester – Sustainable Travel Demonstration Town Travel behaviour research

Final evaluation report for Worcestershire County Council March 2009

CONTENTS

Page

1

INTRODUCTION

1

1.1

This report

1

1.2

Choose how you move in Worcester

1

2

RESEARCH DESIGN

4

2.1

Research concept

4

2.2

Methodology

5

2.2.1 Overview

5

2.2.2 Travel behaviour surveys

6

2.2.3 In-depth surveys (attitudes and potentials)

8

2.2.4 Sampling

8

Implementation of surveys

9

2.3.1 Travel behaviour surveys

9

2.3.2 In-depth surveys (attitudes and potentials)

11

2.3

3

4

TRAVEL BEHAVIOUR

12

3.1

Introduction

12

3.2

Mode choice

13

3.3

Personal mobility

16

3.4

Activities

17

3.5

Car usage

18

3.6

Spatial distribution

19

3.7

Mode choice by time of day, trip purpose and socio-demography

21

3.8

Carbon dioxide (CO2) reductions

25

PERCEPTIONS AND ATTITUDES

26

4.1

26

Introduction

5

6

4.2

Perceptions of traffic situation

26

4.3

Attitudes towards promoting different travel modes

30

4.4

Perceptions of public transport

33

POTENTIAL FOR BEHAVIOUR CHANGE

34

5.1

Introduction

34

5.2

Potential for reduction in car use

34

5.3

Potential for public transport

38

5.4

Potential for cycling

40

5.5

Potential for walking

42

5.6

Conclusion

43

EVALUATION OF CHOOSE HOW YOU MOVE ITM PROGRAMME

46

6.1

Introduction

46

6.2

Changes in travel behaviour across the ITM target population

47

6.3

Stage-by-stage analysis of the ITM programme

50

ANNEXES A

GLOSSARY

B

PRINCIPLES OF THE POTENTIALS ANALYSIS

C

STATISTICAL SIGNIFICANCE OF CHANGES IN MODE CHOICE

D

DATA TABLES

1

INTRODUCTION

1.1

This report

This report presents an evaluation of Worcester’s Choose how you move Sustainable Travel Demonstration Town (STDT) Programme. It is based on travel behaviour surveys and other research conducted across the city in 2004 and 2008 (i.e. before and after implementation of the main STDT measures). The analysis is presented in three main parts, focusing on: •

changes in travel behaviour, attitudes and the potential for change occurring across Worcester between 2004 and 2008 (Chapters 3 to 5 of this report);

•

a measure of the specific effects on travel behaviour of the Individualised Travel Marketing programme undertaken in parts of the city over this period (Chapter 6); and

•

an updated (2008) set of data tables describing travel behaviour across the city (Annex D).

The report has been prepared by Sustrans and Socialdata for Worcestershire County Council. 1.2

Choose how you move in Worcester

In April 2004, Worcester was selected by the Department for Transport (DfT) as one of three STDTs in England (along with Darlington and Peterborough) to showcase the role of ‘smarter choices’ measures in reducing car use. The aim of the programme was to achieve a significant shift from single-occupancy car use to sustainable modes of travel with the added benefits of reduced congestion, improved health and fitness, and improved access to health, employment, schools and leisure facilities. It was anticipated that the five-year duration would be sufficient to build real longer-term behaviour change. Worcester’s STDT programme (branded Choose how you move) combined smarter choices measures with improved transport services and infrastructure (including park and ride, improved local bus services and walking and cycling networks). The combination of infrastructure and soft transport measures was intended, as stated in Worcester’s original STDT bid, to “lead to a city that has vital and bustling economy, [and] residents and visitors that can easily move around in a way that not only tackles environmental issues but also addresses social exclusion.” In early 2005, Sustrans and Socialdata were contracted by Worcestershire County Council to deliver an Individualised Travel Marketing (ITM) project as a

1

major component of the overall Choose how you move programme. The range of measures developed specifically for Choose how you move during the period 2004-09 is described in Table 1.1. Table 1.1

Summary of Choose how you move measures

Type of measure

Specific measure

Travel information

•

Information on the Choose how you move section of Worcestershire County Council’s website

•

New public transport maps and timetables (also used in the ITM programme)

•

Individualised Travel Marketing

•

Public transport marketing campaigns

•

Car sharing marketing campaign

•

Cycling marketing campaign

•

Workplace travel plans

•

School travel plans

•

Service improvements

•

Regular timetable change dates

•

Improved infrastructure and information at bus stops

•

New ticketing initiatives

•

Cycle loan scheme

•

Tour of Britain, Pedal in the Park and Dr Bike events

•

Adult and child cycling training

•

Development of new maps and leisure route information (also used in the ITM programme)

•

Walk to school week

•

Walking buses

•

Walk to work events

•

Car club

•

Car sharing database for employers

Marketing and promotions

Travel planning

Public transport

Cycling

Walking

Other measures

2

The above measures were implemented alongside other Local Transport Plan initiatives across the transport strategy areas: maintenance schemes, traffic management and calming, cycling and walking schemes, safety schemes and travel plans.

3

2

RESEARCH DESIGN

2.1

Research concept

This evaluation is based on a series of travel behaviour surveys conducted across Worcester in 2004 and 2008. The work was undertaken by Socialdata with support from Sustrans using a consistent overall research method, consisting of extensive household travel behaviour surveys coupled with indepth interviews to provide information on attitudes, perceptions and the potential for change in travel behaviour. The aim of both baseline (2004) and final research (2008) programmes was to provide a detailed, representative picture of day-to-day travel behaviour among Worcester residents, together with an assessment of their attitudes towards, and perceptions of local transport. The final part of the research, using Socialdata’s unique ‘situational’ analysis1 model, aimed to reveal the reasons for people’s travel mode choices, and so to provide a measure of the potential for travel behaviour change. Together, the baseline and final research programmes were designed primarily to enable a direct comparison of travel behaviour, attitudes and potentials across Worcester, before and after delivery of the city’s Choose how you move programme. The same approach was taken in the other two STDTs, Peterborough and Darlington, to allow for a comparison of the overall changes taking place in all three locations. Given the city-wide scope of both the Choose how you move programme and the travel behaviour research, this overall analysis cannot distinguish between the effects of the programme itself (or its individual components) and other local transport measures implemented over the same timeframe. Equally, the changes observed between 2004 and 2008 are likely to have been influenced by the unknown effects of other uncontrolled factors (e.g. fuel price fluctuations, changing household car ownership levels). However, the design of the Choose how you move ITM programme – and the availability of detailed data on target areas, participating households, etc – does allow for an evaluation specifically of the effects of this scheme on travel behaviour. In particular, the exclusion of the north-western part of the city (Arboretum, Claines and St Stephen wards) from the ITM programme provides a ‘control’ area which (for the purposes of this evaluation) it is assumed was subject to the same overall package of transport measures as the rest of the city, apart from ITM.

1

This ‘situational’ approach is fully described in Annex B.

4

It was understood when this research programme was conceived that Worcestershire County Council and its partners would conduct further monitoring – for example of bus patronage, road traffic and cycle route usage – to provide a comprehensive evaluation of the Choose how you move programme. The travel behaviour research was designed and conducted separately from this wider monitoring work, so a consideration of the links between the behavioural data and traffic counts etc is beyond the scope of the analysis presented here. 2.2

Methodology

2.2.1 Overview The Socialdata travel behaviour research methodology (known as the New KONTIV Design) uses a mixture of survey forms and exploratory techniques to obtain a complete portrayal of mobility behaviour. This ‘satellite structure’ consisting of validation, in-depth and follow-up research activities around the central travel behaviour survey is illustrated in Figure 2.1. Figure 2.1

Travel behaviour research ‘satellite structure’

5

Originally developed for the German transport ministry during the 1970s, this approach has been used widely in national and international travel surveys (including the Netherlands National Travel Survey with an annual sample of 50,000 people net) and in the evaluation of ITM projects in Europe, Australia and North America. Socialdata's survey techniques have been the subject of continuous external expert scrutiny for more than 35 years and have been independently audited and verified on a number of occasions. Working with Sustrans, Socialdata has undertaken travel behaviour surveys with net samples totalling more than 50,000 people in England since 2002-03, many of which have been used to evaluate the effects of TravelSmart ITM programmes. However the largest individual surveys, with net samples of around 4,000 people each, were undertaken in 2004 and 2008 in each of the three STDTs. Drawing upon this and broader experience in social and market research, Socialdata has developed a unique approach to researching public perceptions of and attitudes towards transport issues, and measuring the potential for travel behaviour change. Linked to the behavioural survey, this in-depth technique uses personal interviews and has been applied in numerous research programmes across the world, notably to support the Western Australia TravelSmart programme and as part of the STDT research. Two main research tools were used in Worcester and the other STDTs: •

household and individual travel surveys using a self-administered mail-back format coupled with motivation by post and telephone; and

•

in-depth ‘situational’ research, using personal interviews to collect information about attitudes, reasons for mode choice and potentials for change.2

The methodology for each component of the research is described in the following sections.

2.2.2 Travel behaviour surveys The behavioural survey uses a mail-back diary technique, proven to be the most reliable method for collecting data on travel behaviour. This consists of a questionnaire sent to each household in the survey sample together with a set of individual travel diaries for all household members for a nominated day of the week. The survey sample includes households completing travel diaries for all seven days of the week.

2

This ‘situational’ approach is fully described in Annex B.

6

The survey form is designed to collect information on individual activities performed by all household members (including children) at all out-of-home destinations on the nominated travel day. Rather than relying on prescriptive and potentially confusing predefined categories, this questionnaire design allows respondents to report their activities in their own words, helping to increase the quality and accuracy of the data. For the STDT surveys, an enhanced survey design was used to allow detailed data on trip stages (including walking) and other information to be collected. This provided the basis for further evaluation of physical activity (health) benefits and carbon dioxide emissions reductions. The same follow-up telephone surveys measured people’s perceptions and expectations compared with their actual experiences of using public transport, walking and cycling. The travel behaviour surveys were conducted using the following step-by-step process, subject to rigorous quality control by Socialdata fieldwork managers in Bristol with support from technical staff in Munich. •

Mailing of an announcement letter (bearing the County Council logo and signed by the transport portfolio holder or senior officer) to all households in the gross sample.

•

Mailing of survey forms and official covering letter (as above) to all households in the gross sample.

•

Mailing of an official reminder letter (as above) to all households who failed to respond within one week.

•

Mailing of a second reminder letter (on Socialdata headed paper and signed by the Socialdata fieldwork manager) to non-responding households a further week later.

•

Reminder telephone calls to non-responding households each week to offer support in completing the forms and to motivate people to return them.

•

Mailing of replacement survey forms followed by third reminder letters and motivation phone calls if required.

A number of further steps were taken to ensure data quality and high response rates. These are outlined below. •

All returned travel diaries were checked by Socialdata staff to see that they were complete and correctly filled out. If they contained implausible or incomplete statements or if clarification was needed, households were phoned to check the information given (checking of ‘non-reported items’). Particular attention was paid to the verification of activity patterns to ensure that all parts of ‘trip chains’ were included. Telephone exploration was also used to collect data on mobility behaviour of children under the age of six.

7

•

All envelopes were personally addressed to the household and carried either the local authority or Socialdata logo as appropriate (see above) and a Royal Mail stamp as opposed to a franking mark.

•

A free phone number was included on the front of the survey form to enable residents to contact Socialdata with any queries.

2.2.3 In-depth surveys (attitudes and potentials) The ‘satellite’ survey design allows for in-depth research based on face-to-face interviews to collect information about attitudes towards and perceptions of local transport issues, reasons for mode choice, and potentials for change. These personal interviews, lasting on average between 45 and 60 minutes, were conducted at the respondent’s home by Socialdata using an interactive technique. During the first part of the interview, respondents were asked to report the reasons for their mode choice on specific car trips recorded in their travel diaries, and the possibilities for using walking, cycling or public transport. The resulting data provide information on actual behaviour rather than average or hypothetical behaviour. The second part of the interview focused on perceptions of travel and transport issues; attitudes towards different transport policy options; and satisfaction with public transport.

