Quasi-longitudinal analysis of links between built environment, travel attitudes and travel behavior: A case of Greeks relocating from US to Greece

(1)

1

Quasi-longitudinal analysis of links between built

1

environment, travel attitudes and travel behavior: a case of

2

Greeks relocating from US to Greece

3 4 5 6 Dimitris Milakis 7

Marie Curie Research Fellow 8

Transport and Logistics Group, 9

Delft University of Technology 10

Institute of Urban and Regional Development, 11

University of California, Berkeley 12 Tel. +31 15 27 83480 13 E-mail: d.milakis@tudelft.nl 14 15 16 Dimitrios Efthymiou* 17

Research Associate, Laboratory of Transportation Engineering 18

School of Rural and Surveying Engineering 19

National Technical University of Athens 20

9, Iroon Polytechniou St., Zografou Campus, 15780 21 Tel: 0030-210-7721675; Fax:0030-210-7722629 22 E-mail: defthym@mail.ntua.gr 23 24 25 Constantinos Antoniou 26

Associate Professor, Laboratory of Transportation Engineering 27

School of Rural and Surveying Engineering 28

National Technical University of Athens 29

9, Iroon Polytechniou St., Zografou Campus, 15780 30 Tel: 0030-210-7722783; Fax:0030-210-7722629 31 E-mail: antoniou@central.ntua.gr 32 33 34 35

5000 Words + 3 Figures + 7 Tables = 7500 36

37

Submitted for presentation to the 94th_{Annual Meeting of the Transportation Research Board} 38

and publication in Transportation Research Record: journal of the Transportation Research 39 Board 40 41 *Corresponding author 42

(2)

ABSTRACT

1 2

Recent quasi-longitudinal studies have offered evidence on the causality of the 3

relationship between built environment and travel behavior. However, these studies 4

have focused on residential moves within the same region, thus limiting the extent of 5

movers’ exposure to different built environments and possibly underestimating the 6

built environment effects on travel behavior in comparison to self-selection effects. In 7

this paper, we explore the relationships between built environment, travel attitudes 8

and travel behavior of people that have moved between totally different urban and 9

transportation contexts, namely US and Greece. A quasi-longitudinal design has been 10

developed, involving 31 Greeks who have relocated back from the US to Greece. We 11

have collected detailed information about their perceived neighborhood 12

characteristics, neighborhood preferences and travel attitudes after relocation. 13

Our findings add evidence to the existing literature identifying a causal 14

relationship between the built environment and car use. For instance, lack of adequate 15

public transportation network and cycle facilities, and worse access to neighborhood 16

amenities in the Greek context were found to be associated with more driving after 17

relocation. More difficult access to regional shopping centers was associated with 18

lower levels of car use. Lack of safe bike conditions and easy access to public 19

transportation were the most important determinants of changes in bicycle use and 20

walking respectively. The results highlight the importance of a holistic approach (in 21

terms of sustainable land use policies and development of infrastructure for 22

alternative modes of transport) when it comes to enhancing accessibility in a city and 23

consequently reducing car use and increasing walking and bicycle use. 24

25

Keywords: Built environment, Travel behavior, Self-selection, Greece, USA

26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

(3)

3

INTRODUCTION

1 2

Although numerous studies have shown that urban form characteristics affect travel 3

behavior, self-selection, namely the causality in this relationship (1, 2, 3) remains 4

unresolved. Thus far, several approaches have been applied to address self-selection 5

with mixed results. 6

According to Cao et al. (2) longitudinal or quasi-longitudinal designs can 7

improve causality inferences, controlling for attitudes and any other variables that do 8

not change over time. However, quasi-longitudinal studies have restricted their focus 9

on residential relocation within the same region (e.g. 4, 5, 6). Hence, the extent of 10

movers’ exposure to different built environments is possibly quite limited and 11

consequently the ‘treatment’ dose may be quite low. This in turn may lead to an 12

underestimation of the built environment effects on travel behavior in comparison to 13

self-selection effects. 14

The objective of this research is to investigate the relationships between the 15

built environment, travel behavior and travel attitudes of people that have moved 16

between totally different urban and transportation contexts, namely US and Greece. 17

The rest of the paper is structured as follows: the literature review is presented after 18

the introduction, and a description of data collection and modeling methodologies 19

follows. Finally, results of the analysis and conclusions are subsequently presented. 20 21 22 LITERATURE REVIEW 23 24

Several approaches have been used to address the attitude-induced self-selection bias 25

in the relationship between the built environment and travel behavior (1, 2). Here, we 26

focus on longitudinal research designs. Longitudinal designs that control for attitudes 27

have been suggested as a powerful approach to explore causal relationships between 28

the built environment and travel behavior for two main reasons: first, they account for 29

the confounding influence of self-selection and second they fulfill the time-30

precedence criterion for causal inference. Most frequently, longitudinal designs are 31

applied in the context of residential moves, where travel related attitudinal data are 32

collected before and after the move. Thus far, longitudinal studies have restricted their 33

focus on residential relocation within the same region limiting the extent of movers’ 34

exposure to different built environments. Consequently the ‘treatment’ dose that the 35

movers receive may be quite low, thus limiting the size of ‘treatment’ effects. 36

