On the relationships between commuting mode choice and public health

Journal of Transport & Health ∎ (∎∎∎∎) ∎∎∎–∎∎∎

Contents lists available at ScienceDirect

Journal of Transport & Health journal homepage: www.elsevier.com/locate/jth

On the relationships between commuting mode choice and public health Mehrdad Tajalli, Ali Hajbabaie n Civil and Environmental Engineering Department, Washington State University, PO Box 642910, Pullman, WA 99164-2910, USA

a r t i c l e i n f o

abstract

Article history: Received 2 August 2016 Received in revised form 16 December 2016 Accepted 21 December 2016

This paper studies the associations that may exist between commuting mode choice and public health. For this purpose, we used Community Health Survey data collected in New York City in 2010. Obesity, blood pressure, and diabetes are used as indicators of respondents’ physical health, and Non-Specific Psychological Distress as an indicator of respondents’ mental health. After rigorous statistical analyses, a binary probit model was fitted for each physical and mental health indicator to quantify the associations between different commuting modes and physical/mental health. Results show that walking, as expected, is associated with a lower probability of obesity, hypertension, diabetes, and mental disorders (all statistically significant) when compared to using private transportation. Using subway is related to a lower probability of obesity and diabetes while using the city bus was linked with a higher probability of obesity (all statistically significant) compared to using personal vehicles. Finally, in comparison with using personal vehicles, working at home is associated with a higher probability of having mental disorders (statistically significant). & 2017 Elsevier Ltd All rights reserved.

Keywords: Commuting mode choice Public health Mental health Homeworking Choice model Binary probit

1. Introduction Traffic congestion yielded 6.9 billion hours of delay, 3.1 billion gallons of extra fuel consumption, and a total cost of $160 billion in 2014 across the United States (Schrank et al., 2015). Passenger cars are among major contributors to traffic congestion, fuel consumption, and air pollution in the US metropolitan areas. In fact, about 88% of all US workers commute to their work place using passenger cars with 77% driving alone (Wener and Evans, 2011). These values are high and need to be reduced to alleviate traffic congestion and its side effects. Encouraging drivers to choose other modes of transportation or to telework will reduce the number of passenger car commuters and is perceived to have positive influence on reducing traffic congestion and its side effects (Anderson, 2014; Litman, 2013; Sælensminde, 2004; Buekers et al., 2015). In this paper, we refer to walking and using transit systems as other “forms of commuting” and homeworking as an alternative to commuting. Transportation decision makers keep developing policies that encourage commuters to use transportation modes other than passenger cars. However, these non-passenger-car transportation forms may influence travelers’ physical and mental health, which are unknown and need to be quantified. Transportation systems, as a major component of the physical environment, play an integral role in public health (Lindström, 2008; Sælensminde, 2004; Yan et al. 2015; Tainio, 2015). In fact, five determinants of public health include n

Corresponding author. E-mail addresses: [email protected] (M. Tajalli), [email protected] (A. Hajbabaie).

http://dx.doi.org/10.1016/j.jth.2016.12.007 2214-1405/& 2017 Elsevier Ltd All rights reserved.

Please cite this article as: Tajalli, M., Hajbabaie, A., On the relationships between commuting mode choice and public health. Journal of Transport & Health (2017), http://dx.doi.org/10.1016/j.jth.2016.12.007i

M. Tajalli, A. Hajbabaie / Journal of Transport & Health ∎ (∎∎∎∎) ∎∎∎–∎∎∎

2

physical environment, social environment, lifestyle and health behaviors, medical care, and genetics (McGovern et al., 2014). This paper studies potential associations between using different commuting forms and public health. In particular, we focus on private transportation, public transit, and active transportation as different forms of commuting and homeworking as an alternative, using a large sample of workers in New York City (NYC). To analyze the effects of various commuting forms on public health, a discrete choice model approach is used. Physical and mental health impacts of different commuting modes and homeworking are compared to those of private commuting mode. In the remainder of this paper, a review of relevant literature is presented, the dataset is described, and the methods to quantify the impacts of different commuting modes on employees’ health are detailed. Then, the discussion is followed by modeling results and concluding remarks.