2.2.4 Sampling The sampling strategy for each survey (2004 and 2008) was determined by the requirement to get a representative picture of mobility for the population of Worcester. •

The survey area covered all residential households in Worcester (a population of approximately 94,000 people).

•

The samples for each survey were randomly selected and stratified to ensure a sufficient sample size to provide reliable data at ward level. The research did not use a ‘panel’ design (where the same participants are deliberately tracked over time). If any individual did participate in both surveys, this was purely as a function of random sampling.

•

The samples included households with and without known telephone numbers (the latter requiring a modified survey procedure using repeat postal contact in place of telephone motivation).

The samples were drawn from a commercially available database of postal addresses and telephone numbers, AFD Names and Numbers, excluding

8

households registered with the Mailing and Telephone Preference Services. This database incorporates the Royal Mail Postal Address File (PAF), the most up-to-date and complete address-only database in the UK. Sub-samples from the behavioural surveys were selected for the in-depth research in 2004 and 2008. These samples were also constructed to cover the whole city and were stratified to give a sufficient basis for analysis of different modes. 2.3

Implementation of surveys

2.3.1 Travel behaviour surveys Sample and response details for the baseline and final travel behaviour surveys are shown in Table 2.1, overleaf. The figures illustrate a high level of interest and willingness to participate in the surveys amongst households in Worcester. Further details of final survey implementation (for both travel behaviour and indepth surveys) are available in the field report delivered in December 2008.

9

603 3,097 1,839 4,125 59% 3,700

Sample loss3

Adjusted household gross

Returns (households)

Returns (persons)

Response (%)

Contracted persons

71%

2,303

1,048

1,470

230

1,700

Households with telephone

49%

1,822

791

1,627

373

2,000

Households without telephone

3,700

63%

4,072

1,845

2,949

551

3,500

TOTAL

75%

1,850

865

1,148

252

1,400

Households with telephone

54%

2,222

980

1,801

299

2,100

Households without telephone

Final survey (2008)

10

3 Sample loss: incomplete address, unknown address, address without letterbox, no private address, company address, house unoccupied, addressee deceased, householder moved away, householder absent for a longer period.

3,700

TOTAL

Baseline survey (2004)

Sample and response details for travel behaviour surveys

Mail-out gross

Table 2.1

2.3.2 In-depth surveys (attitudes and potentials) Sample and response details for the in-depth research interviews are shown in Table 2.2. Table 2.2

Sample and response details for in-depth surveys Baseline survey (2004)

Final survey (2008)

TOTAL

TOTAL

Gross sample

600

600

Sample loss4

50

35

Adjusted gross sample

550

565

Returns (persons)

400

409

Response (%)

73%

72%

Contracted persons

400

400

4

Sample loss: incomplete address, unknown address, address without letterbox, no private address, company address, house unoccupied, addressee deceased, householder moved away, householder absent for a longer period.

11

3

TRAVEL BEHAVIOUR

3.1

Introduction

This chapter reviews the changes in travel behaviour measured across the Worcester urban area between 2004 and 2008. It is based on analyses of data generated by the baseline and final travel behaviour surveys, and in particular those relating to a number of key indicators of day-to-day mobility. The main indicators selected for the evaluation were: •

Trips per person per year by main mode;5

•

Personal daily mobility (including trip rates, distances travelled and trip purposes);

•

Individuals’ exposure to active travel (walking and cycling); and

•

Car use measured by actual usage, number of trips, travel time, distance travelled and average occupancy per private car per day.

This section presents the overall changes in these indicators between the survey periods in autumn/winter 2004 and the same time of year in 2008, with no adjustment for any external factors or background influences. As such it provides a measure of the overall effects of the Choose how you move programme, but it is stressed that the following limitations apply: •

the effects of any external factors and/or background influences (e.g. fuel price fluctuations; bus service changes) cannot be separated out from the overall changes; and

•

the effects of specific Choose how you move measures cannot be isolated from the overall changes.

For the purposes of this evaluation, a trip is defined as one-way movement generated by an out-of-home activity, plus the return leg of the journey. The number of trips per person per year was calculated using the standard formula that, on average, a person will spend 341 days of the year at home. This takes into account the days that a person travels away, for example on holiday or business. The analysis also focuses on day-to-day personal travel behaviour, so excludes commercial trips and trips of over 100km.

5

The main mode of a trip is determined according to the following ranking: public transport; motorised private modes (car, motorbike); non-motorised modes (bicycle, walking).

12

3.2

Mode choice

Changes in mode choice among Worcester residents between 2004 and 2008 are summarised in Figure 3.1, which shows the percentage of all trips by main mode. The share of car-as-driver trips fell from 45% to 42% and the overall share of car trips from 66% to 62%. There were corresponding increases in levels of walking and bus use. Figure 3.1

Changes in percentage of trips by main mode

13

Figure 3.2 expresses the changes in mode choice in terms of trips per person per year, and the relative changes between 2004 and 2008. It shows a seven percent reduction in car-as-driver trips, achieved by switching 33 trips per person per year to other modes (i.e. an average across the population of less than one trip per person per week). Car-as-passenger trips also declined by four per cent. Among the sustainable travel modes, walking saw the biggest gains in absolute terms with an additional 29 trips per person per year being made on foot, a relative increase of 11%. Bus also gained a total of nine trips per person per year resulting in a relative increase of 20%, but other public transport lost two trips per person per year resulting in a decrease of 15%. Cycling saw a relative increase of 19%, or an average of five additional trips per person per year. Figure 3.2

Changes in trips by main mode (trips per person/year)

14

The changes in mode choice reported above led to an increase in the time spent by Worcester residents using active travel modes (walking and cycling). As shown in Figure 3.3, the average total time spent using such modes in 2004 (including the walking and/or cycling legs of car and public transport trips) was 117 hours per person per year. By 2008 this had risen by eight hours per person per year (seven percent). Figure 3.3

Changes in active travel time

15

3.3 Personal mobility As shown in Figure 3.4, there were no major changes in personal daily mobility between 2004 and 2008. This confirms that despite notable changes in how people travel (see above), there was no great impact on the number of activities undertaken on a daily basis or on daily travel demand (measured by number of trips and distances travelled). Only average daily travel time saw a slight decrease, from 60 to 59 minutes. Figure 3.4

Changes in personal mobility (per person/day)

16

3.4

Activities

Figure 3.5 illustrates data on activities (i.e. the reasons for people’s trips) in 2004 and 2008. This shows little change in the types of trip being undertaken by residents of the Worcester urban area over the lifetime of the Choose how you move programme. In 2008, commuting remained constant at just over one fifth of all trips, with shopping and leisure remaining the largest trip generators, accounting together for nearly half of all trips in 2008, down just one percent from 2004. Figure 3.5

Changes in activities (%)

17

3.5 Car usage The changes in car use for day-to-day trips6 shown in Figure 3.6 reflect the reduction in car-as-driver trips shown in Figures 3.1 and 3.2. Figure 3.6 shows reductions between 2004 and 2008 in the share of cars used each day (from 74% to 71%), trips (from 2.4 to 2.2), duration of use (from 44 to 41 minutes), and distances travelled (from 25.3km to 23.0km). Average car occupancy remained constant at 1.5 people per trip. Figure 3.6

Changes in car usage (per car/day)

6

It is particularly important to note in this context the exclusion from this analysis of trips of over 100km.

18

Figure 3.7 shows changes in car distances at the population level. Although the number of cars owned by households across the Worcester urban area rose from 49,500 in 2004 to 52,000 in 2008, the distance travelled per car per day for day-to-day trips7 fell from 25.3km to 23.0km, resulting in a net saving of 19.3 million car km per year, a relative reduction of nine percent. Figure 3.7

7

Changes in car distances travelled

As stated earlier, this analysis excludes the small proportion of trips over 100km.

19

3.6 Spatial distribution An analysis of the spatial distribution of people’s day-to-day travel in 2004 and 2008 shows no change in the share of trips going beyond the Worcester urban area (see Figure 3.8). This is consistent with data shown in Figure 3.4, in that in some ways there appears to have been little change in travel patterns across the city. Figure 3.8

Spatial distribution

20

3.7 Mode choice by time of day, trip purpose and socio-demography The following three charts show how the changes in mode choice between 2004 and 2008 were distributed by time of day, trip purpose and broad sociodemographic group. For the purposes of this analysis, trips by sustainable travel modes (walking, cycling and public transport) are aggregated and compared with car-as-driver trips. Between 2004 and 2008 there was an overall increase of 12% in use of sustainable travel modes for all trip purposes (increasing from a baseline index of 100 to 112). The overall reduction in car-as-driver trips of seven percent is shown by the change from a baseline index of 100 to 93. Figure 3.9 shows that the use of sustainable travel modes increased across all times of day, with the greatest relative increase occurring between 5am and 9am (i.e. during the morning peak). However, the greatest relative reduction in car-as-driver driver trips occurred between 9am and 3pm, outside peak travel times. Figure 3.9

Changes in mode choice by time of day (%)

21

Figure 3.10 shows the changes in mode choice for different types of trip. Increases in the use of sustainable travel modes were greatest for education and, in particular, leisure. Reductions in car-as-driver trips were only observed for purposes other than work and education. On the basis that work and education trips are likely to be relatively constrained in terms of their timing and destination, this is to be expected. Figure 3.10

Changes in mode choice by trip purpose (%)

22

The distribution of travel behaviour change by age and gender is shown in Figure 3.11. Increases in the use of sustainable travel modes were observed across all age and gender groups, with the most notable relative growth occurring in people aged over 60 followed by women aged between 20 and 59. The greatest relative reductions in car-as-driver trips were observed among men aged between 20 and 59. No reduction was observed among people aged under 20 years. Figure 3.11

Changes in mode choice by age and gender (%)

23

Figure 3.12 summarises the changes in mode choice among broad sociodemographic groups. In this case, the proportions of car-as-driver and car-aspassenger trips are shown alongside the combined share for sustainable travel modes using the traffic light symbols of red, amber and green. As expected from the previous analyses, Figure 3.12 shows modal shift towards sustainable travel modes occurring across all groups. The greatest relative changes took place among the not employed group (which includes those with home duties as well as unemployed people) and the retired/pensioner group. Of all groups, changes were least pronounced among employed women and pre-school children. Figure 3.12

Changes in mode choice by socio-demographic group (%)

24

3.8 Carbon dioxide (CO2) reductions The evaluation allows for an estimate of the reduction in annual CO2 emissions from personal car use among Worcester residents between 2004 and 2008 (see Figure 3.13). Based on a UK fleet average CO2 emissions factor,8 the reduction of approximately 19 million car kilometres per year (see Figure 3.7) would result in annual savings of around 3,900 tonnes of CO2. Figure 3.13

Estimated annual CO2 reductions from personal car use

Reduction in car kilometres (per year)

19m

Reduction in CO2 emissions (per year)

3,900 tonnes

8

Based on average emissions of 207.5 g CO2 per vehicle km, from Defra’s (2007) Guidelines to GHG Conversion Factors for Company Reporting.