Krizek (4) focused on households who relocated within Central Puget 37

Sound region. He explored the effects of neighborhood accessibility changes on travel 38

behavior after controlling for socio-demographic characteristics, regional accessibility 39

and workplace accessibility. Approximately 20% of the households in his sample 40

relocated close to their initial location, and over 50% moved to a neighborhood with 41

similar physical characteristics to the previous one. He employed linear regression 42

models and found that households moving to neighborhoods with higher accessibility 43

tend to reduce vehicle use in terms of vehicle-kilometers traveled (VKT). Handy et al. 44

(4)

(5, 7) focused on recent movers in eight neighborhoods in Northern California. The

1

selected neighborhoods varied systematically according to the neighborhood type, 2

size of the metropolitan area and region of state. Both studies employed a quasi-3

longitudinal design, asking the movers to respond about potential changes in driving, 4

walking and cycling behavior and about their perceived characteristics of their current 5

and previous neighborhood. Results from ordered probit models showed that change 6

in accessibility was the most influential factor for driving and walking after 7

controlling for attitudes and socio-demographic characteristics. Built environment 8

variables were also significant in the model for cycling, but the pro-bike/walk 9

attitudinal factor was the most influential factor in this model. Moreover, Cao et al., 10

(8) using the same dataset showed that built environment characteristics like

11

perceived outdoor spaciousness (e.g., large yards and off-street parking) influence 12

auto-ownership after accounting for attitudes. 13

In the European context, Aditjandra et al. (6) employed a quasi-14

longitudinal research design, similar to Handy et al. (5), to examine the relationships 15

between the built environment, neighborhood preferences, travel attitudes and travel 16

behavior in the UK. Their sample comprised 219 movers who had relocated to ten 17

neighborhoods at Tyne and Wear metropolitan area in the North East of England 18

within eight years before the survey. Their structural equation model (SEM) showed 19

that neighborhood characteristics (safety factors and shopping accessibility) do affect 20

driving behavior after controlling for self-selection. They also concluded that the size 21

of the neighborhood characteristics effect on driving behavior is consistent with the 22

respective effect identified by Cao et al. (8) in the US context. Schneiner and Holz-23

Rau (9) focused their research on 791 movers who had relocated to ten study areas in 24

the Cologne region within 14 years before the survey. The study areas represented 25

five neighborhood types ranging from central high-density to typical suburban 26

neighborhoods. The change of built environment type (from suburban to urban/inner 27

city and vice versa) was captured by an ordinal variable. The SEMs showed that built 28

environment changes were associated with significant changes in travel mode use. 29

Specifically a residential move to a more suburban environment was found to induce 30

increases in car use and decreases in public transport use, bicycle use and walking. 31

Finally, Meurs and Haaijer (10) explored whether changes in spatial and 32

personal characteristics for movers in the Netherlands were associated with travel 33

behavior changes in terms of number of car, bicycle, walking, public transport and 34

total trips. They used data for 189 movers from the Dutch Time Use Study at two time 35

points: 1990 and 1999. The regression analyses results showed that spatial 36

characteristics do influence travel behavior (e.g. moving to a pedestrian priority 37

neighborhood was associated with less car trips). Changes in employment or car 38

ownership were found to have major impacts on the number of car trips as well. 39

40 41 42

(5)

5

CASE STUDY SETUP AND METHODOLOGY

1 2

Data collection

3 4

In order to collect the -required for the analysis- data, an electronic questionnaire was 5

designed in Google Forms and disseminated on-line. The questionnaire has been 6

structured based on the works of Handy et al. (5) and Aditjandra et al. (6), but has 7

been adapted to the needs of this research. The purpose of developing a questionnaire 8

based on the one used in (5, 6) was dual. Using a successful, already applied 9

questionnaire reduces the risks of question misformulation, which leads respondents 10

to misunderstanding and contaminate the data, but also allows for comparison of the 11

results with other studies. 12

Handy et al. (5) employed a quasi-longitudinal design, to ask people that 13

had relocated in eight neighborhoods in Northern California within the previous year 14

of the survey to rate a set of twenty-seven variables about characteristics of their 15

current and previous neighborhoods (i.e. accessibility, physical activity options, 16

safety, socializing, outdoor spaciousness and attractiveness). Moreover, they asked 17

them to rate statements that describe their travel attitudes, and preferences for 18

neighborhood characteristics. Both factors were assumed to remain constant before 19

and after relocation. Changes in travel behavior were captured employing a 5-point 20

scale (from ‘a lot less now’ to ‘a lot more now’). Similarly, Aditjandra et al. (11) 21

asked people that had relocated in ten neighborhoods in North East of England within 22

eight years before the survey to rate how true twenty-seven statements about their 23

neighborhood are, and how important they perceive twenty-eight statements about 24

their travel attitudes. 25

Due to the large number of questions in our survey, we decided to use 26

some of them in reverse order. According to Weijeters and Baumgartner (12) reverse 27

order questions help to correct the acquiescence and agreement biases, but also 28

disrupt non-substantive response behavior. 29

Our questionnaire survey is structured in four parts. The first part 30

comprised 7–point ordinal items about changes in car use, bicycle use and walking 31

before and after the move between the US and Greece (from ‘a lot less now’ to ‘a lot 32

more now’) and additional items about travel mode availability (both in the US and 33

