A discrete choice analysis of transport mode choice causality and perceived barriers of sustainable mobility in the MENA region

Transport Policy 79 (2019) 37–53

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A discrete choice analysis of transport mode choice causality and perceived barriers of sustainable mobility in the MENA region

T

Houshmand E. Masoumi Technische Universität Berlin, Germany, Center for Technology and Society, Hardenbergstr. 16-18, 10623 Berlin, Germany

ARTICLE INFO

ABSTRACT

Keywords: Urban transportation planning Urban travel mode choice Travel behavior Multinomial logit model Middle east and north africa

Although there is considerable number of studies on urban travel mode choice, there are still two gaps: we have limited understanding of perceived and attitudinal barriers of sustainable modes and motives of personal car use, and the causes (not correlations) of mode choice decisions are almost unknown for certain geographical contexts such as the Middle East and North Africa (MENA). This study seeks to answer three questions: (1) what are the main barriers to choosing sustainable transport modes like active mobility and public transportation in the Middle East and North Africa? (2) which attitudinal or physical determinants define the transportation mode choice intentions and decisions in Tehran, Istanbul, and Cairo? and (3) what are the differences between the determinants of mode choice decisions in the case cities compared with those of Western societies? In this study, the data collected from 8284 interviewees in Tehran, Istanbul, and Cairo in 2017 were applied in a discrete choice model. The dependent variables of the modeling were the perceived main reasons against walking, biking, and public transit ridership, and the main factor encouraging car-driving. According to the findings, long walking distances, absences or lack of biking infrastructures, social and cultural problems and pressures against biking, and personal preference for cars compared to public transport prevent passengers from walking, biking, and using public transport. Comfort and convenience are the factors that make people avoid public transit in favor of cars. These determinants are fairly different from the main determinants of mode choice decisions in the Western societies. By applying a multinomial logistic regression model, 11 variables related to travel characteristics, perceptions, land-use and neighborhood, socio-economics, and self-selection were found significant or marginally significant in explaining all four models: the barriers to walking, biking, and public transit-use, and the motives for car-use. These findings support the hypothesis of this study that there are differences between the perceived and physical barriers to sustainable mobility as well as the motives of car-use in MENA megacities compared to Western societies. In short, mode-choice decisions and perceived determinants are context-sensitive. The conclusions of this study could be applied in urban and transportation planning in the MENA region to promote more sustainable mobility modes.

1. Introduction Like several regions of the Global South, the urban transportation mode choices of residents of the Middle East and North Africa (MENA) are less well-researched in comparison to the Global North. Moreover, the handful of studies carried out on these countries have had a very strong focus on the mode choice behaviors by analyzing the correlations and leaving the causalities of making decisions about mode selection untouched. This is the case not only for studies on the MENA region but also for research done on high-income countries: they provide very little understanding on the causalities of passengers’ mode selections (refer to the following review of literature for recent studies related to this topic). In other words, we know more about correlations than

causations. The causes of mode choice can be important for decisionmaking and transport planning with the aim of shifting from personal car-use towards more active and sustainable modes. Thus, this study addresses two deficiencies and focuses on the causalities of mode choice in the MENA region. By focusing directly on the needs and preferences of passengers as the backbone of their mobility decisions, the results of this study can be useful for the policy-makers and transportation planners of the region. The objective of this study is to explain the causality of urban travel mode choices in the large cities of MENA. To do so, three larger cities of the region – the so-called megacities of Cairo, Istanbul, and Tehran – were selected for observation. This paper aims to render the research results easy to use for policy-making.

E-mail address: [email protected]. https://doi.org/10.1016/j.tranpol.2019.04.005 Received 13 August 2018; Received in revised form 6 March 2019; Accepted 8 April 2019 Available online 09 April 2019 0967-070X/ © 2019 Elsevier Ltd. All rights reserved.

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The paper continues with a short review of literature about urban travel mode choices in high-income countries and the MENA states with focus on choice behaviors and decisions. The case study areas, data collection, variables, and discrete choice models applied for analysis are then explained. Finally, the findings are discussed by comparing them to previous studies of the region and the international context. Throughout the paper, “behavior” refers to the physical expression of how people act regarding mode selection, while “intention” or “preference” are causes that motivate people to choose a mode.

shopping-leisure trips in Nanjing, China, the hypothesis that “classical socio-demographic variables will have a larger explanatory power in models of mode choice in China than in other Western countries because of limited financial opportunities, a limited transport supply, and a stronger collectivist culture” is proven. This finding could potentially be generalized to regions of the world with more socio-economic, cultural, and spatial differences like the MENA area, but people's mode choice behaviors and intentions in the region are very much unknown, so the linkage is tenuous. The amount of studies on mode choice in the MENA region is hardly comparable to the amount done in China and India. Table 1 summarizes a number of empirical studies conducted based on mobility surveys in the MENA countries. These include researches that only focus on transport mode choice of adults as the dependent variable, while those that focused on special groups such as children (Mehdizadeh et al., 2017) or tourists (Ahmadi Azari, Arintono and Hamid, 2012), or those focusing on mode choice as a part of another transport means (like Attiyah Al-Atawi (2016)), or those that only investigate only one mode of transport (Hatamzadeh et al., 2017; Mehdizadeh et al., 2017) were excluded. One important aspect of these studies is related to the nature of mode choice analyses done in the MENA region. The keywords applied for searching in the Web of Science and Google Scholar were “travel mode choice”, “travel behavior”, and the country names separately for each search. Like the international studies shortly addressed above, all the studies cited in Table 1 except one (Soltanzadeh and Masoumi, 2014) targeted the mode choice behaviors, while the causes of these behaviors remain less known. In other words, the causality of mode choice behaviors has attracted less attention, and mathematical models have only found correlations. Another shortcoming of the mode choice studies in the MENA region is that all of the studies found and summarized in this paper are crosssectional. Due to the difficulty and expensiveness of collecting longitudinal data that tracks mode choice changes over time via panel data, no studies with this kind of approach have been conducted. The studies listed in this table have all been published in 2012 or later; hence, the topic is very new in the region.

2. Background A very large part of the literature focusing on urban transportation mode choices deals with the motives of mode choice that stem from mobility characteristics and the related infrastructures. So far, the influences of several transport-related factors have been investigated, e.g. the significant influences of ability of transit systems to attract passengers and the service quality (Zhao et al., 2002), travel characteristics (Racca and Ratledge, 2003), travel time (Bhat and Sardesai, 2006), combination of in-vehicle time and cost (Algers et al., 1998), value of travel time (Koppelman and Bhat, 2006), tour complexity (Strathman and Dueker, 1996; Ye et al., 2007), availability of car parking at work (Hamre and Buehler, 2014), and traffic safety and the number of nonmotorized trips by the other family members (Aziz et al., 2017) have been found effective in previous studies. Among spatial causes, residential location choices and urban form have also been cited as important factors (Aziz et al., 2017; Ding et al., 2017; Frank et al., 2008; Pinjari et al., 2007; Reilly and Landis, 2003; Van Wee et al., 2002; Zhang, 2004). Moreover, people's attitudes, perceptions, behavioral norms, beliefs, and habits have also been considered deciding elements in mode choices (Beirão and Sarsfield Cabral, 2007; Chen and Chao, 2011; Clark et al., 2016b; Donald and Cooper, 2001; Gardner and Abraham, 2010; Lo et al., 2016). Most of these studies refer to observed factors, while latent and unobserved variables have received less attention. There is a clear lack of studies and observations about the topic in developing and emerging countries. A large part of the literature cited in this paper is based on studies done on cities in high-income countries, while other countries have often been neglected. In a meta-analysis undertaken by Lanzini and Khan (2017), the results of 58 studies on psychological and behavioral determinants of travel mode choice were analyzed. The vast majority were from either Europe, the USA, or Japan – 56 in total. It is probable that new studies on other contexts provide different outcomes. Counterpart studies on the same topic and of the same quality as (Munshi, 2016) are not so frequent and inclusive. This work was done based on the responses of 2050 people in Rajkot, India in 2012 and found significant correlations between access to destinations and other land-use factors with mode choice. By collecting the mobility data of 70 households in Chennai, India, Srinivasan and Rogers (2005) found that variables like gender, employment, income, travel time and cost, and location (in one of the two studied districts) determined mode choices. In a study on two cities in Taiwan, Chen and Lai (2011) found out that rational and habitual factors are both strong predictors of mode choice, but habitual determinants are more influential than rational ones. There are more existing studies on China, such as the work done in Guangzhou by interviewing 1000 residents in 2013 with the conclusion that mode choice is strongly predicted by car ownership but only indirectly affected by attitudinal factors (He and Thøgersen, 2017). In Shanghai, the most important transport attributes for bus-, subway-, and taxi-passengers are in-vehicle time, out-of-vehicle time, and money cost, respectively (LIU, 2007). Shen et al. (2016) also worked on the Shanghai case and found out that money, time, comfort, and safety had influences on mode choice. Their study suggests that rail transit-supported urban expansion can be a viable future strategy for producing more sustainable transport modes. According to Feng et al. (2014), who studied the mode choices for commuting and

3. Research methods 3.1. Research questions and hypotheses The research questions answered in this study are (1) what are the main barriers to choosing sustainable transport modes like active mobility and public transportation in the MENA region? (2) which attitudinal or physical determinants define the transportation mode choice intentions and decisions in Tehran, Istanbul, and Cairo? and (3) what are the differences between the determinants of mode choice decisions in the case cities compared with those of Western societies? The general hypothesis of this study is that there are observable differences between the perceived and physical barriers to walking, biking, and public transit-use as well as the motives behind car-use in the megacities of the MENA region compared to the West. 3.2. Data and variables The primary data used for testing the hypotheses of this study were collected in a mobility survey undertaken in summer and autumn of 2017 in 18 neighborhoods of Cairo, Istanbul, and Tehran (six in each city). In general, 8284 validated subjects resulted from the survey (Cairo: 2786, Istanbul: 2781, Tehran: 2717). The neighborhood-level precisions were 4.5%–4.7% for individual variables and 1.8%–2.4% for household variables. The neighborhoods were selected in three different urban form types, consisting of (1) traditional urban form expressed by historical cores and old peripheral areas, (2) transitional areas including semi-grid street networks with lower population and/or construction compactness, and (3) new developments like centerless 38