2. Background 2.1. Passenger car effects on health Reducing car use and increasing walking, cycling, and using public transportation in metropolitan areas can increase physical activity of travelers and reduce air pollution (Rojas-Rueda et al., 2012; Sælensminde, 2004). The majority of the literature we reviewed indicated that car commuting reduced travelers’ physical activity level (Lachapelle and Frank, 2009; Samimi and Mohammadian, 2010). In fact, MacDonald et al. (2010) showed that spending more time in passenger cars is associated with a statistically significant increase in obesity. Frank et al. (2004) reported that obesity increases about 6% for each hour spent in a car per day, and Stokols et al. (1978) showed that traffic congestion increases drivers’ blood pressure. However, there is no consensus on the effects of car commuting on physical health, as some studies show car commuting has some positive health effects in comparison to other commuting modes. Williams et al. (2008) and Ellaway et al. (2003) showed that driving reduces physical stress and mortality rates significantly. There is no agreement among different studies on the effects of car commuting on travelers’ mental health either. Some studies have concluded that car commuting is more stressful and leads to a negative mood among drivers (Wener and Evans, 2011; Bellet et al., 1969; Ferenchak and Katirai, 2015; Gatersleben and Uzzell, 2007; Künn-Nelen, 2015; Rissel et al., 2014). They explain the results by reasoning that car drivers perceive their trip as more effortful and unpredictable than public transport commuters (Wener and Evans, 2011). On the other hand, Macintyre (2001) showed a significant association between car ownership and better mental health. In addition, it is shown that driving to work gives a positive feeling that individuals have control over their trip and they are more flexible than others (Anable and Gatersleben, 2005) and it provides a feeling of being more secure (Eriksson et al., 2013). 2.2. Public transportation effects on health There seems to be an agreement on the effects of using transit systems on physical health. Public transport users tend to be physically healthier than car commuters as they meet the recommended level of physical activity more often (RojasRueda et al., 2012; Lachapelle and Frank, 2009; Humphrey, 2005; Rundle et al., 2007; Liao et al., 2016; Sener et al., 2016). They walk significantly more than car commuters to reach transit stations (MacDonald et al., 2010; Humphrey, 2005; Wener and Evans, 2007). However, using public transport may harm older people physically due to non-collision injuries inside the vehicle (Kendrick et al., 2015). There are different opinions about the impacts of public transportation on mental health. Some studies suggest that using public transportation causes travelers to experience a lower level of stress because they do not experience traffic congestion especially when they use train or light rail transit (Wener and Evans, 2011; Evans et al., 2002). In addition, living near reliable public transport facilities helps people to be less isolated as they have a chance to communicate with their friends or relatives through these facilities (Boniface et al., 2015). In contrast, it is deduced that crowded public transportation services increase physiological stress of travelers (Gatersleben and Uzzell, 2007; Singer et al., 1974; Cox et al., 2006). 2.3. Active transportation effects on health Active commuters are defined as those who either walk or ride a bicycle to work. There is substantial evidence that active transportation leads to improvements in physical health. Active transportation is associated with a lower rate of being overweight or obese and meeting the recommended physical activity level (Edwards, 2008; Vuori and Oja, 1999; Merom et al., 2010; Oja et al., 1998; Kaczynski et al., 2012; de Geus et al., 2007; Dill, 2009; Scheepers et al., 2014; Liao et al., 2016). Rabl and de Nazelle (2012) indicated that active transportation helps reduce air pollution, which in turn contributes to reducing the risk of cancer (Litman, 2010). In addition, active transportation reduces the risk of cardiovascular diseases (Litman, 2010; Hamer and Chida, 2008; Genter et al., 2008; Scheepers et al., 2014). Schauder and Foley (2015) reported that active transportation has negative association with weight and cholesterol, but no significant impact on blood pressure and glycohemoglobin. On the mental health effects, walking and cycling are perceived to be more exciting than other modes of transportation and help commuters feel more relaxed (Scheepers et al., 2014). Furthermore, active transportation leads to higher life Please cite this article as: Tajalli, M., Hajbabaie, A., On the relationships between commuting mode choice and public health. Journal of Transport & Health (2017), http://dx.doi.org/10.1016/j.jth.2016.12.007i

M. Tajalli, A. Hajbabaie / Journal of Transport & Health ∎ (∎∎∎∎) ∎∎∎–∎∎∎

3

satisfaction (Morris, 2015) and is associated with lower rate of mental and emotional distress in NYC (Bloomberg MRTAF, 2009). 2.4. Teleworking effects on health The effects of teleworking on physical health is not studied as much as other forms of commuting. Lundberg and Lindfors (2002) showed that teleworkers have significantly lower blood pressure than typical workers. In addition, Tsunoda et al. (2015) showed that low frequency of travel with different modes of transportation is highly related to shortage of physical activity. On the other hand, Mann and Holdsworth (2003) showed that there is no significant difference between teleworkers and regular workers from physical health perspective. From the mental health perspective, only a few studies used standard criteria to measure differences between teleworkers and regular workers. For example, Mann and Holdsworth (2003) concluded that teleworkers are isolated. In addition, Hartig et al. (2007) showed that interferences between job duties and home activities bother teleworkers mentally. While Spinks (2002) confirms these results for home-based workers, Kamerade and Burchell (2004) find evidence that teleworkers are not isolated as they have more spare time to participate in social activities and find better mood. Although the results of different studies are not conclusive, the fact is that teleworkers have more flexibility in their work schedule and transportation mode and can avoid stressful environmental conditions such as traffic congestion and air pollution (Cohen et al., 1986). 2.5. Summary of the literature and contribution of this paper Characterizing the effects of different forms of commuting and teleworking on physical and mental health is necessary before encouraging employees to choose one. None of the previous studies have characterized such effects all together. In addition, previous studies did not compare the effects of homeworking to other modes of transportation. Our study aims at showing if there is a significant difference between physical and mental health of workers based on their commuting choice and finding which commuting form has the highest and lowest association with commuters’ health. This study uses a large dataset from New Yorkers, living in five boroughs of the city with totally different characteristics. We are confident that this large and randomly selected sample is suitable for making reasonable conclusions. Unlike previous studies, this study considers several confounding factors regarding demographic and socioeconomic characteristics of workers that could help with identifying the associations between transportation mode and health. Furthermore, previous studies only considered stress as an indicator of mental health condition. We expand upon this by including a number of additional standard mental health indicators in the study.

3. Objective The main objective of this study is to characterize the associations of commuting mode choice, including homeworking, with workers’ physical and mental health. As such, the findings of this study will help understand whether replacing car commuting with other commuting forms as well as not commuting is an appropriate policy from workers’ health perspective. The focus of this research is on the workers in New York City (NYC) in US. The impacts of different transportation modes on physical and mental health are studied separately. Obesity, blood pressure, and diabetes are used as physical health indicators and Non-Specific Psychological Distress (NSPD) is used as the main indicator of mental health.