Greece) and relocation year. In the second part of the questionnaire, we asked people 34

to rate on a seven-point scale how true (from 1: not at all true to 7: entirely true) 24 35

statements are about accessibility and neighborhood characteristics in their US and 36

Greek neighborhoods. We also asked them how significant each of those 37

characteristics is with respect to the choice of their current residence. These items 38

were grouped into the following six categories: 1) car accessibility, 2) accessibility of 39

alternative means of transport, 3) accessibility of shops/facilities, 4) safety, 5) 40

neighborhood physical characteristics, and 6) socializing (see Table 2). In the third 41

part (twenty items) the respondents were asked to respond on a 7-point Likert scale 42

(from 1: strongly disagree to 7: strongly agree) about their travel attitudes (e.g., I like 43

driving, I prefer to take public transport than drive whenever possible) (see Table 4). 44

(6)

The last part included nine items about socio-demographic characteristics of the 1

respondents before and after relocation. 2

The questionnaire was disseminated through various channels, such as e-3

mails and social media and was filled 31 times. Table 1 shows the summary statistics 4

of the respondents. A comparison with the target population (Greeks who lived in the 5

US and returned back to Greece) is relatively impossible, due to the difficulty in 6

gathering demographic information about it, while a comparison with the real 7

population of Greece would be misleading. However, Table 1 shows that the majority 8

of the respondents are between 36 and 45 years old, slightly more males, slightly 9

more singles, very highly educated, primarily of income higher than 25,000 euros per 10

year and employed full-time. 11

12

TABLE 1. Key Demographic Characteristics of the Sample

13 14 Survey Age 18-25 3.23% 26-35 32.26% 36-45 51.61% 46-55 6.45% 56-65 3.23% > 66 3.23% Gender Male 54.84% Female 45.16% Martial Single 48.39% Married 45.16% Divorced 3.23% Widowed 3.23% Household size 1 29.03% 2 19.35% 3 22.58% 4 9.68% 5 or more 19.35%

Employment status Homemaker 3.23%

Retired 3.23%

Student 0%

Working full-time 90.32% Working part-time 3.23%

Education level completed Bachelor 9.68%

Masters 51.61%

Doctorate 38.71%

Income (in thousands euros per year) 10-15 6.67%

15-25 16.67% 25-50 33.33% 50-75 20.00% 75-100 10.00% 100-200 6.67% >200 6.67% 15 16

(7)

7

Methodology

1 2

Our methodology consisted of three sequential steps. First, we explored the 3

differences between US and Greek accessibility and built environment contexts, 4

aiming to verify that the ‘treatment’ that the respondents received during their 5

relocation from the US to Greece was indeed significant. Therefore, statistical testing 6

and graph-theoretic analysis have been applied for the comparison of distributions of 7

responses about perceived accessibility and perceived neighborhood characteristics in 8

the US and Greece. The Mann-Whitney-Wilcoxon test has been used for this purpose. 9

This non-parametric test examines the null hypothesis that two samples come from 10

the same population (that the true location shift between the means is equal to 0) (13). 11

The Mann-Whitney-Wilcoxon test is preferable than the t-test when comparing non-12

normally distributed groups of observations. 13

Second, we analyzed neighborhood preferences and travel attitudes of the 14

respondents aiming to identify latent constructs, since many of the questionnaire items 15

are likely to measure similar dimensions of those two factors. Factor analysis has 16

been performed at the Likert-scale responses (14, 15) on both neighborhood 17

preferences (see Table 3) and travel attitudes (see Table 4) group of items. This 18

method is used to reduce the number of variables in a dataset, by identifying common 19

patterns between them and revealing latent factors. Factor analysis is based on the 20

Spearman statistic test, which is a non-parametric test used for hierarchical cluster 21

analysis (16, 17). Variable clustering is used to assess collinearity and redundancy of 22

the variables, in order to separate them into clusters and thus treat them as single 23

variables, thus reducing their number. 24

Finally, in the third step we modeled travel behavior changes using 25

perceived accessibility and built environment, neighborhood preferences and travel 26

attitudes as explanatory variables. Three linear regression models have been 27

developed to model changes in car, walking and bicycle use before and after 28

relocation from US to Greece. Due to the large number of available explanatory 29

variables, variable reduction techniques have been applied. We first, computed the 30

difference (distance) between the ratings of the US and GR statements. Then, we used 31

the Spearman statistic to measure which variables are statistically dependent with 32

each other. Only one of the variables that belong in the same branch of the resulting 33

tree diagram of correlated variables has been used in the models (see Figure 3). Latent 34

variables of neighborhood preferences and travel attitudes, identified in the second 35

step of our methodology, were also introduced in our models to explain travel 36 behavior changes. 37 38 39 40 41 42 43 44 45

(8)

RESULTS

1 2

Differences between US and Greek accessibility and built environment contexts

3 4

Overall, the results demonstrated that accessibility and built environment contexts, in 5

which participants of this survey have lived in US and Greece, differ significantly. 6