800 respondents of all ages (416 males and 384 females)

Four neighborhoods in Kerman, Iran, Oct. and Nov. 2013 Beirut (Jounieh, and Jiyeh areas), Lebanon, June 2013

Rasht, Iran, 2013

22 areas in Shiraz, Iran, 2014

Beirut, Lebanon, 2010

The central business district of Tehran, Iran, May 2010

Tabuk, Saudi Arabia, 2013

Three neighborhoods in Shiraz, Iran, March 2016

Soltanzadeh and Masoumi (2014)

Mehdizadeh et al. (2018)

Etminani-Ghasrodashti and Ardeshiri (2016)

Danaf et al. (2014)

Shahangian et al. (2012)

Al-Atawi and Saleh (2014)

Soltani and Shams (2017)

Chalak et al. (2016)

546 respondents of all ages

Istanbul, Turkey, Jan. 2015

Gokasar and Gunay (2017)

39 396 respondents of all ages

516 households

594 students of American University of Beirut (278 males and 316 females) 572 respondents of all ages (381 males and 192 females)

900 respondents of all ages (78% male)

735 pupil (boys and girls)/ parent pairs

500 respondents of all ages (72.2% were males and 27.8% females)

900 respondents of 41–64 years of age

22 study areas in Shiraz, Iran, 2014

Etminani-Ghasrodashti and Ardeshiri (2015)

Sample

Case-Study Area(s) and time

Author, Year

Table 1 Summary of studies on determinants of mode choice in the MENA countries.

Self-reported questionnaires filled by randomly selected households Self-reported field questionnaire survey

Cross-sectional, binary mixed logit models Cross-sectional, microeconomic-based nested logit model (NLM)

Cross-sectional, stated preference experiment and generalized nested logit models

Cross-sectional, nested logit models

Online survey Stated preference questionnaires filled out in workplaces and schools in the central business district

Cross-sectional, Structural Equations Modeling (SEM)

Cross-sectional, mixed logit modeling (ML)

Cross-sectional, mixed logit mode switching model (MXL), conditional logit (CL), policy simulations

Cross-sectional, Chi-square test of independence

Cross-sectional, maximum likelihood (ML) method, Structural Equations Modeling (SEM) Cross-sectional, Multinomial Logit Model (MNL), Analysis of Variance (ANOVA)

Study Design/Analysis Method

Household survey

Self-administered questionnaires filled out by parents of students in 9 schools

Multi-stage probability sampling approach, personal interviews

Face-to-face interviews by means of standard questionnaires

Self-reported questionnaires

Multi-Stage Cluster sampling, face-todace interviews

Data Collection Method

In order to enhance bus use against car use, access/egress time, headway, in-vehicle travel time, and number of transfers, and the provision of amenities, including airconditioning and Wi-Fi should be strengthened. Several psychological, socio-economic and built environment characteristics are significant factors in determining the mode choices of parents, which influences their children's trip to school. Distance to city center, as well as population and job density are correlated with mode choice. Mix of land uses is associated with non-work mode choices. Street connectivity is related to home-based work mode choices. Travel time, travel cost, income, gender, residence location, and car ownership significantly affect university students' mode choice. Men and women who pay more for using their cars before car use preventive policy implementations, are more likely to continue using their cars. Different needs for the car affect men's and women's mode choices differently. Employment situation affects men's and women's mode choices differently. Social factors are strong predictors of commute mode choices. Household income, occupation, education, and gender are determinants of non-work mode choice. Among urban form traits, accessibility can explain mode choices better than other physical attributes such as urban density and network design and land use diversity.

Trip distance to access Atatürk International Airport, type of destination, trip cost to the airport, automobile ownership status, employment status, traveling group size, location of the trip origin with respect to public transit influence, and time difference between the flight time and departure time to IST affect modes of travel to the airport. Four variables of gender, household size, age, and household car ownership significantly affect modal choice decisions.

The effects of the built environment on mode choice are significant but less important than lifestyle.

Result

H.E. Masoumi

Transport Policy 79 (2019) 37–53

–

Binary

40 –

Categorical Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Binary

Age Household Car Ownership No. of Driving License in Household Household Income Frequency of Commute Trips Last Relocation Time Commuting Distance

Intersections Density

No. of Accessed Facilities

Accessibility to Facilities

Monthly Living Cost Sense of Belonging to Neighborhood

Euro –

Meter

–

Nodes/ha

– – – Euro – – Meter

– – – –

Categorical Binary Binary Categorical

Subjective Security of Public Transport Cycling Entertainment Place Neighborhood Attractiveness Perception Residential Location Choice

–

–

Categorical

Categorical

– – –

– – –

–

Categorical Binary Categorical

Categorical

Reason for Public Transit Use

–

No. of Non-Work Activities Shopping Place Shopping-Entertainment Mode Choice in Neighborhood Shopping-Entertainment Mode Choice outside Neighborhood Attractive Shopping Centers in Neighborhood Frequency of Public Transit Trips

Categorical

Reason for No Public Transit Use

–

Binary Binary Categorical

Categorical

Reason for Not Cycling

–

–

Unit

Gender Individual Driving License Ownership Commute Mode Choice

Categorical

Reason for Not Walking

Independent

Categorical

Reason for Car Use

Dependent

Data Type

Variable

Variable Type for Modeling Purpose

The usual frequency of the respondent's public transportation ridership according to him/her: every day, a few times per week, a few times per month, rarely, almost never. The level of the securing of public transportation according to the respondent's perception: very secure, secure, medium, insecure, and very insecure. Cycling to near destinations inside the neighborhood: yes or no. The place the respondent usually goes to leisure, recreation, and entertainment activities: inside the neighborhood or farther. Perception of the respondent regarding the attractiveness of the neighborhood social/recreational facilities: very attractive, acceptably attractive, medium, little attractive, and not attractive or not available. The main (one) reason of choosing the living place and the neighborhood from the following options: affordability, proximity to working place/school, attractive surrounding environment, higher price of the house in the future, proximity to relatives and/or friends, live here since I was born/my childhood, convenience of commuting, and availability of public transportation. Reported age of the respondent. The number of personal cars possessed by family members. The number of family members who possess a driving license. Reported gross household monthly income converted from Rial (Toman), Turkish Lira, and Egyptian Pound to Euro in summer and autumn of 2017. The number of commute trips of the respondent during the past seven days. The number of year passed from the last residential relocation of the respondent and possibly his/her family. The street network-based distance between home and workplace of respondents who have work/study activity was calculated by the information about the place of home in the neighborhood and the workplace obtained from respondents. The number of intersections per hectare in a 600 m-catchment area (based on the network) of each of the respondents' homes. Calculations were done for areas inside the neighborhood boundary or outside. The number of neighborhood public facilities within a 600 m-catchment area (based on the network) of the respondents' homes. The facilities included five types: bakeries, clinics and other medical centers, mosques, parks, and schools. The average distance (based on the network) from each respondent's home to neighborhood public facilities within the neighborhood or located within a linear 600-m buffer outside the neighborhood boundary. The facilities included five types: bakeries, clinics and other medical centers, mosques, parks, and schools. Reported household monthly living costs converted from Rial (Toman), Turkish Lira, and Egyptian Pound to Euro in summer and autumn of 2017. Respondent's perception about his/her sense of belonging to the neighborhood: yes or no.

The respondents were asked “If you use personal car as daily transport mode, what is the main reason?” Options: cheaper, I like driving, less time, more comfortable, more secure, and no public transportation. The respondents were asked “If you do not walk to destinations in your neighborhood and prefer to use a vehicle, what is the main reason?” Options: the destinations are not near my living place, there are no attractive and beautiful routes, the streets are not safe, there are social and cultural problems in the spaces near my living place, I do not like walking, and it is slow/takes too much time. The respondents were asked “If you do not cycle to your near destinations, what is the reason?” Options: social and cultural reasons, lack of biking facilities, too old/disabled, and it is slow/takes too much time. The respondents were asked “If you do not use public transit, what is the reason?” Options: it is not comfortable, it is expensive, far stations, no accessibility/no public transportation, social problems, it is slow, and I prefer my own car. The respondents were asked “If you use the public transit, what is the main reason?” Options: it is cheaper than other ways, it is faster, for safety reasons, it is more secure, and it is accessible. Male or female (“others” was not applied due to cultural considerations). Possession of a driving license by the respondent: yes or no. One option out of multiple choices: on foot, bicycle, motorbike, taxi, taxi apps, informal public transport, personal/household car, Others, bus/minibus/ metrobus/microbus/BRT/van, metro/light rail train/tram, organizational service/shuttle The number of times the respondent went out for activities related to entertainment, shopping, etc. during the past seven days. The place the respondent usually shops daily living stuff: inside the neighborhood or farther. The place of the respondent's shopping or recreational activities inside the neighborhood: on foot, bicycle, motorbike, taxi, taxi apps, informal public transport, personal/household car, Others, bus/minibus/metrobus/microbus/BRT/van, metro/light rail train/tram, organizational service/shuttle The place of the respondent's shopping or recreational activities outside the neighborhood: on foot, bicycle, motorbike, taxi, taxi apps, informal public transport, personal/household car, Others, bus/minibus/metrobus/microbus/BRT/van, metro/light rail train/tram, and organizational service/shuttle. Presence of attractive shops or shopping centers in the neighborhood of the respondent according to him/her: yes or no.