4. Dataset The 2010 NYC Community Health Survey (CHS) includes questions on employees’ commuting form that are used to quantify commuting form association with physical and mental health in this study. The 2010 NYC CHS is a self-reported, random, and cross-sectional survey conducted by phone among NYC adults with at least 18 years of age. The sample size was 8665 with a response rate of 34.8% (Community Health Survey, 2010). The survey is sampled within five boroughs including 34 neighborhoods and the questionnaire is mostly based on the national Behavioral Risk Factor Surveillance System. NYC CHS data were adjusted by weighting the observations to address unequal selection probabilities and nonresponding cases (Community Health Survey, 2010). In the final dataset, about 2650 respondents were employed with salary, while the rest were either self-employed, unemployed, homemakers, students, retired, or unable to work. In this study, the public transport mode breaks down into subway and city bus because the characteristic of their users are significantly different (Bodea et al., 2009). The private transportation mode is defined as using passenger cars only, as the number of taxi users was negligible. In addition, active transportation mode is defined as walking as the dataset includes only a few bicycle commuters. There are also a considerable number of respondents who work at home or telework, and hence, do not commute. Approximately 27% of the respondents commute by private vehicles, 30% use subway and 9% use city bus, 11% walk to work, and 23% stay at home during the regular work hours. It should be noted that about 8% of respondents use more than one modes of transportation and are excluded from the analysis. Please cite this article as: Tajalli, M., Hajbabaie, A., On the relationships between commuting mode choice and public health. Journal of Transport & Health (2017), http://dx.doi.org/10.1016/j.jth.2016.12.007i

M. Tajalli, A. Hajbabaie / Journal of Transport & Health ∎ (∎∎∎∎) ∎∎∎–∎∎∎

4

The following demographic and socioeconomic characteristics were included in the analysis to account for potential relationships between the personal characteristics and health: gender, age, marital status, income level, neighborhood poverty, having children, household size, education level, physical activity level, overall diet condition, consuming vegetables or fruits in daily diet, average number of soda drinks per day, drinking alcohol, and smoking tobacco. These variables are believed to be important and have some effects on personal well-being, and a number of previous studies have considered some of them in their analysis as well (Bopp et al., 2013; Wener and Evans, 2011; MacDonald et al., 2010; Bodea et al., 2009). Three questions were presented to respondents to evaluate their physical health, as follows: 1. What is your weight and height? (to measure Body Mass Index (BMI)) 2. “Have you ever been told by a doctor, nurse, or other health professionals that you have hypertension, also called high blood pressure? 3. Have you ever been told you have diabetes?” If the BMI is greater than 30, the respondent is marked as obese. For measuring mental health condition of respondents, the following six questions are used: 1. 2. 3. 4. 5. 6.

“How often do you feel sad? How often do you feel nervous? How often do you feel restless? How often do you feel hopeless? How often do you feel everything is an effort? How often do you feel worthless?”

Respondents rated these questions using a five-point scale. The Non-Specific Psychological Distress (NSPD) variable is scored using the unweighted sum of responses to the six mental health questions, where the responses of “none of the time” take score of zero and the responses of “all of the time being” yield a score of four. Thus, the range of responses is from 0 to 24. An NSPD score of 12–24 indicates that the respondent is at the risk of mental disorders (Kessler et al., 2002). In this research, the six questions were considered in combination to each other as the NSPD variable. Table 1 provides a brief explanation of demographic and socioeconomic characteristics of respondents in addition to transportation and health related variables used in this study.

5. Methods As shown in Table 1, we considered three binary variables for physical health (i.e., obesity, blood pressure, and diabetes) and a binary variable for mental health (i.e., NSPD). Two statistical procedures were used to study the associations of respondents’ commuting mode choice with health. In the first step, we established if there was any statistically significant difference between respondents’ health based on their commuting mode choice. In the second step, we quantified the association of each commuting mode choice (including homeworking) with physical and mental health of respondents using a discrete choice modeling approach. The χ-square test of independence and Analysis of Variance (ANOVA) were used to determine statistically significant differences between different commuting groups in terms of physical and mental health. The χ-square test of independence is applicable to categorical variables to evaluate how likely they are the same, and is appropriate for independent data in large samples (Chernoff and Lehmann, 1954). The null hypothesis is that the proportions of one variable in a group are the same as those in other groups. For continuous variables, such as age, the ANOVA test measures the level of significance of the mean differences between these five commuting groups. We also used a discrete choice modeling approach to study how a commuting mode was associated with health and whether or not that association was statistically significant. According to Stokes (1997), when dependent variable is binary (as was the case on our dataset), binary choice model is the most appropriate approach rather than linear regression. Logit and probit models are two widely used discrete choice models for binary outcomes. Probit analysis is an alternative to logit modelling approach, and the main difference is assuming normal distribution for random variables (Klieštik et al., 2015). In binary response models, the estimates of coefficients in a logit model are approximately π / √ 3 times larger than those of the probit model. However, the results of the choice probabilities would be the same (Scott Long, 1997). In this study, we utilized a binary probit model. Since demographic and socioeconomic variables (e.g., age, household income, diet, activity level, smoking, and educational level) were perceived to influence a respondent's physical and mental health conditions, they were included in the models. In addition, commuting choice and duration variables were added to models. Using public transportation including subway and city bus, walking, and homeworking were represented in models with four dummy variables, which took a value of one if the corresponding mode was selected. As a result, when private commuting mode was selected, these four dummy variables took on a value of zero. Thus, private transportation was the comparison base in probit models.