Sixteen out of 24 accessibility and built environment variables showed a significant 7

difference (p < 0.1) (see Table 2). 8

Specifically, the majority of the respondents relocated in areas away from 9

the city center in Greece, but with lower parking availability both in their 10

neighborhood and at usual destinations outside their neighborhood (see Figures 1(a) 11

and 1(b)). Moreover, accessibility for the alternative means of transport perceived to 12

be significantly lower in the Greek compared to the US context, both in terms of 13

walkability and of the level of development of cycle facilities in the respondent’s 14

neighborhood (see Figures 1(c) and (d)). Perceived accessibility to neighborhood’s 15

amenities and facilities was also significantly lower in the Greek compared to the US 16

context. Greek relocation areas were also not so green (in terms of parks and open 17

spaces) as the neighborhoods in the US that the respondents used to live (see Figures 18

1(e) and 1(f)). Additionally, respondents stated that the conditions for cycling and 19

walking in Greece are not so safe as they were in the US (see Figures 2(a) and 2(b)), 20

which may also be connected with their statement that the residential green space and 21

architectural environment including street furniture were better in the US than in their 22

neighborhood in Greece (see Figures 2(c) and 2(d)). 23

Overall, the results of the Wilcoxon tests indicate that the neighborhoods, 24

where respondents relocated in Greece, have: 1) more difficult access to central 25

arteries, 2) lower parking availability at usual destinations outside the neighborhood, 26

3) higher levels of congestion in major urban arteries, 4) longer travel time to the city 27

center and 5) higher cost of car use. On the other hand, the US neighborhoods, where 28

respondents used to live before moving back to Greece, outperform their Greek 29

counterparts in: 1) pavement facilities, 2) cycle facilities within the neighborhood and 30

towards adjacent areas, 3) parks and open spaces, 4) low levels of traffic on the 31

streets, 5) safe walking, 6) biking and 7) driving conditions, 8) adequate residential 32

green space and 9) nice architectural design, civic buildings and/or street furniture 33

(see Table 2). We cannot conclude about the remaining accessibility and built 34

environment characteristics, because results of the Wilcoxon test were not statistically 35

significant (the null hypothesis that both distributions are the same could not be 36 rejected). 37 38 39 40 41 42

(9)

9

(a) Long travel time to the city center (b) Low availability of parking

(c) Pavements – easy walking routes throughout the neighborhood

(d) Good cycling facilities within neighborhood and towards adjacent

areas

(e) Easy access to amenities/facilities (f) Parks and open spaces nearby

FIGURE 1. Differences in the accessibility and built environment contexts between respondents neighborhoods in the US and Greece

0% 10% 20% 30%

1 2 3 4 5 6 7

Long travel time to the city center

Responses USA Greece Significance 0% 10% 20% 30% 1 2 3 4 5 6 7

Low availability of parking in the neighborhood

Responses USA Greece Significance 0% 10% 20% 30% 40% 1 2 3 4 5 6 7

Good cycle facilities within neighborhood

Parks and open spaces nearby

Responses USA Greece Significance 0% 10% 20% 30% 40% 50% 1 2 3 4 5 6 7

Economic level of neighbors similar to my level

Responses

USA Greece Significance

(10)

(a) Safe biking conditions (b) Safe walking conditions

(c) Adequate residential green space (d) Nice architectural design of the area

1

FIGURE 2. Differences in the accessibility and built environment contexts between respondents neighborhoods in the US and Greece

2 3 4 5 6 7 8 9 10 11 12 13 0% 10% 20% 30% 1 2 3 4 5 6 7

Safe biking conditions

Safe walking conditions

Adequate residential green space

Nice architectural design of the area

Responses

USA Greece Significance

(11)

11

TABLE 2. Results of Wilcoxon test applied on the distributions of US – GR

1 2 Variable Test-stat. P-value Dif. In loc. Car accessibility

1 Difficult access to central arteries 14 0.01 -2.50

2 Low availability of on -or off -street parking in the neighborhood 78 0.12 -1.00

3 Low parking availability at usual destinations outside the

neighborhood 30 0.00 -3.00

4 High levels of congestion in major urban arteries 31 0.00 -2.00

5 Long travel time to the city center 77 0.01 -1.50

6 High cost of car use (e.g. gas, parking, maintenance) 9.5 0.00 -3.00

Accessibility of alternative means of transport

7 Easy access to public transport services (bus/trolley/tram/metro/rail) 168.5 0.88 0.00

8 Good public transport service (bus/trolley/tram/metro/rail) to city

center and other regional destinations 147 0.79 0.00

9 Pavements – easy walking routes throughout the neighborhood 354.5 0.00 3.00

10 Good cycle facilities within neighborhood and towards adjacent

areas 356.5 0.00 3.00

11 High cost of public transport use (e.g. single ticket, monthly pass) 106 0.16 0.50

Accessibility to shops/facilities

12 Easy access to a district shopping center 104 0.43 0.50

13 Easy access to other amenities/facilities (community/leisure center, facilities for children, local shops such as groceries, bakeries, coffee