Description

Table 2 Summary of the dependent and independent variables applied in statistical models.

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neighborhoods, which typically have full-grid street networks. In each city, two neighborhoods of each urban form type were selected. The questionnaire was improved after a pilot survey in five neighborhoods in the three cities (n = 200). The survey instrument included 31 questions in six sections: socioeconomics and household profiles, commute and non-commute travel habits, perceptions about the urban environment, walking and biking infrastructures, and causes of mode choices. By extracting the survey data and generating land-use variables via Geographic Information Systems (GIS) work, 49 variables were developed, including 29 socioeconomic, perception, and mobility variables and 16 land-use variables. A full explanation of the technical details of the survey in the three cities as well as case-study neighborhood selection methods have already been published (Masoumi et al., 2018). Interested readers are recommended to refer to that paper. Twenty-six of the 49 variables were applied as independent variables in this study (Table 2). This was done in a way that reduced collinearity as much as possible. Five variables related to the intentions and reasons of respondents regarding their mode choices were selected as dependent variables: reason for car use, reason for not walking, reason for not cycling, reason for not using public transit, and finally, reason for public transit use. The options that respondents chose from were used as latent-class attributes. Table 3 illustrates the frequencies of percentages of the motives chosen by respondents in the three casestudy cities. During the preliminary modeling, the variable “reason for public transit use” did not yield reliable results; hence, it was omitted, and the study was continued with four models. The dependent variables kept in the analyses, were limited to the barriers of sustainable transportation, these include obstacles to walking, biking, and public transit use as well as the motives of car use as the most-intense unsustainable mode. In other words, the barriers of active and public modes and the motives of personal motorized modes

have been put in one group in this study. The choices of the dependent variables were chosen using the previous studies in the MENA region or outside. Although the number of studies of this type are not considerable, but those scholars that have investigated the mode choice causes have considered some of these as effective motives or barriers, e.g. comfort for public transit use, infrastructures and facilities for biking, and safety and accessibility for walking. Some other choices were added to the study because of the contextual requirements. The best example of such additions is social and cultural issues as barriers against biking (although it can limit walking as well). In many Moslem societies, there are cultural constraints against women's cycling. This choice is seen less in international studies, as maybe in the related contexts, such problems are not so serious. However, in Cairo it can limit the biking activity of half of the population, even if they intend to use bike as a main transport mode. The other barrier considered as social and cultural, is the governmental limitations of women's biking, which may not actually exist but its impacts are transferred to daily lifestyle of people indirectly. In Istanbul, such limitations are less top-down but as much cultural. The motives of sustainable mode use are out of the context of this study. For making the output raw data simple enough for statistical modeling, respondents were asked to give only one barrier or motive as the “dominant” reason including. This eased mathematical modeling and its interpretation and in the same time, made the interviews faster for the interviewers and respondents. The provided options to the central four questions (Table 2) might be somehow correlated, e.g. for public transportation use, no accessibility and far distances between the stations are from the same nature (accessibility and long distances). These are correlated in objective mobility studies. Another point is endogenicity of some of the options in others, e.g. someone might like driving because it is faster or more comfortable or he/she might prefer to drive personal car because public transit is not comfortable enough.

Table 3 Causes behind transportation mode choices (dependent variables) in Cairo, Istanbul, and Tehran in the total sample (n = 8284). Category/City Reason for Car Use

no response cheaper I like driving less time more comfortable more secure

Reason for no Public Transit Use

no public transportation no response expensive Far stations prefer own car No accessibility not comfortable slow Social problems

Reason for not Cycling

no response lack of biking facilities

n % n % n % n % n % n % n % n % n % n % n % n % n % n % n % n % n %

Cairo

Istanbul

Tehran

Total

Category/City

2276 81.7% 17 0.6% 62 2.2% 50 1.8% 303 10.9% 75 2.7% 3 0.1% 1987 71.3% 2 0.1% 25 0.9% 310 11.1% 11 0.4% 321 11.5% 48 1.7% 82 2.9% 290 10.4% 411 14.8%

2567 92.3% 1 0.0% 10 0.4% 42 1.5% 132 4.7% 14 0.5% 15 0.5% 2399 86.3% 2 0.1% 12 0.4% 240 8.6% 15 0.5% 73 2.6% 19 0.7% 21 0.8% 357 12.8% 1555 55.9%

2147 79.0% 7 0.3% 23 0.8% 149 5.5% 339 12.5% 41 1.5% 11 0.4% 1646 60.6% 18 0.7% 31 1.1% 379 13.9% 41 1.5% 369 13.6% 166 6.1% 67 2.5% 301 11.1% 841 31.0%

6990 84.4% 25 0.3% 95 1.1% 241 2.9% 774 9.3% 130 1.6% 29 0.4% 6032 72.8% 22 0.3% 68 0.8% 929 11.2% 67 0.8% 763 9.2% 233 2.8% 170 2.1% 948 11.4% 2807 33.9%

Reason for not Cycling

slow

Reason for Public Transit Use

no response

41

Social and cultural reasons too old/disabled

accessible cheaper faster more secure no car safety

Reason for not Walking

no response destinations not near I don't like walking no attractive routes social and cultural problems streets not safe too old/disabled

n % n % n % n % n % n % n % n % n % n % n % n % n % n % n % n % n %

Cairo

Istanbul

Tehran

Total

362 13.0% 1205 43.3% 518 18.6% 847 30.4% 529 19.0% 919 33.0% 301 10.8% 51 1.8% 124 4.5% 15 0.5% 2227 79.9% 247 8.9% 167 6.0% 53 1.9% 10 0.4% 49 1.8% 33 1.2%

45 1.6% 549 19.7% 275 9.9% 347 12.5% 618 22.2% 971 34.9% 661 23.8% 17 0.6% 161 5.8% 6 0.2% 2532 91.0% 69 2.5% 54 1.9% 36 1.3% 0 0.0% 54 1.9% 36 1.3%

233 8.6% 1025 37.7% 317 11.7% 1043 38.4% 479 17.6% 527 19.4% 356 13.1% 214 7.9% 55 2.0% 43 1.6% 1817 66.9% 455 16.7% 110 4.0% 58 2.1% 48 1.8% 38 1.4% 191 7.0%

640 7.7% 2779 33.5% 1110 13.4% 2237 27.0% 1626 19.6% 2417 29.2% 1318 15.9% 282 3.4% 340 4.1% 64 0.8% 6576 79.4% 771 9.3% 331 4.0% 147 1.8% 58 0.7% 141 1.7% 260 3.1%

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Fig. 1. Breakdown of options chosen by respondents for the dependent variables.

The choices have been designed in their present form because it aims at collecting subjective reasons related to the way people perceive and decide. These psychologic circumstances are different from objective measures that may be actually discrete, e.g. the intersections density of a region and the quality of sidewalks may both be effective variables of walking, but they are completely separate. Correlations maybe much stronger for perceived motives and barriers. Psychologic factors are highly related; altogether they form a package of reasons behind a simple decision of passengers, thus it is hard to separate them from one another and ask respondents to choose from unassociated choices. Moreover, this study seeks the perceptions and attitudes of people, that might be interpreted by a transport expert in another way. Such studies seek what people really think and how they decide about their mobility. The interrelations between choices may also exist between different modes, e.g. personal interest to car driving may make one refrain from active or public transport. However, this may not stop or limit smallerscale model development with the purpose of understanding travel choices. Smaller models are based on detailed questions focusing on only one mode of mobility. This is the approach this study has taken for understanding the overall barriers of sustainable mobility. This will have approximately the same results if the initial models are correctly understood and interpreted. In other words, a high-quality interpretation can join the models and improve the rigor and reliability of conclusions about the obstacles of sustainable transport. The difficulty that might have appeared as a result of making all of the models in one could be that some of the details that are only related to one or two of the modes might be overlooked. For these reasons, four questions about the obstacles have been the bases of four dependent mathematical models for understanding the barriers of sustainability of four modes. This way, it is assumed that descriptive analysis can connect the results of the four models. The other important point regarding the data is related to possibility of integrating the three case-study cities in a unique mode choice causality analysis. Such an analysis necessitates similarity and comparability of the mode choices within the three cities so that the whole respondents can be taken as one sample. To test this hypothesis, a Chisquare test was conducted for mode choices in the three cities for three different travel purposes divided by spatial domains: commute trips, shopping/entertainment travels in the vicinity the homes (inside neighborhoods), shopping/entertainment travels far away from the living places (outside neighborhoods). The output Pearson Chi-square values reject the hypothesis of no relation between the mode choices of

the three cities for the three types of purpose/domains (P < 0.001). In other words, the three mode choices of the three cities are associated, thus it is possible to investigate their causalities in one single sample (Appendix). 3.3. Analysis methods The correlations of the four investigated mode choice causes with the 26 investigated explanatory factors were modeled using a Multinomial Logit (MNL) regression modeling method in IBM SPSS version 25. As discrete choice modeling method, MNL approach is widely used for modeling transport mode choice and the related causes and choices as dependent variables. The reason can be found in the form of the related data. Since the generated data of mode choices and the related issues are often categorical, MNL models are always a reliable and popular method, which is in the same time easier to apply compared to probit logistic models. Thus, in this study MNL models are the selected method although probit models can produce more detailed outputs. Four MNL models were developed using confidence levels of 95%: reason for car use, reason for not walking, reason for not cycling, and reason for not using public transit. The 26 potential predictors were applied in the models and the variables with the highest P-values were omitted from the models until the Nagelkerke Pseudo R2 values of each model reached satisfactory levels. For “reasons for not walking”, four variables (age, gender, cycling, and frequency of public transit use) were eliminated. Only two variables (monthly living cost and sense of belonging to neighborhood) were omitted from the “reasons for not cycling” model. These two variables were also omitted for “reasons for not using public transit” in addition to entertainment place. Lastly, the “reason for car use” led to more omissions consisting of gender, individual driving license ownership, number of non-work activities, shopping place, attractive shopping centers in neighborhood, cycling, entertainment place, age, number of driving licenses in household, last relocation time, commuting distance, number of accessed facilities, accessibility to facilities, monthly living cost, and sense of belonging to neighborhood. 4. Findings 4.1. Descriptive statistics Fig. 1 depicts the percentages of categories for the remaining 42