Please cite this article as: Tajalli, M., Hajbabaie, A., On the relationships between commuting mode choice and public health. Journal of Transport & Health (2017), http://dx.doi.org/10.1016/j.jth.2016.12.007i

M. Tajalli, A. Hajbabaie / Journal of Transport & Health ∎ (∎∎∎∎) ∎∎∎–∎∎∎

5

Table 1 Characteristics of participants included in the study. Variables

Description

Demographic and Socioeconomic Gender 0: male/1: female Age person's age in years Marital status 0: single/1: otherwise Child number of children Household Size size of the household Education level 1: less than high school/ 2: high school graduate/ 3: college or technical school/ 4: college graduate Income level household annual income from all sources by poverty level (5 thresholds: 1: o 100%/ 2: 100% o 200%/ 3: 200%- o400%/ 4: 400% - o 600% / 5: 4600% poverty level) Neighborhood poverty 1: low income/ 2: medium income/ 3: high income Activity level 1: not active at all/ 2: not very active/ 3: somewhat active/ 4: very active Exercise 1: active aside regular job/ 0: otherwise Overall diet healthy diet (1: poor/ 2: fair/ 3: good/ 4: very good/ 5: Excellent) Nutrition average number of servings of fruits/ vegetables per day Average soda average number of sugar sweetened sodas drinks per day Smoking 1: if the individual smokes currently or in the past/ 0: otherwise Drinking 1: if the individual drinks alcoholic beverage at least once in last 30 days/ 0: otherwise Transportation-Related Private Commuting 1: if person use car for travelling to work/ 0: otherwise Subway 1: if person use subway for travelling to work/ 0: otherwise City bus 1: if person use city bus for travelling to work/ 0: otherwise Active Commuting 1: if person walk for travelling to work/ 0: otherwise Homeworking 1: if person stays at home for work/ 0: otherwise Commute time one way commuting time in minutes (except homeworking) Health Obesity Blood pressure Diabetes NSPDn n

1: 1: 1: 1:

if BMI (kg/m2) ¼ weight/height2 4 30 / 0: otherwise if been told by a doctor, nurse, other health prof that have hypertension/ 0: otherwise if been told that have diabetes/ 0: otherwise person have mental issues/ 0: otherwise

Mean Standard deviation

0.55 46.52 0.44 0.75 2.67 3.14

0.50 13.44 0.50 1.04 1.57 1.01

3.43

1.42

2.00 3.06 0.77 3.34 2.65 1.04 0.39 0.15

0.79 0.81 0.42 1.02 2.12 1.16 0.49 0.36

0.27 0.30 0.09 0.11 0.23 25.46

0.44 0.46 0.28 0.31 0.42 23.33

0.23

0.42

0.26 0.07 0.03

0.44 0.26 0.17

NSPD: Non-Specific Psychological Distress

The backward elimination method was used to decide which variables should be included the models. Note that, all commuting modes were kept in the models to measure their association with health outcomes. For developing the model, the Akaike information criterion, the McFadden measure (Likelihood Ratio Index), and the χ-square value were used as fitness measures to compare the developed discrete choice models and select the best one. In addition, using the Wald test, the effect of four transportation-related variables on health models was examined. This test indicates whether or not transportation-related variables significantly improve health indicator prediction. It should be noted that before developing the models, we constructed variance-covariance matrices for all independent variables and also performed the Variance Inflation Factor (VIF) test to check for multicollinearity between independent variables. Marginal effect analysis for the transportation-related variables in the probit model was performed to provide a better understanding of the relationship between dependent variables (health outcomes) and transportation-related variables. For continuous variables, f ( β′x ) β was considered as the vector of marginal effects in binary choice models (Greene, 2002). In fact, a scalar f ( β′x ) was multiplied by the coefficient vector (β ) at any observation. To measure the density function f ( β′x ), the vector of mean of the observations was calculated. For dummy variables, our method to compute the marginal effect was to capture the effects of them on health when the value of variable changes from 0 to 1 (from private commuting to other modes). Eq. 1 shows the details of this method (Greene, 2002).

∆Fz =Prob ⎡⎣ y=1 | z=1⎤⎦−Prob ⎡⎣ y=1 | z=0⎤⎦ =F ⎡⎣ β′x+αz | z=1⎤⎦−F ⎡⎣ β′x+αz | z=0⎤⎦ =F ( β′x + α)−F ( β′x)

(1)

where: ∆Fz: probability density function, which is a function of x and z , y: utility function, z: dummy variable, β′: coefficient vector, x: vector of other variables, and α: coefficient of dummy variable. Please cite this article as: Tajalli, M., Hajbabaie, A., On the relationships between commuting mode choice and public health. Journal of Transport & Health (2017), http://dx.doi.org/10.1016/j.jth.2016.12.007i

M. Tajalli, A. Hajbabaie / Journal of Transport & Health ∎ (∎∎∎∎) ∎∎∎–∎∎∎

6

Table 2 Variable mean value for each commuting mode and significance level for χ-square and ANOVA tests. Total Sample (N ¼ 2650) Variables

Car (n¼ 721; 27%)

Subway (n¼ 802; 30%)

City Bus (n¼ 225; 9%)

Walking (n¼ 289; 11%)

Homeworking (n ¼ 611; 23%)

P-value

Demographic and socioeconomic Gender Agen Marital status Childn Household Sizen Education level Income level Neighborhood poverty Activity level Exercise Overall diet Nutritionn Average sodan Smoking Drinking