shops) 179 0.09 1.00

14 Parks and open spaces nearby 290 0.00 2.50

Travel safety

15 Low level of car traffic on neighborhood streets 259 0.00 2.00

16 Safe walking conditions 372.5 0.00 2.50

17 Safe biking conditions 342.5 0.00 3.50

18 Safe driving conditions 276 0.00 2.50

19 Low crime rate within neighborhood 168 0.18 0.50

Neighborhood physical characteristics

20 High population density 119.5 0.58 -0.50

21 Adequate residential green space 224 0.00 2.50

22 Nice architectural design of residential, civic buildings and/or street

furniture 274 0.01 1.50

Socializing

23 Lots of interaction among neighbors 81.5 0.08 -1.00

24 Economic level of neighbors similar to my level 66 0.16 1.00

3 4 5 6 7 8 9 10 11

(12)

Latent neighborhood preferences and travel attitudes

1 2

Aiming to reduce the number of variables and reveal the latent neighborhood 3

preferences and travel attitudes of the respondents, factor analysis has been performed 4

(18). The varimax rotation has been selected because it results in loadings closer to

5

the extremes (1 and 0). The variables with loadings less than 0.3 have been 6

eliminated, and those with values more than 0.6 are presented in bold. Factor analysis 7

has been applied to the responses about preferences for neighborhood characteristics, 8

and travel attitudes of the respondents. The resulting factors are presented below: 9

1. Preferences for neighborhood characteristics 10

a. Infrastructure provision 11

b. Accessibility 12

c. Public transport provision 13

d. Low-car environment 14

2. Travel attitudes 15

a. Public transport and walking preference 16

b. Car preference 17

c. Cycling preference 18

Infrastructure provision is composed by factors such as the availability of pavements,

19

cycling facilities, parks, safe walking, biking and driving conditions, and nice 20

architectural building and street design (see Table 3). Accessibility to destinations like 21

shopping centers and neighborhood amenities, but also accessibility to transport 22

networks (public transport and central arteries), form the accessibility factor. 23

Characteristics related with public transportation services, in particular, form the third 24

factor, public transport provision. Finally, the low availability of on- and off-street 25

parking in -and outside- the neighborhood, high levels of congestion in major urban 26

arteries, long travel time to the city center, availability of parks and open spaces 27

nearby and safe bike conditions, are correlated, indicating a preference towards a low-28

car environment.

29

The respondents consider infrastructure provision as the most important 30

factor in choosing their residential location at it explains 25.2% of the variance. 31

Accessibility and public transport provision are also very important factors

32

explaining 17.1% and 16.7% of the variance respectively. Finally, low-car

33

environment is another important factor for residential choice, explaining 11% of the 34

variance. Collectively, these four factors explain 70% of the total variance, a very 35

positive outcome. 36

We were also able to distinguish three factors with respect to travel 37

attitudes of the respondents (see Table 4). The first one indicates a preference towards 38

public transportation and walking. Statements describing a liking for public 39

transportation and walking and a preference to use those two modes instead of a car 40

scored particularly high in this factor. The second factor describes a preference 41

towards car. Statements related to the (perceived) higher safety level that car offers in 42

(13)

13

describing a liking for cycling and a preference to use this mode instead of a car, 1

showing the highest scores in this factor. These three factors explain more than 40% 2

of the variance in the data. 3

4

TABLE 3. Results of factor analysis for neighborhood characteristics

5

preferences.

6 7

Loadings Significance

Factor 1 Factor 2 Factor 3 Factor 4

Difficult access to central arteries 0.613

Low availability of on -or off -street parking in the neighborhood

0.918

Low parking availability at usual destinations outside the neighborhood

0.725 0.652

High levels of congestion in major urban arteries

0.333 0.419 0.549

Long travel time to the city center 0.490

High cost of car use (e.g. gas, parking, maintenance)

0.307 0.730

Easy access to public transport services (bus/trolley/tram/metro/rail)

0.651 0.686

Good public transport service

(bus/trolley/tram/metro/rail) to city center and other regional destinations

0.666 0.692

Pavements – easy walking routes throughout the neighborhood

0.533 0.561

Good cycle facilities within neighborhood and towards adjacent areas

0.483 0.322

High cost of public transport use (e.g. single ticket, monthly pass)

0.480 0.523

Easy access to a district shopping center 0.688

Easy access to other amenities/facilities (community/leisure center, facilities for children, local shops such as groceries, bakeries, coffee shops)

0.330 0.880

Parks and open spaces nearby 0.736 0.418 0.339

Low level of car traffic on neighborhood streets

0.556 0.440 0.456

Safe walking conditions 0.538 0.512 0.437

Safe biking conditions 0.567 0.465 0.305

Safe driving conditions 0.828 0.396

Low crime rate within neighborhood 0.638 0.452 0.360

High population density 0.472

Adequate residential green space 0.716 0.474

Nice architectural design of residential, civic buildings and/or street furniture

0.765

Lots of interaction among neighbors 0.823

0.550

Sum of square loadings 6.054 4.114 4.011 2.636

Proportion variance 0.252 0.171 0.167 0.110

Cumulative variance 0.252 0.423 0.590 0.700

Factor interpretation Infrastructure provision Accessibility Public transport provision Low-car environment

(14)

TABLE 4. Results of factor analysis for travel attitudes.