Transport Policy 79 (2019) 37–53

H.E. Masoumi

dependent variables. Because the questions related to the dependent variables were asked in a conditional fashion, there is large percentages for “no response”, e.g. people were asked “If you do not walk to destinations in your neighborhood and prefer to use a vehicle, what is the main reason?” and more than 79% of them did not answer this question because they walked for daily activities. However, due to the large sample size, the remaining answers still provide a good opportunity to investigate mode choice reasons by focusing on proportions of category values. This provides enough estimation power to examine causalities. The exception is reason for not cycling with a lower non-response rate (roughly 11%), indicating that 89% of respondents either do not cycle or did not answer the question for another reason. Between 640 and 2807 respondents chose one of the options of this question. The bias of the no-responses to the model is not so large as it appears in the first glance, because the questions were asked in a conditional manner as explained above. In fact, the best way is to consider sample sizes of 1708 for “not walking”, 7336 for “not cycling”, 2252 for “not using PT”, and 1294 for “car use”. These sample sizes are not small compared to similar studies, but a more important question is how many people do not walk but they have not answered the question regarding the barriers of their walking, and the so on for the other modes. For answering this, the responses of the four main questions should be compared with other mode choice questions. Out of 7397 individuals who do not bike, 7327 people have answered the question about the barriers. This makes 99% of real response rate. Similarly, out of 1521 People who use public rarely or almost never, 1350 respondents have answered the question about the reasons, making a real response rate of 88.8%. Estimating the real response rate of non-walkers is a bit more difficult because the mode choices have been asked separately for commuting, shopping/ entertainment in and outside the neighborhood. The response rates of non-walkers for shopping and entertainment inside their neighborhoods seems the most relevant figure, because the other two domains are often not within the walkable areas in large cities of this study. The response rates of for non-walkers in the neighborhood is 71.9%. However, the response rates are still more complicated, as some of respondents who use the mode in question to some extent have answered the question about the obstacles because sometimes they do not use that mode. Here these answers refer to the reasons for not using that mode some of the time. Moreover, some of the respondents have also answered the question in an imaginary case like the stated preference choice studies, e.g. in 2252 respondents who have declared they do not use PT because they prefer their own car, 206 and 335 people ride PT almost never or rarely, while people who use PT more including 241 individuals who use it a few times per month, 112 who use it a few times per week, and 34 every-day users have answered the question, and only one person has answered neither. The other example that can show the complexity of mobility decisions, is seen in crosstabulation of reasons for not walking against shopping/entertainment mode choice inside the neighborhood. Among 1708 respondents who have announced what their walking barrier is, there are 32 people who walk to their destinations on foot. To explore the reasons, one should have in mind several latent mode choice motives. The most important barrier to walking is lack of accessibility. About one-fifth of the respondents (20.6%) declared a reason for not walking, 9.3% of which reporting that their destinations are not near. Unlike walking behavior which is limited primarily by spatial barriers, biking is limited by a combination of infrastructure and social problems. At 33.9% and 33.6% respectively, a lack of biking facilities and the presence of social/cultural problems both play a definite role in preventing people from cycling. In Cairo and Tehran, social and cultural problems are more influential (Cairo: 43.3%, Istanbul: 19.7%, Tehran: 37.7%), while people in Istanbul cite the lack of biking infrastructure as the main problem (Cairo: 14.8%, Istanbul: 55.9%, Tehran: 31.0%). It's possible that socio-cultural obstacles have caused the residents of Cairo and Tehran to pay less attention to infrastructure shortcomings. Combined, these two obstacles stop 76% of the respondents who gave

reasons for not biking. One-fifth of the whole sample (20.5%) did not use public transportation because they preferred to drive their own car or did not find public transport comfortable. An overwhelming 96% of those respondents cited these two reasons. Preferring one's own car was important in all three cities (Cairo: 11.1%, Istanbul: 8.6%, Tehran: 13.9%), while lack of comfort was a less important reason for avoiding public transit use in Istanbul (Cairo: 11.5%, Istanbul: 2.6%, Tehran: 13.6%). This might reflect a relatively higher satisfaction with public transit services in Istanbul. These two reasons show the importance of psychological factors as well as the role of the quality of services in determining people's satisfaction with public transport. Likewise, respondents cited comfort as the main motive for personal car use – almost a tenth of the whole sample (9.3%), about 60% of the share of respondents who mentioned reasons for personal car-use. 4.2. Model fit The four final MNL models have 22, 24, 23, and 12 independent variables, but not all of the variables for each model are significant. Table 4 lists the significant variables for each model. Some of the variables were omitted from the models to reach the highest model validity. The elimination of variables continued until the best overall model fit was attained. The final models yielded 22, 22, 14, and 12 significant (P < 0.05) or marginally significant (0.05 < P < 0.10) variables (Table 4). Eleven variables are common to all four models: commute mode choice, shopping-entertainment mode choice in the neighborhood, shopping-entertainment mode choice outside the neighborhood, subjective security of public transport, perception of neighborhood attractiveness, residential location choice, household car ownership, household income, frequency of commute trips, intersection density, and accessibility of facilities. These factors are correlated with mode choice decisions related to walking, biking, public transport, and personal car use, hence they are of critical importance for transport policy-makers in understanding people's mobility-related personal decisions. Table 5 shows the Likelihood ratios and goodness-of-fit of the MNL models of the overall sample. According to this table, the four models can explain a very large part of the variances as the Pseudo R2 (Nagelkerke) of the four models are 96.9%, 86.8%, 98.2%, and 98.6%. As evidenced by their P-values of less than or equal to 0.001, the Likelihood ratios for the four models confirm that the results are very highly significant. Moreover, the Pearson and deviance measures also show a very good fit. Except for the Pearson P-value for “reasons for not walking” at 0.339, all the other P-values for Pearson and deviance are 1, which indicates an excellent model fit. In general, the information displayed in Table 5 confirms the quality of the models, their high validity, and good fit, enabling it to reliably predict mode choice decisions. The large overall sample size and high number of variables have led to high R2 values and model fit. Table 5 includes the results of validity tests, so it only reveals the significance of variables in general and does not give information about the direction of their coefficients. More details about the significance of categories making up each variable consisting of B, P-value, and Exp(B) or β are provided in Table 6. The reference category for the model explaining “Reasons for Not Walking” is “I don't like walking”; the reference categories for the three remaining models (“Reasons for Not Cycling”, “Reasons for No Public Transit Use”, and “Reasons for Car Use”) are “Too old/disabled”, “I prefer my own car”, and “I like driving”, respectively. All the categories listed in the table are highly significant, significant, or marginally significant. Insignificant categories have been eliminated from the table to make it presentable in this paper. The direction and strength of the significant explanatory variables of the four models are listed in Table 6. The coefficient, P-value, and β of each significant variable are listed combining the continuous and categorical variables. The significant variables of the models include all 43

Gender Individual Driving License Ownership Commute Mode Choice No. of Non-Work Activities Shopping Place Shopping-Entertainment Mode Choice in Neighborhood Shopping-Entertainment Mode Choice outside Neighborhood Attractive Shopping Centers in Neighborhood Frequency of Public Transit Trips Subjective Security of Public Transport Cycling Entertainment Place Neighborhood Attractiveness Perception Residential Location Choice Age Household Car Ownership No. of Driving Licenses in Household Household Income Frequency of Commute Trips Last Relocation Time Commuting Distance Intersections Density No. of Accessed Facilities Accessibility to Facilities Monthly Living Cost Sense of Belonging to Neighborhood

Effect

Table 4 Likelihood ratio tests.

21.6 93.9 38.4 16.5 1489.1 87.2 19.4 86.1 30.7 81.4 87.0 9.7 14.2 20.8 69.2 11.5 13.5 64.0 22.1 13.2 19.8 24.4

3947.6 4019.8 3964.3 3942.4 5415.0

4013.1

3945.3

4012.0

3956.6 4007.3

4012.9

3935.6 3940.1 3946.7 3995.1 3937.4 3939.5 3989.9 3948.0 3939.1 3945.7 3950.3

ChiSquare

−2 Log Likelihood of Reduced Model

44 0.139 0.027 0.002 < 0.001 0.075 0.035 < 0.001 0.001 0.040 0.003 0.018

< 0.001

0.002 < 0.001

< 0.001

0.079

0.013

0.042 0.014 < 0.001 0.170 < 0.001

2254.1 33.1 52.4

8431.2 6210.1 6229.5 85.2 350.0 8.1 12.6 11.9 41.5 12.4 10.0 74.6 229.7 240.5

42.5 50.7

6219.6 6227.8

6262.2 6527.1 6185.2 6189.6 6188.9 6218.6 6189.5 6187.1 6251.6 6406.7 6417.5

14.5

90.4

592.5 28.7 69.0 128.1 17.4 75.7

ChiSquare

< 0.001 < 0.001 0.086 0.013 0.018 < 0.001 0.014 0.040 < 0.001 < 0.001 < 0.001

< 0.001 < 0.001 < 0.001

0.002 < 0.001

0.070

< 0.001

< 0.001 < 0.001 0.009 < 0.001 0.026 0.002

P-value

Likelihood Ratio Tests

6191.5

6267.4

6769.5 6205.7 6246.0 6305.1 6194.5 6252.8

−2 Log Likelihood of Reduced Model

Model Fitting Criteria

Likelihood Ratio Tests

Model Fitting Criteria P-value

Reasons for Not Cycling

Reasons for Not Walking

71.0 9.0 3.5 11.7 17.2 19.1 7.5 8.9 18.2 24.0 22.6

47.6

3266.0 3289.4 3227.4 3221.9 3230.1 3235.6 3237.5 3225.8 3227.3 3236.6 3242.4 3240.9