0.50 46.52 0.56 0.84 2.98 3.27 3.70 2.12 3.00 0.77 3.30 2.59 1.03 0.42 0.13

0.54 44.04 0.42 0.67 2.54 3.27 3.58 1.90 3.08 0.79 3.41 2.69 1.03 0.35 0.18

0.78 48.18 0.29 0.65 2.30 2.77 2.88 1.93 3.01 0.65 3.12 2.18 1.33 0.38 0.12

0.57 46.93 0.39 0.66 2.54 3.09 3.35 2.05 3.15 0.78 3.32 2.83 0.98 0.36 0.14

0.53 48.98 0.41 0.84 2.70 2.99 3.12 2.00 3.10 0.77 3.40 2.77 1.01 0.43 0.14

0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.172 0.000 0.002 0.003 0.003 0.012 0.026

Health Obesity Blood pressure Diabetes NSPD

0.25 0.26 0.09 0.02

0.18 0.21 0.05 0.02

0.37 0.35 0.11 0.03

0.19 0.23 0.04 0.01

0.23 0.28 0.08 0.06

0.000 0.000 0.001 0.000

Transportation Commuting durationn

26.3

42.2

40.8

16.6

–

0.0

n

ANOVA test is used

6. Results The mean values and the results of χ-square tests of independence and ANOVA are shown in Table 2. Each row of the table corresponds to a certain independent variable. A small p-value in front of each row indicates that the probability of no statistically significant difference between various commuting choices is small. In other words, it is expected that at least one of commuting options is statistically different than the rest in terms of the corresponding independent variable. Test results show that there are significant differences between various commuting choices in terms of all demographic and socioeconomic variables except for the activity level (P-value ¼ 0.172). The table also indicates that commuters who use private vehicles and subway are associated with higher educational level and household incomes. Female respondents are linked to higher probability to choose public transportation mode, specifically the city bus. The same is true for respondents who are single. A larger household size is related to using private car or staying at home for working. Also, the individuals who use subway are related to having more chance to exercise. Respondents with higher age are associated with higher probability of using city bus or skipping their commuting trip and working at home. In addition, lower education and income level is linked to an increase in the probability of using city bus. Finally, private commuting mode users as well as homeworkers are associate with more tobacco use in NYC. These results confirm that commuting behaviors are highly influenced by socioeconomic factors as shown by Clark et al. (2016). Although these comparisons provide important insights on different commuting patterns from employees’ health perspective, they do not quantify the extent of the linkage between commuting mode choice and well-being. The results of binary probit models provide the extent of such linkages as presented in Table 3. It is noted that constructing variancecovariance matrices for all independent variables and performing VIF test confirms no multicollinearity among independent variables that are considered in different models. Table 3, summarizes the findings for four models and the value of coefficients and significance level (p-value) are shown in two different columns. Obesity, blood pressure, diabetes, and NSPD are four dependent variables while, demographic and socioeconomic variables along with the commuting mode choice variable are independent variables. Also the log-likelihood (for fitted models), restricted log-likelihood (log-likelihood for models with interception only), and McFadden pseudo R-squared estimations are shown in the table. The modeling results for demographic and socioeconomic variables support the findings of other studies (see Table 3). Female commuters are associated with higher probability of being obese; higher age is linked to a higher probability of physical health problems; being married is favorable from mental health perspective; higher education shows linkage with Please cite this article as: Tajalli, M., Hajbabaie, A., On the relationships between commuting mode choice and public health. Journal of Transport & Health (2017), http://dx.doi.org/10.1016/j.jth.2016.12.007i

M. Tajalli, A. Hajbabaie / Journal of Transport & Health ∎ (∎∎∎∎) ∎∎∎–∎∎∎

7

Table 3 Final binary probit model results for physical and mental health. Variables

Constant Gender Age Marital status Education level Income level Neighborhood poverty Activity level Overall diet Average soda Subway City bus Active commuting Homeworking Log-likelihood Restricted log-likelihood McFadden R square