25

4

PERCEPTIONS AND ATTITUDES

4.1 Introduction This chapter presents key findings from the part of the in-depth research relating to attitudes and perceptions. These findings are based on interview responses from the in-depth surveys (see section 2.2.3). 4.2 Perceptions of traffic situation Perceptions of the local road traffic situation in 2004 and 2008 are summarised in Figure 4.1. There was a growth over this period (from seven percent to 11%) in the share of respondents perceiving no increase in traffic in recent years. Perceptions of future traffic growth were also much improved. While a majority of 2008 respondents expected an increase in car traffic (and evaluated this increase negatively), a much greater proportion than in 2004 did not (36%, up from just 13% in 2004). A small (and smaller than in 2004) share of respondents said that future traffic growth would be positive. Figure 4.1

Evolution of car traffic

26

Figure 4.2 shows findings of a similar assessment carried out for public transport, cycling and walking. In general, the survey showed greater optimism in 2008 than in 2004 regarding future growth in sustainable travel. Notably, at least half of 2008 respondents expected future growth in all three sustainable travel modes. An almost identical majority of respondents said that they expected increases in bicycle use as said they expected increases in car use (see Figure 4.1), but for bicycle use the increase was widely said to be positive rather than negative. A smaller proportion of respondents said that they expected increases in walking and public transport, although the proportions had risen since 2004 (from 30% to 50% and 26% to 50%, respectively). Again, these increases were almost always said to be positive by those who expected them. Figure 4.2

Evolution of sustainable travel modes

27

Interviewees in Worcester were also asked about their perceptions of risk for different travel modes. Figure 4.3 shows that the balance between perceived low and high risk of traffic accidents to pedestrians decreased from 46% perceiving a high risk in 2004 to 32% in 2008. For cycling, the proportion of respondents perceiving a high risk of traffic accident also showed a notable decrease from 82% in 2004 to 69% in 2008. These can be seen as potentially positive signs for future development of walking and cycling in the city. Figure 4.3

Risk of a traffic accident

28

The in-depth surveys also asked about people’s perceptions of the consequences of car traffic, and of the potential for new technology to solve traffic problems (see Figure 4.4). There was a minimal increase compared with 2004 in the proportion of respondents finding the consequences of car traffic bearable (up from 58% to 59%). The proportion of respondents stating that the consequences were not so bearable increased from 27% to 32%, although the proportion saying that the consequences were no longer bearable fell from 15% to nine percent. The level of public faith in technological solutions for solving traffic problems decreased from 52% in 2004 to 46% in 2008. Figure 4.4

Traffic problems and technological solutions

29

Public perceptions of a range of strategies for tackling traffic problems are summarised in Figure 4.5. Notable changes between 2004 and 2008 are a decrease in the perceived effectiveness of pedestrianisation (index of -6 in 2008 down from +3 in 2004) and a large increase in the perceived effectiveness of limiting car traffic (index of +34 in 2008 compared with +8 in 2004). Developing facilities for cycling and public transport, however, continued to be perceived as the most effective measures by a considerable margin (with indices of +78 and +84, respectively). This analysis also suggests, when compared with Figure 4.4, that respondents viewed measures to improve facilities for sustainable travel modes (especially cycling and public transport) as more effective than technological solutions to traffic problems. Figure 4.5

Suggestions to solve traffic problems

30

4.3 Attitudes towards promoting different travel modes The in-depth surveys also explored people’s attitudes towards potential traffic planning conflicts between the car and sustainable travel modes (Figure 4.6). This analysis suggests a very slight increase in the level of public support for measures favouring public transport, with larger increases in support for measures favouring cycling and/or walking over car use. Baseline levels of support for all sustainable travel modes were already very high, however. For all modes a clear majority of 88% or more favoured improvements for sustainable travel modes, even if these disadvantaged car users. Figure 4.6

Traffic planning conflicts

31

Overall, as shown in Figure 4.7, the 2008 survey showed no change to the overwhelming majority of respondents who favoured making sustainable travel modes (walking, cycling and public transport) a priority in transport policy and/or planning. Figure 4.7

Expectations from transport policy and planning

32

4.4 Perceptions of public transport The research included a detailed assessment of perceptions of and attitudes towards public transport (Figure 4.8). This part of the research showed a substantial increase in levels of satisfaction with public transport (37% in 2008, up from 26% in 2004). Similarly, respondents’ evaluation of the development of public transport over the previous four years was much improved, with 31% reporting an improvement over this timeframe in 2008, compared with only 19% in 2004. Another positive development is that only 17% of respondents reported a deterioration in 2008, compared with 41% in 2004. Public expectations of future developments in public transport also showed substantial improvement, with over a third of respondents in 2008 expecting improvements over the next four years (compared to just 18% in 2004), and a large reduction in the proportion expecting service quality to decline (down from 32% to just nine percent). Figure 4.8

Perceptions of public transport

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5

POTENTIAL FOR BEHAVIOUR CHANGE

5.1 Introduction Reducing car use through promotion of sustainable travel modes was the key objective of Worcester’s Choose how you move programme. The baseline research in 2004 revealed a significant potential for change in travel behaviour among the residential population of the city; the extent to which this potential was activated over the following four years has been summarised in Chapter 3. This chapter reviews how the potential for change itself has changed since the inception of the Choose how you move programme. 5.2 Potential for reduction in car use The in-depth research examined the reasons influencing respondents’ mode choice for actual trips recorded in their travel diaries. In order to assess the potential for reducing car use, each car trip was assessed to see whether there was an objective reason for using the car (e.g. business trip, car trip within a longer trip chain) and/or whether an alternative mode would in fact have been available.9 Charts illustrating the potential for reductions in car use begin overleaf.

9

The ‘situational’ approach used in this analysis is described in Annex B.

34

Figure 5.1 shows that in 2004, 54% of car trips within Worcester had no mode alternative (e.g. lack of public transport alternative, no bicycle available, walking impossible) and/or there were constraints against using alternative modes (e.g. heavy parcels, using a car for business reasons, part of a longer trip chain). The remaining 46% of car trips within Worcester were without constraints and had at least one sustainable travel mode alternative (public transport, bicycle, or walking) available. In these instances, car use was solely for subjective reasons. Figure 5.1

Objective and subjective reasons for car trips

By 2008, the share of local car trips made for solely subjective reasons had increased to 52%, indicating an even greater potential for change than existed in 2004. This suggests that despite significant reductions in car use between 2004 and 2008, potential for further reductions was created by infrastructure improvements and/or other contextual changes. It is also possible that by 2008 Worcester residents took a more relaxed view of constraints on mode choice

35

(i.e. they were more inclined to use non-car modes under conditions that would previously have led to car use). Figure 5.2 examines these changes in more detail. In 2004, the baseline research identified an average of 1.4 alternatives for each car trip (within Worcester) and showed that one fifth of these were replaceable by public transport, around one third (34%) by bicycle and 12% by walking. In 2008, the average number of alternatives per car trip remained unchanged but the share of replaceable local car trips for which cycling provided a realistic alternative had risen to 39%, the potential for public transport had risen to 23% and for walking to 13%. This suggests that improvements to Worcester’s cycle and public transport networks, in particular, may have made these modes realistic alternatives for more local car trips, but that only part of this potential has been realised. It is also possible that non-car modes became viable alternatives for a greater number of journeys because of changes to trip chains, the times trips were made and/or people’s interpretations of potential constraints on mode choice. Figure 5.2

Potential replacement modes for car trips

36

Figure 5.3 extends this analysis to all trips covered by the behavioural research and examines travel by sustainable modes alongside car travel. This shows that while in 2004 nearly one third (30%, shown in light green) of all trips were made by car in situations where there were only subjective reasons against walking, cycling and/or public transport, this share had reduced to 29% in 2008. This suggests that as the combined mode share of walking, cycling and public transport (shown in darker green) increased from 34% to 38%, so the total potential for behaviour change (measured by the share of all trips made by car for solely subjective reasons) declined slightly. Nonetheless, in relation to the actual combined mode share of motorised private modes (66% in 2004 and 62% in 2008), this overall potential remained roughly unchanged. Figure 5.3

Potentials for sustainable travel modes (STM)

37

5.3

Potential for public transport

The 2008 in-depth surveys permit a similar analysis of the potential for public transport as was undertaken for the baseline research in 2004. In this instance, trips not made by public transport were examined to identify the objective and subjective factors affecting mode choice. Figure 5.4 shows trips not made by public transport (in red, plus the left hand white bar) and trips made by public transport (in green, plus the right hand white bar). As noted in Chapter 3, the overall public transport mode share for 2008 was seven percent, up from six percent in 2004. The findings of the 2008 indepth surveys show a small reduction in the share of trips that were not made by public transport because of physical constraints, or the lack of an adequate connection (accounting for 28% and 41% of trips respectively in 2008). Figure 5.4

Potential for public transport

38

Similarly, the share of trips for which lack of information and/or acceptance was the main barrier to public transport use fell slightly to 16% from 18%. The proportion of trips for which a negative subjective evaluation of public transport was a factor in people’s choice to travel by car did increase, but remained small, at three percent. In 2008 there was a five percent share of all trips for which people had a free choice (i.e. they were informed and expressed no negative perceptions of public transport, but still choose to travel by car). This was up from three percent in 2004. In 2008, between one third and half of trips made by public transport were free of choice (three out of seven percent of all trips), and the research indicated no change in the share of trips made by public transport in situations where the individual was either subjectively or objectively bound. The total size of the free of choice group rose from five to eight percent of all trips, indicating more future potential for behaviour change. Figure 5.5 further illustrates the extent to which the level of information about available public transport services affected trips made by motorised private modes. Figure 5.5

Information on public transport and motorised private modes (MPM)

39

In 2004, more than half (52%) of all car trips for which there was an available public transport alternative were made in the absence of information about that alternative. In 2008 that proportion had fallen to 42%, indicating that improved information had reduced this particular barrier to public transport use by a considerable margin. The 2008 research also indicates a relatively modest shift in perceptions of relative travel times by public transport and motorised private modes. Figure 5.6 shows that in 2004, people overestimated door-to-door travel times by public transport by nearly two thirds (62%) while underestimating car travel times by one fifth (20%). The net result was that people overestimated public transport travel times relative to car travel times by around twice the actual difference. Figure 5.6

Perceptions of travel time

40

By 2008, perceptions of public transport travel times had improved to 50% greater than the actual time and people were slightly more realistic about car travel times, underestimating actual values by 17%. This suggests that in 2008 people’s perceptions of relative travel times were more realistic than in 2004, and so perhaps less likely to form a barrier to public transport use. 5.4

Potential for cycling

Figure 5.7 presents an analysis of the potential for cycling in 2008 compared with 2004, using the same format as for public transport in Figure 5.4. As reported in Chapter 3, the overall mode share for cycling remained stable at three percent between 2004 and 2008 (shown in pale yellow, plus the right hand white bar towards the right of Figure 5.7). The share of non-cycling trips (excluding those made on foot) fell from 72% in 2004 to 69% in 2008 (shown in red, plus the left hand white bar). Figure 5.7

Potential for cycling

41

In 2004, 37% of trips were not made by bike because of physical constraints and/or lack of objective choice (e.g. no bicycle, trip too long to cycle). This proportion remained unchanged in 2008, although there was a very slight change in the balance between these two types of barrier to cycling. Perceptions of cycling infrastructure remained relatively unimportant, affecting only three percent of trips in 2008. However, the research indicates a notable improvement since 2004 in people’s subjective evaluations of cycling; in 2008, negative subjective evaluations affected 11% of trips compared with 15% in 2004. This suggests that cycling is now viewed much more positively as a viable travel mode for day-to-day journeys. It may be that this improved perception was not translated into modal shift towards cycling because those trips where this could have happened were transferred to walking, which did show an increase between 2004 and 2008. 5.5

Potential for walking

A similar analysis is possible for walking, as shown in Figure 5.8. Figure 5.8

Potential for walking

42

This suggests a slight reduction in the physical constraints preventing travel on foot (e.g. bulky items to carry, a need to take passengers, part of a trip chain affected by other constraints), affecting 39% of trips in 2008 compared with 40% in 2004. There was also a small change in the share of trips for which walking did not provide a viable alternative (e.g. trip distance too great), which fell from 21% in 2004 to 20% in 2008. The importance of people’s perceptions of infrastructure increased slightly, although in 2008 such perceptions still affected only one percent of all trips. However, as was the case for cycling, the 2008 research suggests a marked improvement in people’s subjective evaluation of walking as a day-to-day travel mode, with a negative subjective evaluation affecting only five percent of trips in 2008 compared with eight percent in 2004. The share of all trips made on foot in the ‘subjectively bound’ category (i.e. because people have a strong personal preference for walking) also increased from four percent in 2004 to six percent in 2008. This suggests an improved walking culture in Worcester, providing greater support in 2008 than in 2004 for this travel mode. 5.6

Conclusion

The 2008 surveys indicate some substantial changes in the potential for sustainable travel modes to replace car travel in Worcester over the period of the Choose how you move programme. Figure 5.9, overleaf, summarises the possible changes in travel behaviour in 2004 and in 2008.