176.3

555.0 181.7

17.1

260.3

27.0 15.1 147.2 141.2 12.1 99.3

ChiSquare

0.085 0.252 0.835 0.110 0.016 0.008 0.382 0.258 0.011 0.001 0.002

0.076

< 0.001

< 0.001 < 0.001

0.249

< 0.001

0.019 0.370 < 0.001 0.297 0.602 0.045

P-value

Likelihood Ratio Tests

3394.6

3773.3 3400.1

3235.5

3478.6

3245.4 3233.5 3365.6 3359.6 3230.4 3317.7

−2 Log Likelihood of Reduced Model

Model Fitting Criteria

Reasons for No Public Transit Use

39.1 16.4 15.8 39.5 46.7

3147.4 314.7 3170.5 3177.7

112.3

73.1

44.8 40.7

3170.1

3243.3

3204.1

3175.8 3171.7

93.9

124.2

3255.2 3224.9

1766.6

Chi-Square

< 0.001

< 0.001

0.012 0.015

< 0.001

< 0.001

< 0.001

0.040 0.092

0.014

< 0.001

< 0.001

P-value

Likelihood Ratio Tests

4897.6

−2 Log Likelihood of Reduced Model

Model Fitting Criteria

Reasons for Car Use

H.E. Masoumi

Transport Policy 79 (2019) 37–53

Transport Policy 79 (2019) 37–53 – < 0.001 1.000 1.000

types, i.e. socio-economics, built environment, perceptions, and mobility-related. The findings for each model are detailled below.

– 12917.9 25629.3 3925.9

– 432 25536 25536

`< 0.001 0.339 1.000

13928.1 6177.0 – – 0.868

– 7751.0 14627.0 6177.0

– 388 16920 16920

– < 0.001 1.000 1.000

17991.3 3218.4 – – 0.982

– 14773.0 16373.0 3218.4

– 665 29617 29617

– 0.001 1.000 1.000

23794.6 3131.0 – – 0.986

– 20663.6 19854.7 3131.0

– 372 36312 36312

4.2.1. Model 1: reasons for not walking The reasons for not walking are listed in comparison to the reference variable of “I don't like walking”. Some of the variables related to residential self-selection were found to be significantly correlated with reasons for not walking. It is 29% less likely that respondents who chose their living place based on the availability of public transport also reported not traveling by foot to their destinations because they were not close-by. Compared to a person who has lived in their current neighborhood since childhood, a person who chose their living place because the neighborhood was attractive is 3.77 times more likely to not walk because their destinations are not nearby rather than having no personal interest in walking. Every additional year of living in the present home increases the probability of not walking due to social and cultural reasons rather than personal preference. Notably, respondents who live in their neighborhoods because they find them attractive are 10.29 times more likely to not walk because of social and cultural issues rather than their own preference against walking. Each additional year of living the current neighborhood increases the chance of not walking because there are no attractive routes rather than personal preference. Sense of belonging is also important for walking: people with no sense of belonging to their neighborhood are 72% more likely to not walk because the destinations are not near compared to their negative attitude to walking, in other words, for these people a physical attribute like accessibility is more important than a perception like interest lack of interest to walking. Compared with having a personal preference against walking, it is 4.27 times more probable that these people do not walk because of social and cultural problems and 5.43 times more likely because they are old or disabled. People who believe their neighborhood is not attractive are much less likely to not walk due to lack of street safety rather than personal preference (91%). People who find their neighborhoods unattractive are 7.33 times more likely to not walk because of age or disability compared to those who find their neighborhood very attractive. The influence of socio-economics is not so dominant in walking decisions. The socio-demographic variables that do correlate with walking choices are mostly economic in nature. Each additional 1000€ of average household income correlates with a 19% greater likelihood of citing that destinations are far away rather than preferring not to walk (β = 0.999812 for every €) and a 10% higher chance of citing social and cultural problems (β = 0.999034 for every €). Each additional car owned by the household correlates with a much higher probability of not walking (89%) due to a lack of attractive routes rather than personally disliking walking. The urban form variables seem to correlate with walking decisions. When intersection density decreases by one unit, the likelihood of deciding to not walk because destinations are far away decreases by 11%, which is surprising and unexpected. When intersection density decreases by one unit, then it is 19%, 27%, and 18% less likely that people decide not to walk because there are no attractive routes, there are social and cultural problems, or they are old/disabled. In other words, if street networks are not connected, people say they dislike walking more often compared to other reasons. Access to each additional neighborhood facility slightly increases the odds of deciding to not walk due to social and cultural problems (0.07%). Travel characteristics are highly correlated with walking decisions. The likelihood of not walking due to high distances between destinations increases by 0.023% (β = 1.000023) for every additional meter. For each additional commute trip per day, respondents are 4% and 6% more likely to not walk because the destinations are far away or the routes are not attractive. Conversely, it is 19% less likely that they do not walk because the routes are unsafe. There is correlation between choosing rail systems, car, or bus for shopping and entertainment activities outside the neighborhood and choosing “no attractive routes” as

Pseudo R-Square

Model Fitting Information Goodness-of-Fit

Null Final Pearson Deviance Nagelkerke

16843.8 3925.9 – – 0.969

P-value df Chi-Square −2 Log Likelihood P-value df Chi-Square −2 Log Likelihood P-value df Chi-Square −2 Log Likelihood df Chi-Square −2 Log Likelihood

P-value

Likelihood Ratio Tests Model Fitting Criteria Likelihood Ratio Tests Model Fitting Criteria Likelihood Ratio Tests Likelihood Ratio Tests Model Fitting Criteria Model

Model Fitting Criteria

Model 1: Reasons for Not Walking Category/Case

Table 5 Likelihood ratios and goodness-of-fit of the MNL models of the overall sample.

Model 2: Reasons for Not Cycling

Model 3: Reasons for No Public Transit Use

Model 4: Reasons for Car Use

H.E. Masoumi

45

46 P-value Exp(B)

Age

0.058

0.021

1.060

less time

Household Income

Reasons for Car Used B

Reasons for Not Using Public Transitc

5.439 0.271 0.874 0.837 7.333

0.821 0.998 0.840 0.818 0.090

Reasons for Not Walkinga

< 0.001 0.001 0.001 0.007 0.082

< 0.001 0.009 0.066 0.071 0.091

social and cultural reasons

slow

Model 4: Reasons for Car Use

1.694 −1.305 −0.134 −0.177 1.992

−0.197 −0.002 −0.174 −0.201 −2.412

0.763 0.999 1.066 10.292

2.472 1.033 0.858 1.076 4.272

0.091

0.064

1.885 0.827 2.198 1.057 0.045

0.636

Model 3: Reasons for Not Public Transit Use

Frequency of Commute Trips Accessibility to Facilities Intersections Density No. of Non-Work Activities Neighborhood Attractiveness Perception = Mediuml Sense of Belonging to Neighborhood = Non Entertainment Place = Far awayf Frequency of Commute Trips Intersections Density Neighborhood Attractiveness Perception = Not attractivel

0.039 0.081 0.086 0.100

0.009 0.081 0.054 0.014 0.038

0.092

0.054

0.020 0.009 0.038 0.051 0.038

0.090

Model 1: Reasons for Not Walking

too old/disabled

streets not safe

l

−0.271 −0.001 0.064 2.331

0.905 0.032 −0.153 0.074 1.452

Attractive shopping centers in NH =Noi Last Relocation time No of non-work activates social and cultural Last Relocation Time problems Sense of Belonging to Neighborhood = No

Intersections Density Household Income No. of Accessed Facilities Residential Location Choice = attractive

−2.394

SEMC out NH = personal/household carh

n

−2.746

SEMC out NH = bush

−0.453 0.634 −0.190 0.788 0.056 −3.109

f

Household car ownership Intersectio ns Density Senses of belonging to neighborhood = Non Frequency of Commute trips SEMC out NH = metro/light rail/tramh

no attractive routes

Shopping Entertainment Place = Farther

n

3.773 1.000 1.044 1.718

Age Accessibility to Facilities No. of Accessed Facilities Intersections Density Individual Driving License Ownership = Noe SEMC out NH= No Responseh No. of Driving License in Household Entertainment Place = Far awayf Subjective Security of Public Transport = Very insecurek Shopping Entertainment Place = Fartherf SEMC out NH =On footh SEMC out NH = personal/household carh Frequency of Commute Trips Commuting Distance Age Accessibility to Facilities Intersections Density Entertainment Place = Far awayf Frequency of Commute Trips Attractive Shopping Centers in NH =Noi Residential Location Choice = affordablem Individual Driving License Ownership = Noe Shopping Entertainment Mode Choice outside NH = h Residential Location Choice = near relativesm No. of Accessed Facilities Residential Location Choice = attractivem Residential Location Choice = near working placem Residential Location Choice = commute to working placem Subjective Security of Public Transport = Very insecurek Age Accessibility to Facilities No. of Accessed Facilities Entertainment Place = Far awayf Subjective Security of Public Transport = Very insecurek SEMC out NH= No Response h No. of Driving License in Household Individual Driving License Ownership = Noe Subjective Security of Public Transport = Insecure25 SEMC out NH = On footh Household Car Ownership Last Relocation Time SEMC in NH = On footg SEMC in NH = motorbikeg SEMC in NH = informal public transportg 0.044 0.050 0.066 0.085

lack of biking facilities

1.328 < 0.001 0.043 0.541

1.000 0.890 0.287

0.012 0.015 0.027

< 0.001 −0.117 −1.248

destinations not near

Household Income Intersections Density Residential Location Choice = Public transportation available m Residential Location Choice = attractive m Commuting Distance Frequency of Commute Trips Sense of Belonging to Neighborhood = No

Reasons for Not Cyclingb

Exp(B)

B

Reasons for Not Walkinga P-value

Model 2: Reasons for Not Cycling

Model 1: Reasons for Not Walking

Table 6 Parameter Estimates for four models: reasons for not walking, reasons for not cycling, reasons for not public transit use, and reasons for car use.