Obesity

Blood pressure

Diabetes

NSPD

Coefficient

P-value

Coefficient

P-value

Coefficient

P-value

Coefficient

P-value

1.30 0.16 0.005

0.000 0.006 0.030

 1.49

0.000

 0.70

0.013

 0.40

0.119

0.04

0.000

0.03

0.000  0.38

0.002

 0.11

0.000

 0.08  0.05

0.012 0.045

 0.16

0.000  0.13

0.001

 0.13  0.27  0.28

0.001 0.000 0.000

0.035 0.001

0.003 0.051 0.039 0.356

0.002 0.050 0.000 0.000 0.014 0.808 0.004 0.158

 0.13  0.18

 0.23 0.21  0.22  0.07  1260.58  1418.73 0.11

 0.17  0.10  0.19  0.20  0.27  0.03  0.47  0.15  584.20  681.16 0.14

 0.20  0.21  0.54 0.27  310.30  348.34 0.11

0.204 0.322 0.040 0.051

 0.15

0.000

 0.09 0.07  0.17  0.07  1321.81  1504.84 0.12

0.211 0.497 0.097 0.385

physical healthiness; higher income or living in a better neighborhood is associated with better physical and mental health conditions; and higher activity level or having a good diet helps the employees to have healthier lifestyle. The modeling results for public transportation is in support of previous studies except for the association of city bus and obesity. Most of the previous studies find that the public transportation helps commuters be more active (Rojas-Rueda et al., 2012; Lachapelle and Frank, 2009; Humphrey, 2005; Rundle et al., 2007). We find the same results for subway, as it is associated with less probability of obesity and having diabetes. However, the city bus has positive association with obesity. Although these two modes are considered as a public transport mode, some significant differences in characteristics of their users are observed (see Table 2). The users of city bus were older than the users of other transportation modes. Also, they had lower income, lower level of exercise, poor diet, lower usage of vegetables, and more usage of soda. Even though most of these factors were not significant in the model, they yield city bus's association with higher probability of being obese. This was not the case for subway. In addition, city bus users walked less than subway users, as city bus had significantly more stops than the subway in NYC. In the case of active transportation, the results are in support of studies in the literature. Active transportation is associated with less probability of obesity, hypertension, and diabetes. As Gatersleben and Uzzell (2007), Morris (2015), and Rissel et al. (2014) showed, active transportation has significant linkage to healthier mental status. This may be due to shorter travel time for pedestrians. Based on Table 2, the average commuting time for active travelers is 16.6 min, which is the lowest among all modes. As Morris (2015) indicated, lower travel time in large cities is associated with more life satisfaction, while increasing the travel time is associated with poor health status (Oliveira et al., 2015). In large cities like NYC, commuters want to skip traffic congestion, so active transportation could be a very interesting means if the workplace is within a reasonable distance from commuters’ home. Our results indicate that homeworking is associated with mental health disorders and no linkage to physical health is observed. As Spinks (2002) and Konradt et al. (2000) have shown, homeworking is highly probable to isolate the employees from society and make them more mentally exhausted. Table 4 presents the results of performing Wald test for all physical and mental dependent variables. The Wald test is used to show whether or not adding transportation mode choice independent variables to models with only demographic and socioeconomic independent variables yielded a significant difference. Based on the findings, adding commuting mode variables yielded a significant improvement in predicting obesity, diabetes, and NSPD, as all these variables are associated with a P-value of less than 0.1 (see Table 4). While adding commuting mode choice variables do not improve the prediction of having high blood pressure, we still can observe their linkage to respondents’ health. Table 4 Wald test results. Model

χ-square

d.f.n

P-value

Obesity Blood pressure Diabetes NSPD

22.33 5.37 13.66 19.31

4 4 4 4

0.000 0.251 0.008 0.001

n

d.f.: Degree of freedom

Please cite this article as: Tajalli, M., Hajbabaie, A., On the relationships between commuting mode choice and public health. Journal of Transport & Health (2017), http://dx.doi.org/10.1016/j.jth.2016.12.007i

M. Tajalli, A. Hajbabaie / Journal of Transport & Health ∎ (∎∎∎∎) ∎∎∎–∎∎∎

8

Table 5 Marginal effect analysis. Model

Variable

Marginal effects (%)

P-value

Obesity

Subway City bus Active commuting Homeworking

 6.0 5.8  5.5  2.0

0.002 0.064 0.027 0.349

Blood pressure

Subway City bus Active commuting Homeworking

 9.4 7.3  16.9  6.9

0.211 0.497 0.097 0.385

Diabetes

Subway City bus Active commuting Homeworking

 3.0  0.4  4.4  1.7

0.008 0.805 0.000 0.139

NSPD

Subway City bus Active commuting Homeworking

 1.1  1.1  2.2 1.8

0.172 0.250 0.002 0.077

The results of marginal effects for health outcomes are presented in Table 5. The variables with P-value of less than 0.10 are considered significant. According to marginal effects of health factors, if a traveler changes the commuting mode from car to subway, the probability of being associated with obesity and having diabetes would reduce by 6% and 3%, respectively. Also if travelers change their transportation mode from car to city bus, the probability of association with obesity would increase by 5.8%. Moreover, changing commuting mode from car to walking is associated with the reduction of the probability of having obesity, hypertension, diabetes, and mental health disorders by 5.5%, 16.9%, 4.4%, and 2.2%, respectively. Changing commuting mode from car to homeworking leads to an increase in the probability of being associated with mental health disorders by 1.8%. While the results of choice models in Table 3 indicate the association between health and different commuting forms, the marginal effects showed how much respondents’ health will improve or deteriorate when the commuting form is changed from private to other forms.

7. Limitations This study used a cross-sectional data with self-reported responses. Self-reported weights and heights are often underand over-estimated, respectively. Therefore, the obesity measure (weight/height2) is underestimated (Shields et al., 2011). Additionally, there may be undiagnosed health issues such as hypertension or diabetes that are not accounted for. In addition, individuals with undiagnosed hypertension and diabetes may consider themselves healthy. Furthermore, the inferences we made show association rather than causality. It is noted that some of the highlighted associations may be reverse causality. For instance, we found that homeworking was associated with worse mental condition; however, could not verify if homeworking led to worse mental health condition or vice versa. Moreover, the employees with multiple modes of transportation were removed and only those with one dominant mode of commuting were considered. However, the health pattern could be different among the commuters with multiple modes of transportation. Also, we only took into account car, subway, city bus, walking, and homeworking. Other modes such as taxi, biking, and train could be as important as others. A dataset with enough sample size is required to consider all of these modes together. Based on different studies (e.g., Bloomberg MRTAF, 2009), the neighborhood and the built environment have association with health status of individuals. However, the dataset used in this study provided the approximated neighborhood of respondents based on the nearest hospital to their living place including 34 regions. In our initial tests, we developed a multilevel model based on the provided locations of employee's living place but we found no significant difference for health status of these neighborhoods. More accurate living and work location data will help develop more accurate models. Finally, the results of this study were limited to NYC and should not be generalized to other cities.