1 2

Loadings Travel attributes

Factor 1 Factor 2 Factor 3

I like walking 0.783 0.376

I prefer to walk rather than drive whenever possible 0.910

I like riding a bicycle 0.728

I prefer to cycle rather than drive whenever possible 0.983

I like travelling by public transport 0.577 I prefer to take public transport than drive whenever

possible

0.656

I like driving

I love getting around the city by car -0.300 0.413 I try to limit my driving to help improve air quality 0.508

The cost of car use (gas, parking, tolls) affects the choices I make about my daily travel

0.500 I often use the telephone/internet to avoid having to travel

somewhere

When I need to buy something, I usually prefer to get it at the closest store possible

-0.346 0.311 The trip to/from work is a useful transition between home

and work

I wish I could teleport from home to work I use my trip to/from work productively

I need a car to do many things I like to do -0.428 0.335

Driving saves a large amount of money 0.406

Travelling by car is safer overall than taking public transport

0.799

Travelling by car is safer overall than walking 0.970

Travelling by car is safer overall than riding a bicycle 0.374 -0.348

Sum of square loadings 3.483 2.648 2.189

Proportion variance 0.174 0.132 0.109 Cumulative variance 0.174 0.306 0.415 Factor interpretation Public transport and walking preference Car

preference _preferenceCycling

3 4 5 6 7 8 9 10

(15)

15

Travel behavior changes

1 2

In order to model the change in driving, walking and bicycle usage before and after 3

relocation to Greece, three linear regression models have been developed. All three 4

models have relatively good fit (R squared equal to 0.46 ~ 0.74). 68% of the 5

respondents stated that they now drive more than in US, while 45% of the respondents 6

answered that they now bike and walk less. 7

Some variables that result in low t-test (less than 95% confidence level) have 8

been maintained into the model, as in this case low t-test values do not necessarily 9

imply low significance, but could be the result of low data availability (19). 10

The results show that changes in accessibility (either in the form of 11

better/worse access to public transport and cycle facilities or in the form of easier 12

access to destinations) are the main determinant of changes in driving (see Table 5). 13

However, travel behavior preferences also appeared to explain (to a lower extent 14

though) changes in driving after relocation from US to Greece. According to our 15

results, the worse the public transport services (β=1.169, t=3.59) and the cycle 16

facilities (β=0.345, t=1.09) in the neighborhoods, where respondents relocated in 17

Greece, the more these respondents tended to use their car. Also, more difficult access 18

to regional shopping centers in Greece was associated with lower levels of car use 19

(β=-0.600, t=-1.94). At the same time, however, lower level of accessibility to 20

neighborhood facilities and amenities was found to be associated with higher levels of 21

driving (β=0.413, t=1.35). Finally, a preference towards walking and public 22

transportation appeared to be associated with lower levels of car use. 23

Concerning the change in bicycle usage from US to Greece, results show 24

that the single most important factor is perceived safety of cycling in each place (see 25

Table 6). The lower the perception of safety about cycling conditions in Greece, the 26

lower the levels of bicycle use of the respondents after relocating in this country (β=-27

1.450, t=6.73). Travel attitudes seem to play a role also in change of bicycle use. 28

Respondents with a preference towards walking and public transportation were more 29

likely to increase bicycle use after relocation to Greece (β=0.332, t=1.70). Moreover, 30

the lower car use and maintenance cost in Greece seems to be associated with lower 31

bicycle use (β=-0.267, t=1.40), while higher levels of car traffic in Greek local roads 32

were found to be associated with higher levels of bicycle use after relocation (β=-33

0.385, t=1.86). 34

The results of the model about changes in walking before and after 35

relocation to Greece (see Table 7) show that both access to public transport services 36

and travel attitudes play a significant role. Specifically, the lower the accessibility to 37

any public transport networks (from local buses to regional trains) the less the 38

respondents used to walk after their relocation to Greece (β=-0.781, t=2.35). Also, 39

preference to walking or public transportation was associated with higher levels of 40

walking in Greece (β=0.753, t=2.50). Finally, higher parking availability outside the 41

neighborhood and lower cost of public transport were associated with lower walking 42

levels after relocation (β=-0.362, t=1.06 and β=-0.572, t=-1.89, respectively). 43

The analysis presented in this paper has been performed in R (20). 44

(16)

1

2

FIGURE 3. Spearman statistic of US-GR variables (Numbers refer to Table 1;

3

bold indicates variable used in later models)

4 5 6 7

(17)

17

TABLE 5. Model estimation: How much less/more do you drive now than in US?

1

Explanatory variables measure differences between the US and Greek contexts.