43

Figure 5.9

Possible changes in travel behaviour

In 2004, nearly a third (30%) of all trips were undertaken by motorised private modes (MPM) for subjective reasons (i.e. lack of awareness and negative perceptions of alternative modes). In principle, these trips are replaceable by sustainable travel modes if the subjective barriers could be removed. The remainder of trips made by car (amounting to 36% of all trips) could not be made by sustainable travel modes because of physical constraints or objective reasons related to the lack of a viable alternative. Of the 34% of all trips made by sustainable modes, fewer than half (15% of all trips) were not made by car because of physical constraints (e.g. if the reason for the trip was to go for a walk) or objective reasons (e.g. car not available). The remainder (19% of all trips) were made by sustainable modes for subjective reasons and are, in principle, replaceable by car travel. By 2008, the share of all trips undertaken by motorised private modes for purely subjective reasons had fallen slightly to 29%. This suggests that, although still substantial, a little of the potential for reducing car trips through information and motivation campaigns (to address the subjective barriers to sustainable travel) had been realised since 2004. At the same time, the share of trips made by 44

sustainable modes for subjective reasons had increased to 18% and the share made by sustainable modes because of constraints had risen to 20%. While in 2004 the theoretical potential for use of sustainable travel modes extended to 64% of all trips, in 2008 this reached 67%. This suggests that although some of the potential for change has been realised during the Choose how you move programme, further opportunities have been created for sustainable travel to replace car trips in Worcester, perhaps through improved infrastructure, land-use planning measures and/or changes in the way people interpret their physical context (and hence what they view as a constraint on their travel mode choice).

45

6

EVALUATION OF CHOOSE HOW YOU MOVE ITM PROGRAMME

6.1

Introduction

Individualised Travel Marketing (ITM) was a key component of Worcester’s Choose how you move programme. Between 2005 and 2007, Sustrans and Socialdata delivered an ITM programme in which a total of 23,504 households in the city were offered personalised travel information and support in a series of five separate campaigns carried out in three stages. The target area for the ITM programme included most of the Worcester urban area except the city centre and an area to the north covering Arboretum, Claines and St Stephen wards. This chapter presents an analysis of travel behaviour change that may be attributed to the ITM programme as a whole, and to each of the three main stages in which it was carried out. This involves a comparison of behavioural data collected during the baseline and final surveys from the ITM target population(s) and from households in the area where ITM was not carried out (as control). The control group is designed to provide a measure of the changes in travel behaviour due to non-ITM local transport measures and other background influences affecting travel patterns across the city (e.g. fuel price fluctuations). By comparing changes among the ITM target population with the background effects observed among the control group, it is possible to draw conclusions about the behaviour change that may be attributed to the ITM programme.10 The basic measure used for this analysis was mode choice.11 The indicator selected for the evaluation was trips per person by main mode.12 In the tables that follow, the following nomenclature applies: •

‘Total’ refers to the Worcester average, as reported in Chapter 3.

•

‘ITM target population’ refers to the gross target population approached for the Choose how you move ITM programme (a total of 23,504 households). This includes non-responding and non-participating households, but does not include households resident in the area covered by ITM that did not form part of the gross target population. In the stage-by-stage analysis presented

10

A key limitation of this analysis is the assumption that the background changes in the control area - and in particular the effects of non-ITM transport measures - were typical of those across the rest of the city (i.e. the areas targeted by ITM).

11

As in Chapter 3, a trip is defined as one-way movement generated by an out-of-home activity, plus the return leg of the journey. 12

The method for determining the main mode of a trip is described in Annex A.

46

in section 6.3, ‘ITM target population’ refers to the target population for the stage in question only. •

‘Target area non-ITM’ refers to those households resident in the target area(s) for the ITM programme who were not approached to participate. This is a distinct group from those who were approached to participate but declined, or were non-contactable.

•

‘Control’ refers the Arboretum, Claines and St Stephen wards, where no ITM was carried out.

6.2

Changes in travel behaviour across the ITM target population

This section analyses changes in travel behaviour across the entire ITM target population between 2004 and 2008, with reference to changes among the control group, to arrive at a measure of the modal shift that may be attributed to the ITM programme as a whole. Table 6.1 shows mode choice (measured in percentage of trips) in 2004 and 2008 across the city as a whole (as presented in Chapter 3) and among the ITM target population, Target area non-ITM and Control groups. Table 6.1

Mode choice in 2004 and 2008

ITM target population

Total

Target area non-ITM

Control

2004

2008

2004

2008

2004

2008

2004

2008

Mode

%

%

%

%

%

%

%

%

Walking

25

28

23

27

29

31

26

28

Bicycle

3

3

3

3

1

1

4

5

Motorbike

0

0

0

0

0

0

0

0

Car-as-driver

45

42

46

41

44

40

44

43

Car-as-passenger

21

20

22

21

20

23

21

18

Bus

5

6

5

7

4

4

4

4

Other PT

1

1

1

1

2

1

1

1

100

100

100

100

100

100

100

99

TOTAL

47

Table 6.1 shows that in 2004 mode choice among the ITM target population was broadly similar to the Worcester average (shown in the ‘Total’ column). Only walking shows a difference of more than one percentage point. The table also enables a comparison of the changes in mode choice in the ITM target population, Control group and the city as a whole between 2004 and 2008. This indicates a more substantial mode shift among the ITM target population than in the Control group, notably a reduction in the car-as-driver share from 46% to 41%, compared with 44% to 43% among the Control group. Table 6.2 shows relative changes in mode choice between 2004 and 2008. Table 6.2

Relative change in mode choice between 2004 and 2008

Total

ITM target population

Target area non-ITM

Control

Relative change

Relative change

Relative change

Relative change

Walking

+ 12 %

+ 15 %

+ 7%

+ 7%

Bicycle

+ 19%

+ 19%

- 5%

+ 37%

n/a

n/a

n/a

n/a

Car-as-driver

- 7%

- 10%

- 8%

- 2%

Car-as-passenger

- 4%

- 5%

+ 14%

- 14%

+ 20%

+ 30%

- 2%

+ 15%

n/a

n/a

n/a

n/a

Mode

Motorcycle

Bus Other PT

Table 6.2 indicates a 10% relative reduction in car-as-driver trips among the ITM target population compared with a two percent reduction in the Control group, an eight percent relative reduction among the Target area non-ITM group and a seven percent reduction across the city as a whole. A similar pattern is observed for walking and bus travel, with a greater relative increase among the ITM target population than among other groups (in fact bus travel decreased notably among the Target area non-ITM group). However, for cycling this trend was reversed, with a relative increase of 37% among the Control group, compared with 19% among the ITM target population. Similarly the change in car-aspassenger trips was more pronounced in the Control group (in this case a

48

relative reduction of 14%) than among the ITM target population. The Target area non-ITM group showed a 14% increase in car-as-passenger trips. The surveys also provided data on activities, trip types and other personal mobility indicators, as described for the overall Choose how you move programme in Chapter 3. Table 6.3 shows trip types13 for Worcester residents grouped by (city-wide) Total, ITM target population, Target area non-ITM and Control groups. Table 6.3

Trip types by ITM group

ITM target population

Total

Target area non-ITM

Control

2004

2008

2004

2008

2004

2008

2004

2008

Trip type

%

%

%

%

%

%

%

%

Mandatory

32

33

33

33

32

31

31

33

Discretionary

40

38

39

39

40

39

41

40

Leisure

28

29

28

28

28

30

28

27

TOTAL

100

100

100

100

100

100

100

100

Table 6.3 shows that the share of each trip type made by members of the ITM target population stayed constant between 2004 and 2008. However, members of the Control group made more mandatory trips and fewer discretionary and leisure trips in 2008 than they did in 2004. Members of the Target area non-ITM group made fewer mandatory and discretionary trips and more leisure trips in 2008 than they did in 2004.

13

Mandatory trips include work and education, discretionary trips include shopping, escort and personal business, and leisure includes all other trips.

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Table 6.4 shows personal mobility figures for Worcester residents grouped by Total, ITM target population, Target area non-ITM and Control groups. Table 6.4

Personal mobility by ITM group

ITM target population

Total Per person per day

Target area non-ITM

Control

2004

2008

2004

2008

2004

2008

2004

2008

Activities

1.7

1.7

1.7

1.7

1.8

1.7

1.7

1.7

Travel time

60

59

60

58

63

61

59

58

Trips

3.0

3.0

3.0

3.0

3.0

3.0

3.0

3.0

Distance (km)

23

23

23

22

24

25

20

24

In 2008, all groups (except Target area non-ITM, for whom there was a slight increase) were performing the same number of activities and making the same number of trips each day as in 2004. Average daily travel time fell for all groups except Target area non-ITM, with the largest fall seen among the ITM target population. In 2008 there was a notable difference in average daily travel distance between the ITM target population and the Target area non-ITM and Control groups. Members of the Control group travelled, on average, 24km each day, up from 20km in 2004. Members of the Target area non-ITM group travelled 25km per day in 2008, up from 24km in 2004. Members of the ITM target population, by contrast, had reduced their average daily travel distance from 23km to 22km over this period. 6.3

Stage-by-stage analysis of the ITM programme

This section presents an analysis of the behaviour change between 2004 and 2008 associated with each stage of the Choose how you move ITM programme. Tables 6.5 and 6.6, overleaf, provide results from Stage 1, carried out in Warndon, Gorse Hill, Warndon Parish North and Warndon Parish South wards, together with parts of Rainbow Hill, in late 2005. These tables compare the travel behaviour change among the Stage 1 ITM target population with that of the Control group (described in section 6.1). For the stage-by-stage analysis, all sustainable travel modes (STM: walking, cycling and public transport) are treated together.

50

Table 6.5

Relative changes in mode choice for ITM Stage 1 (2004-2008)

ITM target population

Control

Relative change

Relative change

STM

+ 14%

+ 11%

Car-as-driver

- 11%

- 2%

Car-as-passenger

+ 3%

- 14%

Mode

As Table 6.5 shows, the relative increase in STM mode share among the Stage 1 ITM target population (14%) was greater than the increase among the Control group (11%). Similarly, the relative reduction in car-as-driver trips (11%) among the ITM target population was greater than that among the Control (two percent). Car-as-passenger trips increased by three percent among the Stage 1 ITM target population, but were reduced by 14% among the Control group. Table 6.6 shows the 2004 and 2008 mode choice data for the two groups. Table 6.6

Mode choice for ITM stage 1 (2004 and 2008) ITM target population

Control

2004

2008

2004

2008

Mode

%

%

%

%

STM

30

34

35

39

Car-as-driver

47

42

44

43

Car-as-passenger

22

23

21

18

TOTAL

100

100

100

100

These changes may be viewed alongside the findings of an interim evaluation of Stage 1 of the ITM programme (conducted a few months after its completion) which indicated a 12% reduction in car-as-driver trips (net of Control changes). It should be noted, however, that the interim evaluation used a different survey method so the results may not be entirely comparable.