1.000

Exp(B)

0.843 1.004 1.129 0.808 0.338 0.004 0.725 1.621 0.161 1.746 0.078 0.176 0.972 1.000 0.867 1.002 0.814 2.417 1.048 0.582 0.410 0.523 0.020 0.489 1.029 0.417 0.438 0.416 0.183 0.860 1.002 1.054 1.988 0.059 0.003 0.714 0.478 0.216 0.037 1.517 1.019 29.000 35.208 26.726

Exp(B)

(continued on next page)

Pvalue

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.005 0.005 0.013 0.024 0.042 0.078 0.090 0.095 0.096 < 0.001 < 0.001 < 0.001 < 0.001 0.006 0.020 0.021 0.030 0.070 0.073 0.082 0.084 0.086 0.093 0.095 < 0.001 < 0.001 < 0.001 < 0.001 0.001 0.001 0.004 0.005 0.010 0.023 0.028 0.057 0.068 0.082 0.090

P-value

0.000

B

−0.171 0.004 0.121 −0.213 −1.083 −5.444 −0.322 0.483 −1.825 0.557 −2.550 −1.739 −0.028 0.000 −0.142 0.002 −0.206 0.882 0.047 −0.541 −0.892 −0.649 −3.922 −0.715 0.028 −0.875 −0.826 −0.877 −1.696 −0.151 0.002 0.053 0.687 −2.831 −5.936 −0.337 −0.737 −1.533 −3.297 0.417 0.019 3.367 3.561 3.286

B

H.E. Masoumi

Transport Policy 79 (2019) 37–53

not comfortable

47

streets not safe

social and cultural slow problems

no attractive routes

no accessibility

−4.798 1.070 −2.291 −1.712 −3.581 −3.511 −0.001 −3.302 −0.170 −4.629 −2.642 1.266 −3.133 −0.099 0.221 −0.001 −0.034 0.961 0.018 0.933 −3.123 −2.427 0.789 −2.569 1.780 1.024 0.000 −1.363 −0.184 0.000 −0.866 0.039 −3.594 −4.210 −4.290

SEMC out NH = personal/household carh Intersections Density No. of Driving License in Household Accessibility to Facilities Frequency of Commute Trips Neighborhood Attractiveness Perception = Little attractivel Age Neighborhood Attractiveness Perception = Not attractivel SEMC out NH =On footh SEMC out NH = metro/light rail/tramh Neighborhood Attractiveness Perception = Mediuml Frequency of Public Transit = Trips a few times per weekj

Individual Driving License Ownership = Noe Attractive Shopping Centers in NH =Noi Commuting Distance Frequency of Public Transit Trips = almost neverj Intersections Density Household Income Shopping Entertainment Place = Fartherf Frequency of Commute Trips SEMC in NH = personal/household carg SEMC in NH = informal public transport g SEMC in NH = bicycleg

0.010 0.027 0.071 0.078 0.014 0.016 0.021 0.042 0.082 0.045 0.100 < 0.001 0.014 0.014 0.029 0.030 0.043 0.053 0.061 0.069 0.075 0.091 0.095 < 0.001 0.001 0.001 0.004 0.007 0.008 0.009 0.017 0.033 0.061 0.066 0.069 5.932 2.785 1.000 0.256 0.832 1.000 0.421 1.040 0.027 0.015 0.014

0.044 0.906 1.248 0.999 0.966 2.614 1.018 2.543 0.044 0.088 2.202 0.077

0.008 2.914 0.101 0.180 0.028 0.030 0.999 0.037 0.844 0.010 0.071 3.547

cheaper

more comfortable

0.029 0.042 0.044 0.064

0.001 2.881 0.876 1.907

Accessibility to Facilities Subjective Security of Public Transport = k Frequency of Public Transit Trips = a few times per monthj Neighborhood Attractiveness Perception = Not attractivel

−0.822 1.465 1.665

Residential Location Choice = affordablem Subjective Security of Public Transport = Insecurek Neighborhood Attractiveness Perception = Not attractivel Frequency of Public Transit Trips = almost neverj Subjective Security of Public Transport = k Household Car Ownership Household Car Ownership

0.098 0.100 0.003

0.088

0.061 0.067

0.031

0.025 0.026

0.559 5.102 3.509 2.858

5.285

0.440 4.328

5.147

0.050 1.000

6.241

2.360 1.002 8.322

5.324

9.359

6.731

1.001 17.832 2.401

17.428

0.837 1.995 16.569

Exp(B)

(continued on next page)

−0.582 1.630 1.255 1.050

1.638

Subjective Security of Public Transport = Mediumk

−2.990 0.000

0.020

1.831 Frequency of Public Transit Trips = No Response Household Income

0.001 0.001 0.018

0.859 0.002 2.119

j

0.081

1.672

Neighborhood Attractiveness Perception = Little attractivel Household Car Ownership Accessibility to Facilities Neighborhood Attractiveness Perception = Little attractivel Subjective Security of Public Transport = Securek

0.077

2.236

Subjective Security of Public Transport = Insecurek

0.022

2.858

Subjective Security of Public Transport = Securek

< 0.001 0.013 0.015 0.021

Pvalue

−0.178 0.691 2.808

B

Intersections Density Household Car Ownership Subjective Security of Public Transport = Mediumk

far stations

destinations not near

SEMC out NH = No Response h Household Car Ownership Residential Location Choice = commute to working placem Residential Location Choice = near relativesm Subjective Security of Public Transport = Mediumk Subjective Security of Public Transport = Securek Household Income Subjective Security of Public Transport = Insecurek Frequency of Commute Trips SEMC out NH = No Response h Neighborhood Attractiveness Perception = Acceptably attractivel Individual Driving License Ownership = Noe

Reasons for Car Used

P-value Exp(B)

Reasons for Not Using Public Transitc

Reasons for Not Walkinga B

Model 4: Reasons for Car Use

Model 3: Reasons for Not Public Transit Use

Model 1: Reasons for Not Walking

Table 6 (continued)

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social problems

3.582 −3.651 −1.364 1.446 −0.189 0.000 0.717 −1.454

0.078 0.094 0.005 0.012 0.025 0.030 0.052 0.056

b

The reference category is: I don't like walking. The reference category is: Too old/disabled. c The reference category is: I prefer my own car. d The reference category is: I like driving. e Refer to Individual Driving License Ownership = Yes. f Refer to Shopping-Entertainment/Entertainment Place = Neighborhood. g Refer to Shopping-Entertainment Mode Choice in Neighborhood (SEMC in NH) = taxi apps. h Refer to Shopping-Entertainment Mode Choice outside Neighborhood (SEMC out NH) = taxi apps. i Refer to Attractive Shopping Centers in Neighborhood (NH) = Yes. j Refer to Frequency of Public Transit Trips = rarely. k Refer to Subjective Security of Public Transport = Very secure. l Refer to Neighborhood Attractiveness Perception = Very attractive. m Refer to Residential Location Choice = since childhood. n Refer to Sense of Belonging to Neighborhood = yes.

a

too old/disabled

Neighborhood Attractiveness Perception = no responsel SEMC in NH = busg Cycling = No Individual Driving License Ownership = Noe Intersections Density Commuting Distance Attractive Shopping Centers in NH=Noi Neighborhood Attractiveness Perception = Acceptably attractivel 35.953 0.026 0.256 4.244 0.827 1.000 2.048 0.234

Intersections Density Frequency of Public Transit Trips = almost neverj

Household Income Frequency of Public Transit Trips = a few times per no public transporta- weekj tion Residential Location Choice = attractivem Accessibility to Facilities

more secure

Reasons for Car Used

P-value Exp(B)

Reasons for Not Using Public Transitc

Reasons for Not Walkinga B

Model 4: Reasons for Car Use

Model 3: Reasons for Not Public Transit Use

Model 1: Reasons for Not Walking

Table 6 (continued)

0.059 0.065 0.070 0.080

2.011 0.001

< 0.001 0.008 0.010

Pvalue

0.000 −2.418

−0.223 −1.146

B

7.469 1.001

1.000 0.089

0.800 0.318

Exp(B)

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a reason for not walking. These people are more likely to use taxi apps for such travels. People who do their shopping and entertainment outside their neighborhood are 64% less likely to not walk because destinations are far away. People who do their non-work activities outside the neighborhood are 27% more likely to not walk because of their personal preference rather than reasons related to age and disability. Each additional non-work activity per week correlates with an 18% lower probability of choosing “routes are not safe” instead of “I don't like walking”. Lastly, perceptions about the urban environment play a role in influencing walking decisions. Residents who reported not having a sense of belonging in their neighborhood are 72% more likely to not walk because their destinations are far away and 2.2 times more likely to not walk due to a lack of attractive routes. If they do not find the neighborhood shops attractive, they are 147% more likely to choose “no attractive routes”. If they perceive their neighborhood as moderately attractive, they are 9% more likely to not walk because they do not like walking rather than because the streets are unsafe.