2 3

Variable definition β t-value

Intercept 5.090 17.58 ***

Good public transport service (bus/trolley/tram/metro/rail) to city center and other regional destinations

1.169 3.59 ** Good cycle facilities within neighborhood and towards adjacent

areas 0.345 1.09

Easy access to a district shopping center -0.600 -1.94 . Easy access to other amenities/facilities (community/leisure center,

facilities for children, local shops such as groceries, bakeries, coffee shops)

0.413 1.35 Nice architectural design of residential, civic buildings and/or street

furniture

-0.762 -2.53 * Public transport and walking preference -0.450 -1.47

R-squared 0.58

F-stat 5.254

4 5

TABLE 6. Model estimation: How much less/more do you bike now than in US?

6

7 8

Intercept 2.712 14.35 ***

High cost of car use (e.g. gas, parking, maintenance) -0.267 -1.40 Low level of car traffic on neighbourhod streets 0.385 1.86 .

Safe biking conditions -1.450 -6.73 ***

Public transport and walking preference 0.332 1.70 .

R-squared 0.74

F-stat 15.05

9 10

TABLE 7. Model estimation: How much less/more do you walk now than in US?

11

12 13

Intercept 3.661 12.51 ***

Difficult access to central arteries 0.363 1.18

Low parking availability at usual destinations outside the

neighborhood -0.362 -1.06

Easy access to public transport services (bus/trolley/tram/metro/rail) -0.781 -2.35 *

High cost of public transport use (e.g. single ticket, monthly pass) -0.572 -1.89 .

Public transport and walking preference 0.753 2.50 *

R-squared 0.46

F-stat 3.956

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 14

(18)

CONCLUSIONS

1 2

Longitudinal research designs have been frequently applied to account for self-3

selection in the relationship between the built environment and travel behavior. 4

However longitudinal studies have restricted their focus on residential relocation 5

within the same region limiting the extent of movers’ exposure to different built 6

environments. This in turn may have led to an underestimation of the built 7

environment effects on travel behavior in comparison to self-selection effects. The 8

objective of this research was to investigate the relationships between the built 9

environment, travel attitudes and travel behavior of people that have moved between 10

totally different urban and transportation contexts, namely US and Greece. 11

An electronic questionnaire was designed (based on (5)) and disseminated 12

on-line to people that currently reside in Greece, but have lived for more than a year 13

in the US. A sample of 31 responses was collected, and the data were qualitatively 14

and quantitatively analyzed using a three-step methodology. 15

Wilcoxon tests’ results showed that accessibility and built environment 16

contexts in which participants of this survey have lived in US and Greece differ 17

significantly. As we expected, after relocation in Greece respondents typically 18

experienced lower car accessibility at regional level (e.g. more difficult access to 19

central arteries and lower parking availability at usual destinations outside the 20

neighborhood) and worse conditions for walking and cycling at the neighborhood 21

level (mainly due to lack of adequate infrastructure, particularly pavements, cycle 22

facilities and open spaces). 23

Quasi-longitudinal analysis showed that changes in driving are mainly 24

associated with changes in accessibility and to a lesser extent with travel attitudes. 25

This result adds to previous evidence linking built environment and car use after 26

accounting for self-selection (e.g. 4, 5, 6, 9). We should however note that in our case 27

the transportation networks component of accessibility seems to play a very important 28

role in changing driving behavior. In other words, lack of adequate public 29

transportation network and cycle facilities in the Greek context was found to be 30

associated with more driving after relocation. The built environment component of 31

accessibility was found to be important as well, with more difficult access to regional 32

shopping centers and better access to neighborhood amenities being associated with 33

lower car travel. These results highlight the importance of a holistic approach (in 34

terms of sustainable land use policies and development of networks for the alternative 35

modes of transport), when it comes to enhancing accessibility in a city and 36

subsequently reducing car use. Quasi-longitudinal analysis of changes in bicycle use 37

and walking between the US and Greece confirmed the importance of the 38

transportation networks component of accessibility for those two modes of transport. 39

It seems that in contexts like Greece, where transport infrastructures are not 40

adequately developed, lack of safe bike conditions and easy access to public 41

transportation are critical determinants of bicycle use and walking. According to a 42

(19)

19

other hand, travel attitudes, and the preference for public transportation and walking 1

in particular, also appeared as important factors for the explanation of change in 2

walking (and to a much lesser extent cycling) after relocation. 3

The authors are aware that there are several limitations that should be taken 4

into consideration in this study. First, the sample profile (mainly students and 5

researchers that have moved from the US to Greece), along with the relatively small 6

sample size, limit the generalizability of our results. A logical next step would involve 7

replication of this study in a greater scale identifying as population, for example, any 8

person that has moved from the US to a European city. Second, many respondents 9

may have returned to Greece a long time ago, meaning that they may not well 10

remember the conditions of their US neighborhood. Also, connected to the previous 11

issue is that respondents’ travel attitudes in this study are assumed to be constant 12

before and after relocation, which simply may not be the case. Finally, perception of 13

accessibility and built environment characteristics may not reflect the objective reality 14

of respondent’s neighborhood characteristics (22, 23), which may restrict the 15

usefulness of our results with respect to policy implications. 16

In conclusion, the results of our study add evidence to the existing 17

literature identifying a causal relationship between the built environment and car use 18

after accounting for travel attitudes. What this study reminds us, though, is that 19

accessibility is defined not only by the spatial allocation of destinations (i.e. the built 20