51

Tables 6.7 and 6.8 provide data relating to Stage 2 of the ITM programme, carried out in Nunnery, Battenhall and parts of Warndon Parish South, Cathedral, Gorse Hill, St Peter’s Parish, Norton and Whittington wards between April and November 2006. Table 6.7

Relative changes in mode choice for ITM Stage 2 (2004-2008)

ITM target population

Control

Relative change

Relative change

STM

+ 6%

+ 11%

Car-as-driver

- 6%

- 2%

Car-as-passenger

- 5%

- 14%

Mode

The increase in STM mode share among the Stage 2 ITM target population (six percent) was less pronounced than the increase among the Control group (11%). However, the reduction in car-as-driver trips (six percent) among the ITM target population was greater than the reduction among the Control group (two percent). Car-as-passenger trips decreased by five percent among the Stage 2 ITM target population and by 14% among the Control group. Table 6.8 shows the mode choice figures on which the relative changes shown in Table 6.7 are based. Table 6.8

Mode choice for ITM stage 2 (2004 and 2008) ITM target population

Control

2004

2008

2004

2008

Mode

%

%

%

%

STM

35

38

35

39

Car-as-driver

42

40

44

43

Car-as-passenger

23

22

21

18

TOTAL

100

100

100

100

52

Again these data may be compared with the findings of the interim evaluation of Stage 2 of the ITM programme which indicated an 11% reduction in car-asdriver trips (net of control group changes) six months after its completion. Finally, Tables 6.9 and 6.10 provide results from Stage 3 of the ITM programme, carried out in Bedwardine’s, St John, St Clement and Broadheath wards between May and December 2007. Table 6.9

Relative changes in mode choice for ITM Stage 3 (2004-2008)

ITM target population

Control

Relative change

Relative change

STM

+ 31%

+ 11%

Car-as-driver

- 13%

- 2%

Car-as-passenger

- 14%

- 14%

Mode

Table 6.9 shows a relative increase of 31% in STM trips among the Stage 3 ITM target population, compared to a relative increase of 11% among the Control group. For car-as-driver trips, the ITM target population showed a relative reduction of 13%, compared to a relative reduction of two percent among the Control. Both groups showed a relative reduction of 14% in car-as-passenger trips. Table 6.10 provides mode choice data for Stage 3. Table 6.10

Mode choice for ITM stage 3 (2004 and 2008) ITM target population

Control

2004

2008

2004

2008

Mode

%

%

%

%

STM

31

40

35

39

Car-as-driver

47

41

44

43

Car-as-passenger

22

19

21

18

TOTAL

100

100

100

100

53

ANNEX A

GLOSSARY

General terminology Trip

Movement generated by an out-of-home activity plus trips back home. More than one mode can be used for one trip.

Journey

A sequence of trips starting and ending at home, to do one or more activities.

Activity

Main business carried out in one spatial setting away from the home.

Persons

All members of the surveyed households.

Mobile persons

Persons undertaking at least one trip during the sampling day.

Mode

The means of transport used for one trip; for one trip generally more than one mode can be used. If more than one mode is used for one trip, a main mode (of the trip) is determined according to the following ranking: public transport; motorised private modes (car, motorbike); nonmotorised modes (bicycle, walking).

(Trip) distance

Door-to-door distance of a trip (as reported by the respondent). The analysis of day-to-day mobility excludes trips of more than 100 km (around 2% of all trips) to avoid skewing any distance-related indicators.

(Trip) duration

Duration between the start of a trip and arrival at the destination (based on the time starting a trip and arriving at the destination, both reported by the respondent).

Commercial (trip)

Trips undertaken exclusively as professional services (e.g. as a taxi driver, freight driver, etc) are not included in the results presented here.

54

Activity terminology Work

Commuting including trips to usual place of work from home, or from work to home. Also trips to work from a place other than home or in the course of work (e.g. coming back to work from going to the shops during a lunch break).

Work-related business

Personal trips in the course of work.

Education

Trips to school including nursery school and further / higher education by full-time students, students on day-release and part-time students following vocational courses.

Escort

When the traveller has no purpose of his or her own other than to escort or accompany another person (e.g. taking a child to school).

Shopping

All trips to shops or from shops to home, even if there was no intention to buy.

Personal business Visits to services (e.g. hairdressers, launderettes, betting shops, solicitors, banks, estate agents, libraries, etc) or for medical consultations or treatment. Leisure

Visits to meet friends, relatives, or acquaintances, both at someone’s home or at a pub, restaurant, etc. Religious activities, all types of entertainment or sport, clubs, and nonvocational evening classes, political meetings, recreation, leisure walks, day trips, holidays (within the UK) etc.

55

ANNEX B

PRINCIPLES OF THE POTENTIALS ANALYSIS

Researching reasons for mobility behaviour has concerned decision-makers, planners and scientists for several decades. Surveys in the transport field deal with various aspects of people’s mobility and therefore with a type of behaviour that appears to be simple and easily explained but is, in reality, very complex and sophisticated. Serious empirical studies must adjust to the world they wish to depict and cannot expect that this world will adapt itself to their simplifying methods. For example, even ascertaining the reasons which determine mode choice is a complicated matter which requires reliable information from very diverse data fields. The personal circumstances of an individual will influence and constrain the choice of a certain mode of transport (data field: sociodemography); the possibility of using this mode of transport must exist (data field: transport systems); the persons involved must perceive this option accurately (data field: perceptions); and they must be willing to use this option (data field: attitudes); This means that the existence of an option alone is not enough if people do not perceive it or if their personal circumstances prevent its use. One implication is that a positive attitude towards a mode of transport does not lead to its use when no option is available. But the starting point for people’s behavioural decisions is always the world they perceive, irrespective of how (in)complete and/or (in)accurate this perception is. If one wishes to understand the behaviour of human beings, logic dictates that not only knowledge of the prevailing (external and personal) conditions for their decision is required, but knowledge of their perceived world(s) as well. Therefore, even the description – let alone the explanation and prediction – of mode choice becomes a task of considerable complexity. It is made even more difficult by the fact that daily, weekly, and monthly, every individual is confronted by many different decision situations with regard to their travel mode choices that involve varying combinations of the above data fields. Simplifying approaches which only deal with the ‘unit person’ are therefore of little help in the description, explanation, or prediction of mode choice. A perceptible improvement in the quality standard for mobility research is becoming more and more important, as such research is increasingly recognised as an aid to decision-making in many planning fields. This decisionmaking assistance needs to be of the highest quality in order to safeguard the investment of considerable financial resources, to contribute to environmental

56

protection, and to improve social conditions now and for future generations. Practical mobility research is much more than an abstract academic exercise. Therefore our efforts must be directed above all to a constant improvement in our methodological standards and practices. In spite of the complexity of our research topic (mobility), the application of common sense often suffices. It requires no great scientific effort to recognise, for example, that the use of an alternative mode of transport is only possible: if there is no constraint requiring the use of the present mode; if a suitable alternative mode is actually available; if the person involved is also adequately informed about this alternative; if s/he thinks the use of this alternative is possible with regard to travel time, costs, comfort (and similar usage characteristics); and if s/he has no objection in principle against this alternative so that s/he finally chooses it from the available alternatives. From this (conceptual) model of mode choice, data requirements can be formulated. These data requirements will include various types of primary and secondary data. On close examination, it is clear that even the primary data can only be obtained through a combination of different (partial) surveys. If reliable data are to be collected, different survey methods – each suitable for its respective data field – must be used. Once the data are collected and recorded, careful analyses must be employed to determine if the conceptual assumptions that have led to the development of the underlying conceptual model can be confirmed or, if necessary, need to be modified. Only then can the mathematical/statistical use of a model begin. Many models predicting transport behaviour are based on empirically measured behaviour patterns. They determine the framework conditions and then try to derive a statistical correlation between framework conditions and behaviour to infer the behaviour under framework conditions. Subjective scopes of action are not taken into account, nor are they replaced by experience-based assumptions. Such models usually just predict status-quo behaviour under new framework conditions. Transport decision makers can neither develop innovative solutions, nor understand the potential to better use existing systems, by using models of the existing system. They need to know the extent to which existing behaviour cannot be changed, which measures will (not) achieve the predicted effects, and how such measures are to be valued beyond behaviour change. Socialdata developed the ‘situational approach’ as a model to measure the real potential (and limits) for travel behaviour change. The situational approach offers not only everything that is asked for by an ‘individual behaviour model’, but also has some advantages compared to other versions of this ‘model family’ e.g.: 57

•

The approach is based on actual behaviour and achieved changes in behaviour (therefore it is possible to make a projection of the ‘total behaviour’ for a given population).

•

With this information, factors which determine behaviour are recorded (through this it is possible to estimate the impact of measures which were not subject of the initial project).

•

The recording of all objective and subjective factors (through this it is possible to get information about car trips that are constrained by lack of an alternative, for example, and also about the trips where ‘soft policies’ such as information and motivation have the potential to prompt behaviour change).

•

By testing every trip against the actual system for alternatives to the car and the objective constraints against using the alternatives (e.g. distance to/from the public transport stop, actual travel time), it becomes possible to evaluate the actual extent of information in comparison to the perception of these alternatives.

•

A summary of all factors in a single model structure makes it possible to evaluate the impacts of opposing and complementary factors.

•

A strict reference to the unit family/household. This reference enables analysis to determine the influence of individual behaviour on the behaviour of other members of the family. Also to estimate the direction and impact of possible secondary reactions (e.g. if a working car driver changes to public transport, the other members of the family may use the spare car and not use public transport any longer).

The situational approach assumes, simply, that each individual has a unique scope of behaviour as a result of her/his environment (i.e. objective situation). Each individual experiences these objective situations – the transportation infrastructure they can access, the constraints and options of the individual and their household which can be socio-demographically deduced, and social values, norms and options which are pertinent to travel behaviour – differently. This experience creates individually-different subjective situations. The subjective situations differ from objective situations due to perceptions being incomplete or distorted, consciously or unconsciously. The extent of deviation depends upon the individual and their specific experiences. Decisions are made in these subjective situations. Subjective situations therefore have a major influence on behaviour. The situational approach is not limited to individual behavioural situations, such as factors influencing an individual’s free choice. The approach also recognises the fact that individual (behavioural) decisions are made in accordance with a

58

personal, subjective logic that is frequently at odds with the researcher’s, planner’s or politician’s ‘rationality’. This does not imply that the individual does not act rationally; only that their logic is also subjective. The influences and processes of the ‘situational approach’ can be simplified as shown in Figure B1. Figure B1

The situational approach

Individual situation

Framework

Perception n Evaluation Behaviour Disposition

Individual

‘Individual situation’ includes primarily socio-demographic variables such as age, sex and occupation. ‘Framework’ is infrastructural (e.g. system measures) as well as legislative parameters (e.g. parking rules). Therefore behaviour change measures are based on ‘system measures’ (hard policies) as well as on ‘measures on the mind’ (soft policies). Simplified, five different groups of trips with different potentials for change can be derived, as illustrated in Figure B2.

59

Figure B2

Potential groups

AREA

DIMENSION(S)

POTENTIALGROUP

Constraints (CON)

Individual situation

Constraints

Framework conditions

No "Objective" choice connection operation (SYS)

Perception

Information Subjective disposition

Lack of information / acceptance (PAW)

Evaluation

Time Comfort Costs

Negative subjective evaluation (SUB)

Behavioural disposition

With "objective" and subjective choice

Free of choice (FOC)

Group CON: Trips with constraints because there is no behavioural alternative (e.g. car use for business reasons). Group SYS: Trips without behavioural alternatives because of infrastructural constraints (e.g. no adequate public transport connection available). Group PAW: Trips with objective behavioural alternatives but these options are excluded by the individual’s subjective filter (e.g. persons without sufficient information about existing public transport alternatives, or prejudice against public transport).

60

Group SUB: Trips with an objective alternative and where the individual is aware of the alternative, but this existing option is assessed negatively (e.g. negative perception of travel time). Group FOC: Trips with a real alternative and a subjective awareness of the alternative, but where the option is not currently chosen. Each of these five trip types has potential for change. Three types of measures are important: •

System measures for group SYS. This includes also restrictive measures against other modes.

•

Public awareness measures for group PAW.

•

Conventional marketing and information campaigns for groups SUB and FOC.

(Behaviour change for group CON cannot be easily achieved, so it is not considered for further actions). These five groups exist because of multiple individual situations and perceptions, which have an influence on mode choice for every single trip. In-depth research involving intensive dialogue with each household, with each member having previously completed a travel diary, led to the formulation revealed the above behaviour types. This method of in-depth research is more reliable than the common approach of asking non-users to state why they don’t choose alternatives to the car. Mode choice is determined by several of the influences identified by the research. The combinations of influences need to be considered to identify the potential for change for each of the transport measures available. A group of people/trips with a system constraint (SYS) may also have a negative perception regarding public transport (PAW), so a system solution alone will be insufficient to change mode choice.