for the model explaining causes of not using public transportation. Like biking, age is important in determining public transit usage. With each additional year of age, respondents are 6% more likely to not use public transit because the stations are far away and 2% more likely to not use it due to lack of comfort. Every one-car increase per household is associated with a 2.9-time increase in the likelihood of choosing “far stations”. Respondents who chose their residential place because of commuting to work or being near to relatives are less likely to choose “far stations” compared to respondents who reported that they lived [at their current home] since childhood. Compared to personal preference for car, the number of individuals per household and the number of driving licenses per household have a positive association with not using public transport due to lack of comfort, low speed, or societal problems. Subjective perceptions of safety in the public transport process (traveling to the transit stations and being in the stations and vehicles) are only associated with decisions related to accessibility. Public transit riders who perceive security as “insecure”, “medium”, or “secure” are slightly less likely to not use public transport because of inaccessibility (3–4%) compared to those who find public transit very secure. In reference to preference for car, the number of commute trips has a strong negative correlation with not using public transit due to accessibility, comfort, and speed. In other words, respondents who make more commute trips are less likely to not commute via public transit due to these reasons and more likely to commute via personal car because they simply like driving the car. People who choose car, rail, or walking rather than taxi apps to travel outside of their neighborhoods are less likely to not use public transport because of lack of comfort. The mode choices for intraneighborhood trips including bus, bike, paratransit, and personal/ household car are negatively correlated with not using public transport because of slow speeds. The attractiveness of the shopping opportunities is also decisive for some passengers. Compared to respondents who find their neighborhood shops attractive, respondents who do not find the shops and shopping malls of their neighborhoods attractive are 2.8 times more likely to choose “low speed” and 2.05 times more likely to choose social and cultural problems as a barriers of public transit use. Respondents who commute more are more likely to not use public transport because of low speed or societal difficulties. Compared to those who find their neighborhood very attractive, people who find their neighborhood less or moderately attractive are 2.6 and 2.2 times less likely to not use public transit because of lack of comfort instead of preference for car. Some of the land-use variables also correlate with barriers to public transit use. Intersection density has strong negative correlation with choosing “not comfortable”, “slow”, and “social and cultural problems” over preference for car-driving. If the street networks become more connected, the reasons for not using public transit tend toward personal preference for car use rather than other reasons like speed, comfort, and societal problems. Accessibility to facilities (distance) only correlates with comfort.

4.2.2. Model 2: reasons for not cycling For analyzing the causalities of biking decisions, “too old/disabled” was selected as a reference category representing personal physical limitations. As expected, age is correlated with all the decisions based on all other categories (lack of biking facilities, slow, and social and cultural reasons), meaning it is a fairly decisive factor in affecting people's biking decisions. Every additional year correlates negatively with choosing “lack of biking facilities” (−16%), “slow” (−13%), and “social and cultural problems” (−14%). In other words, for every additional year of age, a person is 14% more likely to not bike primarily due to being old or disabled. People who do not possess a driving license are less likely to choose the above three reasons for not biking instead of “too old/disabled” compared to those who have one. Number of driving licenses in the household is negatively correlated with deciding to not bike due to lack of biking facilities and social and cultural problems instead of being old or disabled. All the other correlating factors are related to perceptions, the built environment, and travels. Increasing intersection density is negatively correlated with not biking because of the lack of biking and being slow rather than age and physical ability. Both accessible distance to neighborhood facilities and the number of accessible facilities are correlated with all three biking reasons (lack of biking facilities, slow, and social and cultural reasons). Commuting distance is only associated with social and cultural as a barrier. Relative to being old or disabled, the respondent's shopping/entertainment location is strongly correlated with all three biking barriers. Residents who do not perceive their neighborhood as an attractive place are 58% less likely to not cycle because of all the above three reasons instead of being old/disabled compared to people who find their neighborhood attractive. Residential self-selection also plays a role when it comes to deciding to bike. Compared to those who have lived in their homes since childhood, people who choose their home location based on being near to their work, commuting, or living in an attractive neighborhood are more likely to not bike due to being old or disabled rather than because it is slow. Compared to those who say public transportation is very safe, respondents who said that public transportation is very unsafe are 16% and 22% less likely to not bike because there are not enough biking facilities or there are social and cultural problems rather than age and disability. Like walking, biking is limited when people have lived in their neighborhood for longer times. Respondents who moved houses a long time ago are more likely to not bike because of social and cultural problems rather than age or disability.

4.2.4. Model 4: reasons for car use The fourth model deals with the reasons for driving an individual or household car. The reference category of this model is “I like driving”, which addresses a personal preference motive. Household income is positively correlated with choosing “less time” as a reason for driving a car opposed to choosing “I like driving”. As expected, household car ownership plays an important role in decisions about car-driving. It is positively associated with choosing “less time”, “more comfortable”, “cheaper”, and “more secure” by 2, 2.36, 3.51, and 2.86 times more than personal preference for driving a car. The strongest correlations of all four models were found between the perceived security of public transit and choosing time and comfort as the reasons for car use rather than personal interest. People who believe their public transportation systems are not very safe (average,

4.2.3. Model 3: reasons for not using public transit The response “I prefer my own car” has been selected as reference 49

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secure, insecure, etc.) are between 5 and 17 times more likely to drive their own car because it is faster or more comfortable compared to those who find their public transit secure. Accessible distance to facilities is positively correlated with driving due to it being faster or more comfortable, or because there is no public transport. When intersection density decreases, it is more probable that passengers drive cars because it is faster or more secure rather than because they simply like driving. Finally, when perceived neighborhood attractiveness is weakened, then it is highly probable (428–732%) that people drive a car because it requires less time or is more comfortable. Residential self-selections correlate with some of the reasons. People who choose their living locations because of affordability are 44% more likely to drive a car because it is more comfortable. Passengers whose location choice is based on neighborhood attractiveness are about 7.5 times more likely to drive a car because no public transport is accessible rather than a personal preference for car-driving.

The obstacle to walking that respondents cited most frequently was the lack of accessibility to destinations and facilities. These results show that respondents choose to not walk to destinations because those destinations are not within walkable distances. This finding is in line the results of a study on Seattle, USA, and Toronto and Ottawa, Canada (Goldsmith, 1992), in which the most important constraint on walking was distance (33%, 47%, and 56% of the respondents, respectively). The negative influence of distance on walking has also been repeated in other studies (McKeever et al., 1991). In contrast to this, (Walton and Sunseri, 2010) did not find that distance, fear of crime, carriage of goods, or concern for time affected walking to public transportation. They found that the convenience of a car park induces park-and-ride demand the most limiting factor for such trips. Other studies discuss different main barriers to walking, such as lack of walking facilities and lack of safety and recreational facilities in the USA (Brownson et al., 2000; Eyler et al., 1998; King et al., 2000; Li et al., 2005), poor access to facilities, road safety, and fear of injury in Finland (Vuori et al., 1994) and Australia (Booth et al., 2000). The survey results showed that the lack of biking facilities and presence of social and cultural problems are the most impactful barriers to biking. The former is in line with the finding of Akar and Clifton (2009), who showed that the students of the University of Maryland, USA are more likely to bike as a commute mode if there are more lanes, trails, and paths to do so. This result is corroborated by a study on 43 large cities in the US (excluding college towns) that, after controlling for demographic and geographic variables, found that increasing biking infrastructures is associated with increased commuting by bike (Dill and Carr, 2003). However, societal problems limiting bicycling have been less studied in other contexts, at least in the form that this study has investigated them. In many areas of the region, biking is sometimes seen as a strange or outdated way of mobility. Awareness-raising via marketing and public programs might be what MENA cities need to increase their biking modal share. People are better to be more informed and aware of alternative modes of transport and use all possible options for different activities (Rose and Marfurt, 2007). The results of an example of the Western studies that address social problems limiting biking was published back in 2001 based on an omnibus survey in Woodley, between Reading and Wokingham, Hull, and Middlesborough, UK. The findings of interviews with 300 individuals in the three mentioned areas showed that the most influential factor against biking is the social pressure (Davies et al., 2001). However, a wide range of other studies address other issues, such as trip length, gradient, traffic, weather, and people's physical fitness in the UK, USA, and Australia (Kingham et al., 2001; Newby, 1993; Wardman et al., 1997). The findings of this study confirms the suggestion of Gatersleben and Appleton (2007): “If we want to increase cycling it is necessary to make infrastructural changes (e.g., providing more cycle lanes) as well as societal changes (changing work hours and attitudes to parenting) and individual changes (e.g., attitudes towards driving and cycling).” Of course, this conclusion was made for a British context, but it was published 11 years before the present research on the MENA region and several European countries have successfully improved bicycle infrastructure and removed social barriers in the meantime. The most important barriers to public transit use in this study's sample were preferring a personal car and the lack of comfort on public transport. A review carried out in 2013 on the quality attributes of public transport lists the most-addressed public transport improvement strategies that can attract car-users. This review found 26 studies related to comfort (e.g. improving the quality of stations, seats, vehicles, etc.) and 24 to convenience (e.g. streamlining ticket purchases, online systems, etc.); reliability with 34 studies, frequency with 23, and speed with 39 were other important qualities (Redman et al., 2013). Beirão and Cabral (2007) compiled a list of disadvantages of public transportation in Porto, Portugal from in-depth interviews and found eleven shortcomings: waste of time, too crowded, lack of comfort, time uncertainty, lack of control, unreliability, long waiting times, need for transfers, traffic, lack of flexibility, and long walking time. Their respondents mention lack of comfort as one of the barriers to using public transit but do not explicitly state that they do not ride bus and metro simply because they like driving their car. Based on this, it follows that, people in