8. Disscusions This paper studies how commuting mode choice (i.e., private, transit, active, and homeworking) is associated with physical and mental health of workers in NYC. For this purpose, we used the data collected by the Community Health Survey in NYC in 2010. Obesity, blood pressure, and diabetes were used as indicators of respondents’ physical health. Feeling sad, nervous, restless, hopeless, and worthless, as well as feeling everything is an effort were considered by a variable called Non-specific Psychological Distress (NSPD). NSPD was used as indicators of respondents’ mental health. Please cite this article as: Tajalli, M., Hajbabaie, A., On the relationships between commuting mode choice and public health. Journal of Transport & Health (2017), http://dx.doi.org/10.1016/j.jth.2016.12.007i

M. Tajalli, A. Hajbabaie / Journal of Transport & Health ∎ (∎∎∎∎) ∎∎∎–∎∎∎

9

Models showed that choosing public transportation was associated with two extreme outcomes. The workers who used subway were associated with lower chance of being obese and having diabetes (95% significance level). However, employees who used city bus were associated with higher obesity chance. There was no statistically significant association between using city bus and blood pressure, diabetes, and mental health disorders. The results showed that walking was associated with lower probability of obesity, hypertension, and diabetes. Moreover, active commuting was associated with better mental health conditions. Active commuting showed significant linkage with all health indicators (95% confidence level) except for blood pressure. Homeworking was mostly associated with higher probability of mental health issues rather than physical health problems. Although homeworking had negative coefficients for all physical health variables (positive physical health effect), none of them were statistically significant.

9. Conclusions Findings of the probit models showed that commuting mode choices including car, subway, city bus, walking, and homeworking had different physical and mental health characteristics in terms of obesity, blood pressure, diabetes, and NSPD. Our findings also indicated that walking to work is associated with lower probability of being obese and having hypertension, diabetes, and mental health disorders (all statistically significant) when compared to using personal passenger cars. Using the subway is linked to lower probability of being obese and having diabetes when compared to using personal vehicles. However, city bus users are associated with higher chance of being obese in comparison with car users. Finally, when compared to using personal vehicles, homeworkers are associated with the higher probability of having mental disorders. Our findings showed that walking had the highest positive impact on blood pressure, diabetes, and NSPD compared to other modes of transportation. Subway had a higher effect on obesity. We found walking as the healthiest mode of transportation, which can be considered to replace private commuting whenever possible. Therefore, it is suggested to encourage commuters to walk more. Furthermore, commuting with subway is healthier than commuting with city bus and homeworking. For future studies, increasing the sample size will allow using multinomial modeling by creating more subcategories for different health variables. In addition, developing a time series model will be helpful for studying long term consequences of different commuting modes on commuter's health condition. Also, considering the effects of neighborhood, workplace location, and their built environment helps find more realistic results.

Refrences Anable, J., Gatersleben, B., 2005. All work and no play? The role of instrumental and affective factors in work and leisure journeys by different travel modes. Transp. Res. Part A Policy Pract. 39, 163–181, http://dx.doi.org/10.1016/j.tra.2004.09.008. Anderson, M.L., 2014. Subways, strikes, and slowdowns: the impacts of public transit on traffic congestion. Am. Econ. Rev. 104, 2763–2796, http://dx.doi. org/10.1257/aer.104.9.2763. Bellet, S., Roman, L., Kostis, J., 1969. The effect of automobile driving on catecholamine and adrenocortical excretion. Am. J. Cardiol. 24, 365–368, http://dx. doi.org/10.1016/0002–9149(69)90430-5. MRTAF, B., 2009. A report from the New York City community health survey NYC vital signs physical activity in New York city new. Yorkers Can Easily Integr. Exerc. into Their Dly. Routine Bloomberg. Bodea, T.D., Garrow, L.A., Meyer, M.D., Ross, C.L., 2009. Socio-demographic and built environment influences on the odds of being overweight or obese: the Atlanta experience. Transp. Res. Part A Policy Pract. 43, 430–444, http://dx.doi.org/10.1016/j.tra.2008.11.009. Boniface, S., Scantlebury, R., Watkins, S.J., Mindell, J.S., 2015. Health implications of transport: evidence of effects of transport on social interactions. J. Transp. Heal 2, 441–446, http://dx.doi.org/10.1016/j.jth.2015.05.005. Bopp, M., Kaczynski, A.T., Campbell, M.E., 2013. Health-related factors associated with mode of travel to work. J. Environ. Public Health 2013http://dx.doi. org/10.1155/2013/242383. Buekers, J., Dons, E., Elen, B., Panis, L.I., 2015. Health impact model for modal shift from car use to cycling or walking in Flanders: application to two bicycle highways. J. Transp. Heal. 2, 549–562. Chernoff, H., Lehmann, E.L., 1954. The use of maximum likelihood estimates in $/chi\widehat2$ tests for goodness of fit. Ann. Math. Stat. 25, 579–586, http: //dx.doi.org/10.1214/aoms/1177728726. Clark, B., Chatterjee, K., Melia, S., 2016. Changes to commute mode: the role of life events, spatial context and environmental attitude. Transp. Res. Part A Policy Pract. 89, 89–105, http://dx.doi.org/10.1016/j.tra.2016.05.005. Cohen, S., Evans, G.W., Stokols, D., Krantz, D.S., 1986. Behavior, health, and environmental stress. Behav. Heal. Environ. Stress http://dx.doi.org/10.1007/ 978-1-4757–9380-2. Community Health Survey, 2010. [WWW Document]. New York City Dep. Heal. Ment. Hyg. URL http://www1.nyc.gov/site/doh/data/data-sets/communityhealth-survey-public-use-data.page (accessed 1.1.12). Cox, T., Houdmont, J., Griffiths, A., 2006. Rail passenger crowding stress, health and safety in Britain. Transp. Res. Part A Policy Pract. 40, 244–258, http://dx. doi.org/10.1016/j.tra.2005.07.001. de Geus, B., De Smet, S., Nijs, J., Meeusen, R., 2007. Determining the intensity and energy expenditure during commuter cycling. Br. J. Sport. Med. 41, 8–12, http://dx.doi.org/10.1136/bjsm.2006.027615. Dill, J., 2009. Bicycling for transportation and health: the role of infrastructure. J. Public Health Policy 30 (Suppl 1), S95–S110, http://dx.doi.org/10.1057/ jphp.2008.56. Edwards, R.D., 2008. Public transit, obesity, and medical costs: assessing the magnitudes. Prev. Med. (Baltim.) 46, 14–21, http://dx.doi.org/10.1016/j. ypmed.2007.10.004. Ellaway, A., Macintyre, S., Hiscock, R., Kearns, A., 2003. In the driving seat: psychosocial benefits from private motor vehicle transport compared to public