61

ANNEX C

STATISTICAL SIGNIFICANCE OF CHANGES IN MODE CHOICE

With regard to the statistical significance of changes in mode choice, expert opinions differ on whether this test should be based on persons or trips. For that reason the following tests were implemented using both of these parameters as inputs. The basis for the tests is persons in independent travel behaviour survey samples before and after the STDT programme (i.e. in 2004 and 2008). Share of car-as-driver trips (based on persons) The following test can be performed. The null hypothesis and the alternative hypothesis, respectively, are: H0: P1 ≤ P2 H1: P1 > P2 Where P1 = car-as-driver mode share in 2004 and P2 = car-as-driver mode share in 2008. The null hypothesis postulates that the share of trips made by car-as-driver in 2008 is not lower than the share of trips made by car-as-driver in 2004. If this null hypothesis can be rejected, we can say that the change in car-as-driver mode share between 2004 and 2008 is statistically significant. The calculation is performed as a t-test for independent samples. The share of car-as-driver trips in 2004 (44.8%) and in 2008 (41.6%) and the number of cases (individuals from whom data were gathered) are the inputs (2004: n1 = 4,125; 2008: n2 = 4,072). The formula for the test is: T=

=

P1 − P2 P1 (1 − P1 ) P2 (1 − P2 ) + n1 n2 0.032 0.0001

=

= 2.9243

62

The test decision is:

1   ϕ ( y, y ) =   0

if T < z a

other

z0.001 = 2.326 (critical value for a level of significance of 99.0%). It follows based on this test that the null hypothesis (no change in the share of car-as-driver trips between 2004 and 2008) can be rejected with a probability of more then 99.0%. In other words, we would expect to obtain this result by chance in less than one in 100 tests using samples drawn from the same survey populations. Share of car-as-driver trips (based on trips) For testing on the basis of trips, the same test can be performed. The null hypothesis and the alternative hypothesis are: H0: P1 ≤ P2 H1: P1 > P2 Where P1 = car-as-driver mode share in 2004 and P2 = car-as-driver mode share in 2008. Again, the null hypothesis postulates that the share of trips made by car-asdriver in 2008 is not lower than the share of trips made by car-as-driver in 2004. If this null hypothesis can be rejected, we can say that the change in car-asdriver mode share between 2004 and 2008 is statistically significant. The calculation is done as t-test for independent samples. The share of car-asdriver trips in 2004 (44.8%) and in 2008 (41.6%) and the number of observed trips are the inputs (2004: n1 = 12,037; 2008: n2 = 11,620).

63

The formula for the test is: T=

=

P1 − P2 = P1 (1 − P1 ) P2 (1 − P2 ) + n1 n2 0.032 = 4.9673 0.00004

The test decision is:

1   ϕ ( y, y ) =   0

if T < z a

other

z0.01 = 2.326 (critical value for a level of significance of 99.0%). It follows based on this test that the null hypothesis (no change in the share of car-as-driver trips between 2004 and 2008) can be rejected with a probability of more then 99.0%. So the significance tests performed produced a significance level of more than 99.0% based on both persons and on trips (see Table C1). Table C1

Significance tests for reductions in car-as-driver mode share

Level of significance

Persons

Trips

> 99.0%

> 99.0%

Share of sustainable travel modes (based on persons) As for car-as-driver trips, tests were carried out to assess the statistical significance of changes in the mode share of sustainable travel modes (STM: bicycle, walking and public transport). In the following analyses the combined share for all three of these modes was examined.

64

The following test can be performed. The null hypothesis and the alternative hypothesis are: H0: P1 ≥ P2 H1: P1 > P2 Where P1 = STM mode share in 2004 and P2 = STM mode share in 2008. The null hypothesis postulates that the share of trips made by STM in 2004 is larger than or equal to the share in 2008. If this null hypothesis can be rejected, we can say that the change in STM mode share between 2004 and 2008 is statistically significant. The surveys in 2004 and 2008 provide two independent samples, so the calculation is performed as an independent samples t-test. The mode share for STM in 2004 (33.3%) and in 2008 (37.3%) and the number of observed persons are the inputs (2004: n1 4,125; 2008: n2 = 4,072). The test formula is: T=

=

P1 − P2 P1 (1 − P1 ) P2 (1 − P2 ) + n1 n2 − 0.04 0.0001

=

= −3.7889

The test decision is:

1   ϕ ( y, y ) =   0

if T < z a

other

65

z0.001 = 2.326 (critical value for a level of significance of 99.0%). It follows based on this test that the null hypothesis (no increase in the share of trips made by STM between 2004 and 2008) can be rejected with a probability of 99.0%. In other words, the increase in STM mode share in Worcester is highly statistically significant. Share of sustainable travel modes (based on trips) A similar test can be performed based on trips. The null hypothesis and the alternative hypothesis are: H0: P1 ≥ P2 H1: P1 > P2 Where P1 = STM mode share in 2004 and P2 = STM mode share in 2008. The null hypothesis postulates that the STM mode share in 2004 is larger than or equal to the STM mode share in 2008. If this null hypothesis can be rejected, we can say that the change in STM mode share between 2004 and 2008 is statistically significant. The calculation is again performed as a t-test for independent samples. The STM mode share in 2004 (33.3%) and in 2008 (37.3%) and the number of observed trips are the inputs (2004: n1 = 12,037; 2008: n2 = 11,620). The test formula is:

T=

=

P1 − P2 P1 (1 − P1 ) P2 (1 − P2 ) + n1 n2 − 0.04 0.00004

=

= −6.4358

66

The test-decision is:

1   ϕ ( y, y ) =   0

if T < z a

other

z0.01 = 2.326 (critical value for a level of significance of 99.0%). It follows that the null hypothesis (no increase in STM mode share between 2004 and 2008) can be rejected with a probability of over 99.0%. The significance tests performed produced a significance level of more than 99.0% based on both persons and on trips.

Table C2

Significance tests for STM mode share increase

Level of significance

Persons

Trips

> 99.0%

> 99.0%

As with the reduction in car-as-driver trips, these values indicate a highly statistically significant increase in the use of sustainable travel modes in Worcester during the period of the Choose how you move programme.

67

ANNEX D

DATA TABLES

68

SURVEY DESIGN

OBJECTIVES:

MEHODS:

COMMISSIONER:

o

Information about factual personal travel behaviour of the city’s population

o

Analysis of data and reporting results

o

Evaluation of the outcomes of Worcester’s Sustainable Travel Demonstration Town programme

o

Postal self-administered household survey with telephone support (New KONTIV®-Design, travel diary)

o

Collection of complete activity patterns for each person for one sampling day

o

Random sample of the residential population of the Worcester urban area (including people 0 years of age and older)

o

Number of respondents: 4,072 people (net)

o

Response rate: 63 %

o

Sampling days: Monday to Sunday

o

Time of survey: September – December 2008 The results are cleared of the ‘Non-Response-Effect’ and ‘Non-Reported-Trips’

o

Database: All persons, trips up to 100.0 km

o

Commercial trips are excluded

Worcestershire County Council

LIST OF TABLES

STRUCTURE DATA I

Household size

II

Cars in the household

III

Ownership of a car driver’s license

IV

Gender

V

Age

VI

Employment

BASIC TRAVEL CHARACTERISTICS 1

Basic travel characteristics (journeys, activities, trips, travel-time, distance)

2

Mobile persons by number of trips per day

3

Mobile persons by number of journeys and activities per day

4

Time budget

5

Travel time per day

6

Distance per day

7

Activities

8

Home-related trips

9

Home orientation and activities at destination

10

Trip purpose by time of the week

11

Trip starting by time of day and time of the week

12

Patterns of activities per mobile person and day

13

Patterns of activities per journey

LIST OF TABLES (continued)

MODE CHOICE 14

Mode choice

15

Mode choice by gender

16

Mode choice by age

17

Mode choice by employment

18

Mode choice by day of the week

19

Mode choice by trip purpose

20

Mode choice by time of day

21

Mode choice by participation

22

Mode choice (all modes used per trip)

DURATION AND TRIP DISTANCE 23

Duration, distance, speed per trip

24

Duration and distance per person and day

25

Modal-Split per trip, per duration and per trip distance

26

Trip distance by mode (cumulated)

27

Mode choice by distance

CAR USAGE 28

Car usage

29

Car usage by duration and trip distance

LIST OF TABLES (continued)

SPATIAL ORIENTATION 30

Spatial orientation

31

Mode choice by spatial orientation

TRAVEL BEHAVIOUR BY WARDS 32

Basic travel characteristics (per ward per day)

33

Activities per ward

34

Mode choice per ward

35

Spatial orientation per ward

STRUCTURE DATA

TABLE I

HOUSEHOLD SIZE

WORCESTER % NUMBER OF PERSONS IN THE HOUSEHOLD One person

29

Two persons

41

Three persons

13

Four persons

13

Five and more persons

4

Total

100

Average (persons per household)

2.2

Database:

1,845 households

TABLE II

CARS IN THE HOUSEHOLD

WORCESTER % No car

18

One car

47

Two cars and more

35

Total

100

Average (cars per household)

1.2

Database:

1,845 households

TABLE III

OWNERSHIP OF A CAR DRIVER’S LICENSE

WORCESTER ALL PERSONS % Yes

63

No

37

Total

100

PERSONS OVER 16 YEARS % Yes

81

No

19

Total

100

Database:

4,072 people

TABLE IV

GENDER

WORCESTER % Male

49

Female

51

Total

100

Database:

4,072 people

TABLE V

AGE CLASSES

WORCESTER % Under 6 years

8

6 to under 18 years

14

18 to under 25 years

9

25 to under 45 years

32

45 to under 65 years

23

65 years and older

14

Total

100

Database:

4,072 people

TABLE VI

EMPLOYMENT

WORCESTER % Not yet at primary school

6

Home duties

5

Retired / pensioner

16

At school, college, university

21

Looking for work

2

Employed women

25

Employed men

25

Total

100

Database:

4,072 people

BASIC TRAVEL CHARACTERISTICS

TABLE 1

BASIC TRAVEL CHARACTERISTICS – per day –

WORCESTER

Share of mobiles

ALL DAYS

MONDAY FRIDAY

85 %

89 %

1.99 1.53 3.52 6.11

1.99 1.53 3.52 6.32

1.70 1.30 3.00 5.22

1.78 1.36 3.14 5.63

59 23

63 24

MOBILE PERSONS: - Activities - Journeys - Trips - Legs

ALL PERSONS: - Activities - Journeys - Trips - Legs

- Travel time (min) - Distance (km)

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 2

MOBILE PERSONS BY NUMBER OF TRIPS PER DAY

WORCESTER %

MOBILE PERSONS WITH - one trip

2

- two trips

47

- three trips

8

- four trips

25

- five trips and more

18

Total

100

Share of mobiles (%)

Database:

85

4,072 people, 11,620 trips (up to 100 km)

TABLE 3

MOBILE PERSONS BY NUMBER OF JOURNEYS AND ACTIVITIES PER DAY

WORCESTER %

MOBILE PERSONS WITH 1 journey and - 1 activity - 2 activities - 3 activities - 4 activities and over

48 7 5 2 --62

2 journeys and - 2 activities - 3 activities - 4 activities and over

19 3 4 --26

3 journeys and - 3 activities - 4 activities and over

4 journeys and over

6 2 --8 4

Total

100

Share of mobiles

85

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 4

TIME BUDGET

WORCESTER

MOBILE PERSONS Time at home

16 h 53

Travel time

1 h 09

Time at destinations

5 h 58

Total

24 h

ALL PERSONS Time at home

17 h 55

Travel time

0 h 59

Time at destinations

5 h 06

Total

24 h

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 5

TRAVEL TIME PER DAY

WORCESTER ALL PERSONS %

Not out-of-home

15

Up to 15 min

5

16 to 30 min

16

31 to 45 min

13

46 to 60 min

15

61 to 90 min

16

91 to 120 min

10

Over 120 min

10

Total

100

Average travel time (min)