5. Discussion Availability and accessibility of destinations and infrastructures are the main constraints against non-motorized transportation in the three studied cities. Social and cultural issues limit biking as well. Lack of comfort and convenience are the most influential factors limiting public transit ridership and motivating car use. Moreover, personal preference for car is a decisive limiting factor of public transport use in Tehran, Istanbul, and Cairo. This research finds a very complex relationship between personal norms and preferences and latent factors in defining the modal choice decisions. In general, this study is in line with the finding of Bamberg et al. (2003), who concluded that “choice of travel mode is largely a reasoned decision; that this decision can be affected by interventions that produce change in attitudes, subjective norms, and perceptions of behavioral control; and that past travel choice contributes to the prediction of later behavior only if circumstances remain relatively stable.” In order to change personal preferences for cars, transportation policy and urban planning can improve the general condition of public transportation and non-motorized infrastructures like walking and biking routes and other environment-related issues in the built environment. According to the findings of this causality analysis, these improvements could influence decisions that are currently based on personal preference and established norms. For example, increasing street network connectivity could result in fewer decisions against public transportation due to lack of comfort and lengthy travel time. Another example is that subjectively more attractive neighborhoods increase the probability of not biking because of old age or physical problems instead of issues related to accessibility, comfort, speed, and societal problems. In these cases, interventions can be firstly designed to improve the quality of urban transportation and the built environment simultaneously, and these measures are then followed by informative measures aiming at changing people's attitudes regarding sustainable mobility. Currently, it does not seem that environmentally friendly transport has become a widely accepted societal norm in the region. This necessitates changing mobility preferences by changing people's perceptions about what are mobility options are considered viable and valuable. The results of this study address a wide range of variables including individual, household, and socio-economic factors as well as travel characteristics, residential self-selections, and land-use attributes that drive people in the MENA region away from sustainable transport modes and toward car use. Moreover, there is evidence that commuting by car is more stable than commuting by bus, bike, or on foot, which makes it is especially difficult to change it toward sustainable modes (Clark et al., 2016a). Thus, urban planning interventions must address several causative factors simultaneously in order to effectively produce sustainable urban transformations. 50

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the MENA context might still continue driving their cars due to cultural and societal reasons even if several aspects of public transit systems were improved. This “lifestyle” factor has not been addressed as a key element in the European studies. Comfort has been assessed in other studies, such as a Swedish study on 1708 commuters of age 18 to 64 who lived in Uppsala and worked in Stockholm (Johansson et al., 2006): “preferences for comfort increase the likelihood of choosing bus over car and train over bus.” Personal norms towards the environment and personal obligations to use public transport have been found to correlate significantly with public transport use in two samples in Bochum/Dortmund and Frankfurt, Germany (Bamberg et al., 2007), but these norms were not found to be important in the MENA context. During the survey, very few people mentioned such issues; hence, the survey team did not include them in the survey instrument. The main factor mentioned as a motive for car use in the MENA case-study areas was comfort, which was included in the questionnaire, indirectly addressing both comfort and convenience. In this study, comfort and convenience were the most-cited reason by the respondents. This finding is in line with the finding of a study in Porto, Portugal that listed ten factors that encourage people to use car: freedom/independence, ability to go where I want, convenience, rapidity, comfort, flexibility, know what I can expect, safety, having my own private space, and listen to music (Beirão and Cabral, 2007). The study did not rank the reasons, but comfort and convenience were both mentioned by interviewees as important motives. The Swedish study on Uppsala-Stockholm commuters (75 km) also confirms the importance of comfort but they also find flexibility (referring to the need to shop, do daily tasks, collect children on the way to and from work, etc.) important. Flexibility has not been addressed by the respondents of this study even in the pilot survey. The study of Palma and Rochat (2000) in Geneva, Switzerland showed that household size is important in determining car use: larger households tend to use cars more. In contrast, the present study did not find any relationship between household size and car use. One could argue that the number of driving license holders in Geneva in the year 2000 might be more than that of the MENA cities, but the number of individual/household driving licenses was not found to be significant in the fourth model of this study. In Gothenburg, Sweden, the most influential driver of car use for short distances is time (24%), but this element is in second place in the MENA-study, drawing less than onethird of the attention in comparison to comfort. As a hypothesis that deserves much research, it can be added that the potential main reason behind such relatively contextual differences between barriers of sustainable mobility modes in the MENA region and the industrial countries can be sought in culture and geography. These two can be found behind many contextual differences such as overuse of personal cars and poor motivation towards active transport. An example can be observed in lack or even absence of tendency toward biking in women in Iran. After the 1979, a religious regime took over the country and set Islamic lifestyle as the basis of everything including women's behaviors and appearance. So many years after the revolution, women's cycling became an act against social norms, while no rules were against it. Today after four decades, women have biking tendency near to zero not because of the rules but because the dictated religious lifestyle has been established in the society. This can affect half of the country's population, so studying biking and especially its causes is heavily under its influence. The same situation can be seen in Egypt, with the difference that the lifestyle is not top-down and comes from people's self-selection. Another example is the effect of hot climate on walking decisions. Although this car use motive can be dealt with like the approach to biking in cold European climates as in the Amsterdam or Copenhagen, it is still a big obstacle due to lack of effective mobility and urban planning policies and infrastructures in the MENA region. In short, the national will for shifting the modes toward walking is absent in favor of the more profitable monopolized car industry. Such

context-specific relationships between socioeconomics and nationallevel strategies one the one side and individuals’ transportation mode choices are under-researched and need good datasets and funding. 6. Conclusion The findings of this study shed light on the perceived and objective obstacles to sustainable transportation like non-motorized and public transport in the megacities of the MENA region. According to these results, long walking distances limit pedestrian activities, while the absence/complete lack of bicycling facilities and presence of social and cultural problems reduce people's biking. Personal preference for the car diminishes public transit ridership. Meanwhile, the lack of comfort and convenience in public transit systems is the main factor causing people to avoid using public transportation. The same factors (comfort and convenience) lead passengers toward car use. The above-mentioned determinants of mode choice decisions in the MENA region are not exactly the same as those of Western countries, where the largest part of the related literature stems from. In those countries, social and cultural problems are not the most influential barriers to biking and comfort is not the main determinant of public transport and car use. By discrete choice modeling the decisions that residents make about walking, biking, and using public transit together with their motives for using personal vehicles, a total of eleven variables were found to almost entirely determine people's mode choice decisions. Those factors were commute mode choice, shopping-entertainment mode choice in neighborhood, shopping-entertainment mode choice outside the neighborhood, subjective security of public transport, perception of neighborhood attractiveness, residential location choice, household car ownership, household income, frequency of commute trips, intersection density, and accessibility of facilities. The four mentioned models listed 22, 22, 14, and 12 different variables as significant or marginally significant determinants of the above dependent variables. These findings support the hypothesis that there are differences between the perceived and physical barriers to sustainable mobility and the motives for car use in the megacities of the MENA region compared to Western societies. However, further research is needed to study the applicability and generalizability of these findings to other large cities of the region. It is not exactly known how much of these results can be used for policymaking in large yet (potentially) less complex or smaller cities of the region. According to the results of an unpublished paper, the results of this study can be generalized to 9 other cities of more than one million inhabitants, namely Algiers, Algeria; Tabriz, Iran; Fez, Marrakech, and Rabat in Morocco; Tunis, Tunisia; and Ankara, Bursa, and Konya in Turkey. This generalization has been derived of comparing the three case-study cities of this study with 57 cities of more than one million population in the region based on 16 criteria (motorization, publicprivate investment in transport, gross domestic production per capita, Gini coefficient, absolute poverty, high temperature, low temperature, temperature range, high precipitation, low precipitation, precipitation range, median age, social support, free choice, perception of corruption, and Human Development Index). With applying 14 and 12 generalization criteria, the number of comparable one-million cities increased to 18 and 25 respectively. This shows acceptable transferability and generalizability of the findings of this study to other relatively large cities of the region. Furthermore, more detailed data collection methods such as longitudinal, stratified, or panel surveys can provide more reliable and robust modeling inputs. Acknowledgement This study was undertaken by the support of German Research Foundation (DFG) as a part of the research project “Urban Travel Behavior in Large Cities of MENA Region (UTB-MENA)” with the project number MA 6412/3-1. 51

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Appendix Transportation mode choices across the three case-study cities for three different purposes: commuting as well as shopping/entertainment in and outside of the neighborhood. No bicycle bus Response

informal public transport

Commute Mode Choice City Cairo 649 24 760 7 Istanbul 942 1 808 16 Tehran 888 8 380 126 Total 2479 33 1948 149 Shopping-Entertainment Mode Choice in Neighborhood City Cairo 14 55 317 34 Istanbul 47 3 191 4 Tehran 4 53 129 185 Total 65 111 637 223 Shopping-Entertainment Mode Choice outside Neighborhood City Cairo 38 11 1070 25 Istanbul 47 0 1436 0 Tehran 10 9 294 189 Total 95 20 2800 214

metro/light rail/tram

motorbike On foot

Others personal/ household car

service/ shuttle

taxi taxi apps

Total Pearson Chi-square

P-value

272 290 512 1074

100 12 79 191

256 288 114 658

9 6 1 16

485 219 521 1225

150 189 43 382

37 9 22 68

37 1 23 61

2786 955.7 2781 2717 8284

< 0.001

17 0 10 27

88 6 97 191

1833 2442 1634 5909

32 0 0 32

334 78 547 959

4 5 8 17

41 4 39 84

17 1 11 29

2786 1164.8 2781 2717 8284

< 0.001

401 704 251 1356

93 10 97 200

11 26 21 58

2 1 1 4

816 498 1749 3063

0 2 15 17

187 57 6 250

132 0 75 207

2786 2572.2 2781 2717 8284

< 0.001

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