environment), but also by the transportation networks. Thus, any policies aiming to 21

reduce car use and especially promote walking and cycling may first make sure that 22

adequate transportation networks are developed. 23 24 25 REFERENCES 26 27

1. Mokhtarian P. L. and X. Cao (2008). Examining the impacts of residential 28

self-selection on travel behavior: A focus on methodologies. Transportation 29

Research Part B: Methodological, 42 (3), 204-228. 30

2. Cao X. J., P. L. Mokhtarian and S. L. Handy (2009). Examining the impacts of 31

residential self-selection on travel behavior: A focus on empirical findings. 32

Transport Reviews Vol. 29, No. 3, pp. 359-395. 33

3. Van Wee, B., 2009. Self-Selection: A Key to a Better Understanding of 34

Location Choices, Travel Behaviour and Transport Externalities? Transport 35

Reviews 29 (3): 279–292. 36

4. Krizek K. (2003). Residential relocation and changes in Urban Travel. Does 37

neighborhood-scale urban form matter? APA Journal, Vol. 69, No. 3, 265-281. 38

5. Handy S., X. Cao and P. Mokhtarian (2005). Correlation or causality between 39

the built environment and travel behavior? Evidence from Northern California. 40

Transportation Research Part D, 10, pp. 427-444. 41

6. Aditjandra, P. T., Cao X. J. and C. Mulley (2012). Understanding 42

neighborhood design impact on travel behavior: application of structural 43

(20)

equations model to a British metropolitan data. Transportation Research Part 1

A: Policy and Practice, 46 (1), 22-32. 2

7. Handy S., X. Cao and P. Mokhtarian (2006). Self-selection in the Relationship 3

between the Built Environment and Walking. Empirical Evidence from 4

Northern California. Journal of the American Planning Association, 72 (1): 5

55-74. 6

8. Cao X. J., P. L. Mokhtarian and S. L. Handy (2007). Cross-sectional and 7

quasi-panel explorations of the connection between the built environment and 8

auto ownership. Environment and Planning A Vol.39, pp. 830-847. 9

9. Schneiner J. and C. Holz-Rau (2013). Changes in travel mode use after 10

residential relocation: a contribution to mobility biographies. Transportation, 11

Vol. 40, pp. 431-458. 12

10. Meurs H. and R. Haaijer (2001). Spatial structure and mobility. Transportation 13

Research Part D No. 6, pp. 429-446. 14

11. Aditjandra P. T., C. A. Mulley and J. D. Nelson (2009). Neighbourhood 15

design. Impact on travel behavior: A comparison of US and UK experience. 16

Projections, Vol. 8, MIT Journal of Planning. 17

12. Weijters B. and H. Baumgartner (2012). Misresponse to reversed and negated 18

items in surveys: A review. Journal of Marketing Research, Vol. XLIX, pp. 19

737-747. 20

13. Mann, H. B. and D. R. Whitney (1947) On a test of whether one of two 21

random variables is stochastically larger than the other, Annals of 22

Mathematical Statistics, 18 (1) 50–60. 23

14. Likert, R., 1932. A technique for the measurement of attitudes. Archiv für 24

Psycho- logie (Frankf) 55, 140. 25

15. Richardson, A.J., 2003. Simulation study of estimation of individual specific 26

values of time using adaptive stated-preference study. Transportation Research 27

Record: Journal of the Transportation Research Board, No. 1804, 28

Transportation Research Board of the National Academies, Washington, DC, 29

pp. 13–20. 30

16. Lehamn, A. (2005). Jmp For Basic Univariate And Multivariate Statistics: A 31

Step-by-step Guide. Cary, NC: SAS Press. p. 123. ISBN 1-59047-576-3. 32

17. Myers, J. L.; Well, Arnold D. (2003). Research Design and Statistical 33

Analysis (2nd ed.). Lawrence Erlbaum. p. 508. ISBN 0-8058-4037-0. 34

18. Washington, S.P., Karlaftis, M.G., Mannering, F.L., 2003. Statistical and 35

Econometric Methods for Transportation Data Analysis. Chapman & Hall/ 36

CRC, London. 37

19. Ziliak, S. T. and McCloskey, D. N. (2008). The cult of statistical significance: 38

How the standard error costs us jobs, justice, and lives. University of 39

Michigan Press. 40

20. R Core Team (2014). R: A Language and Environment for Statistical 41

Computing. R Foundation for Statistical Computing. Vienna, Austria, 42

http://www.R-project.org 43

21. Milakis, D., in press. Will Greeks cycle? Exploring intention and attitudes in 44

(21)

21

22. Krizek, J. K., J. Horning and A. El-Geneidy (2012). Perceptions of 1

accessibility to neighbourhood retail and other public services. In: 2

Accessibility analysis and transport planning. Challenges for Europe and 3

North America. Editors: Geurs K. T., K. J. Krizek and A. Reggiani. Edward 4

Elgar Publishing Limited, UK. 5

23. Van Acker, V., B. Derrudder and F. Witlox (2013) Why people use their cars 6

while the build environment imposes cycling. The Journal of Transport and 7

land Use, 6 (1): 53-62. 8