59

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 6

DISTANCE PER DAY

WORCESTER ALL PERSONS %

Not out-of-home

15

Up to 3.0 km

8

3.1 to 5.0 km

10

5.1 to 10.0 km

18

10.1 to 20.0 km

18

20.1 to 30.0 km

8

30.1 to 40.0 km

5

40.1 to 50.0 km

4

Over 50.0 km

14

Total

100

Average distance per day (km)

23

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 7

ACTIVITIES

WORCESTER SHARE %

TIME AT DESTINATION OF EACH ACTIVITY

22

6 h 40

3

1 h 57

Education

10

5 h 50

Shopping

20

1 h 10

4

0 h 44

Escort

12

0 h 14

Leisure

29

1 h 51

100

3 h 00

ACTIVITIES Work Work-related business

Personal business

Total

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 8

HOME-RELATED TRIPS

WORCESTER %

SHARE OF TRIPS - from home to out-of-home destinations

44

- between out-of-home destinations

12

- back home

44

Total

100

ACTIVITIES OF TRIPS DIRECTLY FROM HOME - Work

24

- Work-related business

1

- Education

12

- Shopping

19

- Personal business

4

- Escort

12

- Leisure

28

Total *)

100

less than 0.5 % Database:

4,072 people, 11,620 trips (up to 100 km)

70 84

Up to 5.0 km

Up to 10.0 km

Database:

100

Total

first activity of journey

23

Other

*)

77

From home

ORIGIN

100

43

Up to 3.0 km

More than 10.0 km

18

TOTAL %

Up to 1.0 km

DISTANCE FROM HOME*)

TABLE 9

100

12

88

100

95

85

60

24

EDUCATION %

100

29

76

100

91

81

48

23

SHOPPING %

4,072 people, 11,620 trips (up to 100 km)

100

17

83

100

67

50

28

11

WORK %

100

25

75

100

85

68

43

19

LEISURE %

ACTIVITIES AT DESTINATION – cumulated –

WORCESTER

HOME ORIENTATION AND ACTIVITIES AT DESTINATION

100

31

69

100

91

78

49

19

OTHER %

TABLE 10

TRIP PURPOSE BY TIME OF THE WEEK

WORCESTER TOTAL %

MONDAY FRIDAY %

SATURDAY

SUNDAY

%

%

24

30

7

6

2

3

0*)

1

Education

11

15

0*)

0*)

Shopping

20

16

37

24

Personal business

4

4

3

3

Escort

9

11

6

6

Leisure

30

21

47

60

Total

100

100

100

100

Share of all trips

100

75

14

11

TRIP PURPOSE Work Work-related business

*)

less than 0.5 %

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 11

TRIP STARTING BY TIME OF DAY AND TIME OF THE WEEK

WORCESTER TOTAL %

MONDAY FRIDAY %

SATURDAY

SUNDAY

%

%

TIME OF DAY 0*)

Before 5 a.m.

0*)

1

0*)

5 a.m. - 9 a.m.

19

24

7

4

9 a.m. - 12 a.m.

19

15

30

30

12 a.m. - 3 p.m.

19

15

27

33

3 p.m. - 7 p.m.

33

35

26

25

7 p.m. - 12 p.m.

10

11

9

8

Total

100

100

100

100

Share of all trips

100

75

14

11

*)

less than 0.5 %

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 12

PATTERNS OF ACTIVITIES PER MOBILE PERSON AND DAY

WORCESTER %

cum. %

H–W–H

17

17

H–E–H

10

27

H–S–H

9

36

H–L–H

8

44

H – W –H – L – H

4

48

H–S–H–L–H

3

51

H–L–H–L–H

2

53

H–E–H–L–H

2

55

H–W–H–S–H

1

56

H–L–H–S–H

1

57

H – ES – H – ES – H

1

58

42

100

PATTERNS OF ACTIVITIES

Other patterns of activities Explanation: H E ES L S W

= = = = = =

Home Education Escort Leisure Shopping Work

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 13

PATTERNS OF ACTIVITIES PER JOURNEY

WORCESTER %

cum. %

H–L–H

24

24

H–W–H

20

44

H–S–H

17

61

H–E–H

10

71

H – ES – H

8

79

H–P–H

3

82

H–L–L–H

1

83

H–L– S–H

1

84

H – ES – W – H

1

85

H – W – WB – W – H

1

86

H–W–L–H

1

87

Other journeys with two activities

5

92

Other journeys with more than two activities

8

100

PATTERNS OF ACTIVITIES

Explanation: H = Home E = Education ES = Escort L = Leisure P = Personal business S = Shopping W = Work WB = Work-related business Database:

4,072 people, 11,620 trips (up to 100 km)

MODE CHOICE

TABLE 14

MODE CHOICE

WORCESTER PER MOBILE PERSON PER DAY

PER PERSON PER DAY

%

Walking

0.98

0.84

28

Bicycle

0.11

0.09

3

Motorcycle

0.01

0.01

0*)

Car as driver

1.46

1.25

42

Car as passenger

0.73

0.62

20

Bus

0.18

0.16

6

Other public transport

0.04

0.04

1

Total

3.52

3.01

100

MAIN MODE

*)

less than 0.5 %

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 15

MODE CHOICE BY GENDER

WORCESTER TOTAL %

MALE %

FEMALE %

Walking

28

26

30

Bicycle

3

4

2

Motorcycle

0*)

0*)

0*)

MAIN MODE

Car as driver

42

47

37

Car as passenger

20

17

24

Bus

6

5

6

Other public transport

1

1

1

Total

100

100

100

Share of all trips

100

49

51

*)

less than 0.5 %

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 16

MODE CHOICE BY AGE

WORCESTER TOTAL %

UP TO 15 YEARS %

16-25 YEARS %

26-45 YEARS %

46-60 YEARS %

61 Y. AND OLDER %

Walking

28

39

34

23

21

29

Bicycle

3

3

3

3

4

1

Motorcycle

0*)

-

1

0*)

0*)

0*)

MAIN MODE

Car as driver

42

-

27

61

57

39

Car as passenger

20

54

23

9

12

17

Bus

6

3

9

2

5

13

Other public transport

1

1

3

2

1

1

Total

100

100

100

100

100

100

Share of all trips

100

18

10

36

19

17

*)

less than 0.5 %

Database:

4,072 people, 11,620 trips (up to 100 km)

less than 0.5 %

100

Share of all trips

*)

100

Total

20

Car as passenger

1

42

Car as driver

Other public transport

0

Motorcycle

6

3

Bicycle

Bus

28 *)

%

%

Database:

6

100

-

1

63

-

-

0*)

36

NOT YET AT PRIMARY SCHOOL

TOTAL

Walking

MAIN MODE

TABLE 17

*)

*)

*)

16

100

1

15

19

35

0

2

*)

21

100

1

6

42

5

0

4

42

%

%

28

AT SCHOOL, COLLEGE, UNIVERSITY

RETIRED / PENSIONER

4,072 people, 11,620 trips (up to 100 km)

5

100

0

7

12

40

0

3

38

%

HOME DUTIES

WORCESTER

*)

2

100

2

10

19

34

0

4

31

%

LOOKING FOR WORK

MODE CHOICE BY EMPLOYMENT

*)

25

100

1

3

15

55

0

2

24

%

EMPLOYED WOMEN

25

100

2

2

6

67

1

5

17

%

EMPLOYED MEN

TABLE 18

MODE CHOICE BY DAY OF THE WEEK

WORCESTER TOTAL %

MONDAY - SATURDAY FRIDAY % %

SUNDAY %

MAIN MODE Walking

28

28

26

28

Bicycle

3

3

3

3

Motorcycle

0*)

0*)

0*)

0*)

Car as driver

42

44

36

36

Car as passenger

20

17

29

32

Bus

6

6

5

1

Other public transport

1

2

1

0*)

Total

100

100

100

100

Share of all trips

100

75

14

11

*)

less than 0.5 %

Database:

4,072 people, 11,620 trips (up to 100 km)

less than 0.5 %

100

Share of all trips (%)

*)

100

Total

Database:

24

100

2

7

1

20

Car as passenger

63

Other public transport

42

Car as driver

1

4

0

Motorcycle

6

6

3

Bicycle

Bus

28 *)

%

%

17

WORK

TOTAL

Walking

MAIN MODE

TABLE 19

*)

*)

11

100

2

9

33

4

0

3

49

%

EDUCATION

*)

20

100

0*)

10

23

36

0

2

29

%

*)

4

100

2

10

21

45

0

2

20

%

SHOPPING PERSONAL BUSINESS

4,072 people, 11,620 trips (up to 100 km)

2

100

1

5

11

78

0

1

4

WORKRELATED BUSINESS %

WORCESTER

MODE CHOICE BY TRIP PURPOSE

*)

9

100

0*)

1

8

66

0

1

24

%

ESCORT

30

100

1

3

29

33

0*)

3

31

%

LEISURE

*)

less than 0.5 %

100

Share of all trips (%)

10

7 p.m. - 12 p.m. 100

9

33

3 p.m. - 7 p.m.

Total

31

19

12 a.m. - 3 p.m.

Database:

28

100

20

19

19

9 a.m. - 12 a.m.

0*) 21

0*)

3

100

7

100 42

0*)

11

32

18

18

21

0*)

CAR AS DRIVER %

100

13

36

16

21 31

8

27

-

MOTORCYCLE %

15

25

1

%

BICYCLE

WORCESTER

TIME OF DAY BY MODE CHOICE

4,072 people, 11,620 trips (up to 100 km)

%

%

19

5 a.m.

WALKING

TOTAL

5 a.m. - 9 a.m.

Before

STARTING TIME OF TRIP

TABLE 20

20

100

12

36

19

19

14

0*)

7

100

6

28

19

26

20

1

CAR AS PUBLIC PASSENGER TRANSPORT % %

20 7

Car as passenger

Public Transport

*)

less than 0.5 %

(...)

42

Car as driver

Share of all persons

0

Motorcycle

100

3

Bicycle

Total

28 *)

Database:

39 %

100

4

13

19

0

2 *)

*)

5%

100

4

8

19

0

56

13

%

BICYCLE

1%

47 %

100

1

0*) 100

4

79 8

11

*)

0

2

0*) 75

14

CAR AS DRIVER %

6

MOTORCYCLE %

PARTICIPATION GROUPS

WORCESTER

MODE CHOICE BY PARTICIPATION

4,072 people, 11,620 trips (up to 100 km)

%

%

62

WALKING

TOTAL

Walking

MAIN MODE

TABLE 21

*)

29 %

100

2

68

10

0

2

18

12 %

100

58

10

12

0*)

2

18

PUBLIC CAR AS PASSENGER TRANSPORT % %

TABLE 22

MODE CHOICE – all modes used per trip –

WORCESTER % ALL MODES Walking

79.9

Bicycle

3.2

Bike + Ride

0.0*)

Motorcycle

0.3

Private car as driver

41.3

Company-car as driver

0.3

Other mot. vehicle as driver

0.1

Car as passenger - family car - other car - company car

15.8 4.8 0.2

Bus

5.3

Train

0.5

Work- / School bus

0.1

Park + Ride

0.2

Taxi

0.5

Other

0.2

Total (Multiple responses) *)

152.7

less than 0.05 %

Database:

4,072 people, 11,620 trips (up to 100 km)

DURATION AND TRIP DISTANCE

DURATION, DISTANCE, SPEED PER TRIP*)

TABLE 23

WORCESTER DURATION (min)

TRIP DISTANCE (km)

SPEED (km/h)

Walking

16

1.3

4

Bicycle

16

3.2

14

Motorcycle

21

12.2

35

Car as driver

20

10.8

30

Car as passenger

18

9.7

32

Public transport

33

10.4

19

Total

20

7.7

23

MAIN MODE

*)

"Door-to-door"

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 24

DURATION AND DISTANCE PER PERSON AND DAY

WORCESTER (min)

(km)

23

1.7

Bicycle

1

0.3

Motorcycle

0*)

0.1

MAIN MODE Walking**)

Car as driver

23

13.4

Car as passenger

8

5.9

Public transport

4

1.9

59

23.3

Total *) **)

less than 0.5 minutes Including walking legs of bicycle-, motorcycle-, car- and public transporttrips

Database:

4,072 people, 11,620 trips (up to 100 km)

TABLE 25

MODAL-SPLIT PER TRIP, PER DURATION AND PER TRIP DISTANCE

WORCESTER PER TRIP

PER DURATION

PER DISTANCE

28

39

7

Bicycle

3

2

1

Motorcycle

0*)

0*)

1

MAIN MODE Walking**)

Car as driver

42

39

58

Car as passenger

20

13

25

7

7

9

100

100

100

Non-motorised modes

31

41

8

Motorised private modes

62

52

83

7

7

9

Total

100

100

100

Occupancy***)

1.5

1.3

1.4

Public transport Total

Public transport

*)

less than 0.5 % Including walking legs of bicycle-, motorcycle-, car- and public transport-trips ***) In relation to all cars (private and company cars) **)

Database:

4,072 people, 11,620 trips (up to 100 km)

100

100

98

Up to 50.0 km

7.7

Average distance (km)

less than 0.5 %

100

Share of all trips

*)

100

Total

Database:

1.3

28

10.6

42

0*) 12.2

100

95

71

52

22

5

100

96

60

48

12

0*)

CAR AS DRIVER %

4,072 people, 11,620 trips (up to 100 km)

3.8

3

100

97

100

83

Up to 10.0 km

100

86

99

68

Up to 5.0 km

Up to 3.0 km

59

18

Up to 1.0 km 91

%

43

%

%

MOTORCYCLE %

WORCESTER

TRIP DISTANCE BY MODE – cumulated –

BICYCLE

17

WALKING

TOTAL

51

TRIP DISTANCE

TABLE 26

9.7

20

100

98

82

61

25

7

10.7

7

100

92

81

59

19

0*)

CAR AS PUBLIC PASSENGER TRANSPORT % %

*)

less than 0.5 %

100

Share of all trips

Database:

7

Public transport 100

20

Car as passenger

Total

42

Car as driver

25

26

100

10

29

48

1

3

9

3.1 - 5.0 KM %

4,072 people, 11,620 trips (up to 100 km)

18

100

5

0*) 100

16

29

8

11

0*)

0*)

Motorcycle

0*)

5

3

3

Bicycle

1.1 - 3.0 KM %

45

UP TO 1.0 KM %

WORCESTER

MODE CHOICE BY DISTANCE

78

28

%

TOTAL

Walking

MAIN MODE

TABLE 27

14

100

10

31

54

0*)

3

2

5.1 - 10.0 KM %

17

100

7

21

70

1

1

0*)

OVER 10.0 KM %

CAR USAGE

TABLE 28

CAR USAGE

WORCESTER PER PRIVATE CAR / DAY

"MOBILE" CARS

Usage (%)

71

(100)

Trips

2.2

3.1

Duration (min)

41*)

57*)

Distance (km)

23*)

33*)

Occupancy per trip - All days - Workdays

1.5 1.3

Drivers per car and day

1.0

Number of journeys per day

1.31

Parking per day (out-of-home destinations)

1.81

*)

Pure car-trip-duration without walking stages

Database: 4,072 people, 11,620 trips (up to 100 km)

TABLE 29

PRIVATE CAR USAGE BY DURATION AND TRIP DISTANCE*)

WORCESTER "MOBILE" CARS Duration

0 h 57

Parking time at home

17 h 24

Parking time at destination

5 h 39

Total

24 h

ALL PRIVATE CAR TRIPS – cumulated – SHARE %

DURATION (min)

TRIP DISTANCE (km)

Trips up to 1 km

5

7

1

Trips up to 3 km

22

9

2

Trips up to 5 km

52

12

3

Trips up to 10 km

71

13

4

Trips up to 50 km

96

19

9

100

20

11

Total *)

"Door-to-door" Database: 4,072 people, 11,620 trips (up to 100 km)

SPATIAL ORIENTATION

TABLE 30

SPATIAL ORIENTATION

WORCESTER %

SPATIAL ORIENTATION Trips entirely within Worcester*)

76

Trips to / from Worcester

21

Trips outside Worcester

3

Total *)

100

Urban area of Worcester

Database: 4,072 people, 11,620 trips (up to 100 km)

20 7

Car as passenger

Public transport

*)

less than 0.5 % Database:

7.7

42

Car as driver

Average trip-distance (km)

0

Motorcycle

100

3

Bicycle

Total

28 *)

%

*)

3.2

100

7

20

34

0

4

35

TRIPS ENTIRELY WITHIN WORCESTER %

23.8

100

6

24

67

1

1

1

TRIPS TO/FROM WORCESTER %

SPATIAL DISTRIBUTION

WORCESTER

MODE CHOICE BY SPATIAL ORIENTATION

4,072 people, 11,620 trips (up to 100 km)

TOTAL

Walking

MAIN MODE

TABLE 31

14.0

100

3

24

52

0*)

1

20

TRIPS OUTSIDE WORCESTER %

TRAVEL BEHAVIOUR BY WARDS

WORCESTER

1.7 1.3 3.0

59 23

- Travel time (min) - Distance (km)

2.0 1.5 3.5

85 %

Database:

48 17

1.3 1.2 2.5

1.5 1.5 3.0

82 %

59 25

1.6 1.3 2.9

1.9 1.6 3.5

82 %

61 20

1.8 1.4 3.2

2.1 1.6 3.7

85 %

65 30

1.9 1.4 3.3

2.1 1.6 3.7

89 %

4,072 people, 11,620 trips (up to 100 km)

73 32

2.1 1.6 3.7

2.4 1.8 4.2

88 %

Claines

49 18

1.3 1.0 2.3

1.7 1.4 3.1

77 %

Gorse Hill

BASIC TRAVEL CHARACTERISTICS – per ward per day –

Total Arboretum Battenhall Bedwardin Cathedral (all wards) e

- Activities - Journeys - Trips

ALL PERSONS:

- Activities - Journeys - Trips

MOBILE PERSONS:

Share of mobiles

TABLE 32 – Part 1 –

58 24

1.7 1.3 3.0

1.9 1.5 3.4

88 %

Nunnery

69 23

1.9 1.4 3.3

2.4 1.7 4.1

81 %

Rainbow Hill

TABLE 32 – Part 2 –

59 23

- Travel time (min) - Distance (km)

61 20

1.7 1.5 3.2

2.1 1.7 3.8

85 %

Database:

1.7 1.3 3.0

2.0 1.5 3.5

87 %

- Activities - Journeys - Trips

ALL PERSONS:

- Activities - Journeys - Trips

MOBILE PERSONS:

Share of mobiles

58 16

1.6 1.2 2.8

2.0 1.6 3.6

80 %

St Johns

66 26

1.8 1.4 3.2

2.1 1.6 3.7

87 %

St Peters

58 23

2.0 1.5 3.5

2.3 1.6 3.9

90 %

St Stephen

50 16

1.6 1.3 2.9

1.9 1.4 3.3

87 %

Warndon

4,072 people, 11,620 trips (up to 100 km)

Total St (all wards) Clements

WORCESTER

BASIC TRAVEL CHARACTERISTICS – per ward per day – (continued)

63 37

1.9 1.3 3.2

2.1 1.5 3.6

89 %

Warndon Parish North

65 32

1.8 1.4 3.2

2.1 1.6 3.7

86 %

Warndon Parish South

8 28

12 29

Escort

Leisure

*)

less than 0.5 %

Total

Database:

100

3

4

100

18

20

13

13 28

4 36

11 30

100

4,072 people, 11,620 trips (up to 100 km)

100

31

100

2

3

4

7

100

20

21

17

19

8

10

12

9

9

18 3 6 38

22 5 13 28

20 4

100

23

8

100

10

8 15

100

1

3 3

5

1

4

4

6

27

24

25

%

Rainbow Hill %

24

%

%

Nunnery

21

Gorse Hill

Claines

22

19

26

Shopping

3

22

10

Personal business

WORCESTER

ACTIVITIES PER WARD

Total Arboretum Battenhall Bedwardin Cathedral e (all wards) % % % % %

Education

Work-related business

Work

ACTIVITIES

TABLE 33 – Part 1 –

TABLE 33 – Part 2 –

30

13

6

19

9

1

22

%

100

Database:

100

29

Leisure

Total

12

Escort

4

20

Shopping

Personal business

10

3

22

Education

Work-related business

Work

ACTIVITIES

%

100

29

11

4

22

12

1

21

%

St Johns

100

33

15

4

16

10

1

21

%

St Peters

100

25

15

5

22

10

3

20

%

St Stephen

100

18

17

3

21

15

2

24

%

Warndon

4,072 people, 11,620 trips (up to 100 km)

St Total (all wards) Clements

WORCESTER

ACTIVITIES PER WARD (continued)

100

33

15

4

18

10

2

18

Warndon Parish North %

100

27

15

3

19

11

2

23

Warndon Parish South %

7

Public transport

*)

less than 0.5 %

100

20

Car as passenger

Database:

100

5

16

33

0*)

0*)

Motorcycle 42

6

3

Bicycle

Car as driver

40

28

Total

WORCESTER

100

5

18

43

0*)

6

28

100

5

13

30

0*)

2

50

100

6

19

50

0*)

3

22

100

13

22

40

0*)

3

22

%

%

4,072 people, 11,620 trips (up to 100 km)

100

6

24

43

1

2

24

Gorse Hill

Claines

MODE CHOICE PER WARD

Total Arboretum Battenhall Bedwardin Cathedral (all wards) e % % % % %

Walking

MAIN MODE

TABLE 34 – Part 1 –

100

6

24

40

0*)

3

27

%

Nunnery

100

9

22

38

2

1

28

Rainbow Hill %

TABLE 34 – Part 2 –

*)

less than 0.5 % Database:

100

7

7

Public transport 100

19

20

Car as passenger

Total

47

0*)

Motorcycle 42

0*)

3

Bicycle

Car as driver

2

28

25

%

Walking

MAIN MODE

%

100

9

19

34

0*)

4

34

%

St Johns

100

3

23

47

1

3

23

%

St Peters

100

7

19

43

0*)

5

26

%

St Stephen

100

13

24

38

0*)

1

24

%

Warndon

4,072 people, 11,620 trips (up to 100 km)

Total St (all wards) Clements

WORCESTER

MODE CHOICE PER WARD (continued)

100

6

28

54

0*)

1

11

Warndon Parish North %

100

4

25

46

0*)

2

23

Warndon Parish South %

15 42 21 3

Trips to/from city centre

Other trips within Worcester

Trips to/from Worcester

Trips outside Worcester 100

19

Total

WORCESTER

Database:

100

2

16

43

16 3

28 3

20 4

100

4

25

32

18 7

19 1

14 3

100

100

49

39 58

100

11

18 13

13

15

% 23 12

26

4,072 people, 11,620 trips (up to 100 km)

100

100

30

35

49

100

28

13

12

23

23

21

15

16

%

%

Rainbow Hill %

Nunnery

Gorse Hill

Claines

SPATIAL ORIENTATION PER WARD

Total Arboretum Battenhall Bedwardin Cathedral (all wards) e % % % % %

Trips within ward

TABLE 35 – Part 1 –

TABLE 35 – Part 2 –

1

3

Trips outside Worcester 100

Database:

100

21

21

Trips to/from Worcester

Total

43

42

Other trips within Worcester

17

15

Trips to/from city centre

18

19

%

Trips within ward

%

3 2

2

100

1

19 23

16

100

12

47

46

40

100

46

11

15

14

100

15

26

20

14

28

%

Warndon

%

St Stephen

%

St Peters

%

St Johns

4,072 people, 11,620 trips (up to 100 km)

Total St (all wards) Clements

WORCESTER

8 51 24 3

13 39 33 8

100

14 7

100

Warndon Parish South %

Warndon Parish North %

SPATIAL ORIENTATION PER WARD (continued)