Please cite this article as: Tajalli, M., Hajbabaie, A., On the relationships between commuting mode choice and public health. Journal of Transport & Health (2017), http://dx.doi.org/10.1016/j.jth.2016.12.007i

10

M. Tajalli, A. Hajbabaie / Journal of Transport & Health ∎ (∎∎∎∎) ∎∎∎–∎∎∎

transport. Transp. Res. Part F Traffic Psychol. Behav. 6, 217–231, http://dx.doi.org/10.1016/S1369-8478(03)00027-5. Eriksson, L., Friman, M., Gärling, T., 2013. Perceived attributes of bus and car mediating satisfaction with the work commute. Transp. Res. Part A Policy Pract. 47, 87–96, http://dx.doi.org/10.1016/j.tra.2012.10.028. Evans, G.W., Wener, R.E., Phillips, D., 2002. The morning rush hour: predictability and commuter stress. Environ. Behav. 34, 521–530, http://dx.doi.org/ 10.1177/00116502034004007. Ferenchak, N.N., Katirai, M., 2015. Commute mode and mental health in major metropolitan areas. Transp. Lett. 7, 92–103, http://dx.doi.org/10.1179/ 1942787514Y.0000000040. Frank, L.D., Andresen, M.A., Schmid, T.L., 2004. Obesity relationships with community design, physical activity, and time spent in cars. Am. J. Prev. Med. 27, 87–96, http://dx.doi.org/10.1016/j.amepre.2004.04.011. Gatersleben, B., Uzzell, D., 2007. Affective appraisals of the daily commute: comparing perceptions of drivers, cyclists, walkers, and users of public transport. Environ. Behav. 39, 416–431, http://dx.doi.org/10.1177/0013916506294032. Genter, J.A., Donovan, S., Petrenas, B., Badland, H., 2008. Valuing the health benefits of active transport modes. N. Z. Transp. agency Res. Rep. 359, 72. Greene, W.H., 2002. LIMDEP: Version 8.0: Reference Guide. Econometric Software. Hamer, M., Chida, Y., 2008. Active commuting and cardiovascular risk: a meta-analytic review. Prev. Med. (Baltim.) http://dx.doi.org/10.1016/j. ypmed.2007.03.006. Hartig, T., Kylin, C., Johansson, G., 2007. The telework tradeoff: stress mitigation vs. constrained restoration. Appl. Psychol. 56, 231–253, http://dx.doi.org/ 10.1111/j.1464-0597.2006.00252.x. Humphrey, N.P., 2005. Does the built environment influence physical activity? TR NEWS 237, 32. Kaczynski, A.T., Bopp, M.J., Wittman, P., 2012. To drive or not to drive: factors differentiating active versus non-active commuters. Heal. Behav. Public Heal 2, 14–19. Kamerade, D., Burchell, B., 2004. Teleworking and participatory capital: is teleworking an isolating or a community-friendly form of work? Eur. Sociol. Rev. http://dx.doi.org/10.1093/esr/jch030. Kendrick, D., Drummond, A., Logan, P., Barnes, J., Worthington, E., 2015. Systematic review of the epidemiology of non-collision injuries occurring to older people during use of public buses in high income countries. J. Transp. Health 2, 394–405, http://dx.doi.org/10.1016/j.jth.2015.06.002. Kessler, R.C., Andrews, G., Colpe, L.J., Hiripi, E., Mroczek, D.K., Normand, S.L.T., Walters, E.E., Zaslavsky, A.M., 2002. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol. Med. 32, 959–976, http://dx.doi.org/10.1017/S0033291702006074. Klieštik, T., Kočišová, K., Mišanková, M., 2015. Logit and probit model used for prediction of financial health of company. Procedia Econ. Financ 23, 850–855, http://dx.doi.org/10.1016/S2212-5671(15)00485-2. Konradt, U., Schmook, R., Wilm, A., Hertel, G., 2000. Health circles for teleworkers: selective results on stress, strain and coping styles. Health Educ. Res 15, 327–338, http://dx.doi.org/10.1093/her/15.3.327. Künn-Nelen, A., 2015. Does commuting affect health? Health Econ. 19, N./a–N./a http://dx.doi.org/10.1002/hec.3199. Lachapelle, U., Frank, L.D., 2009. Transit and health: mode of transport, employer-sponsored public transit pass programs, and physical activity. J. Public Health Policy 30 (Suppl 1), S73–S94, http://dx.doi.org/10.1057/jphp.2008.52. Liao, Y., Tsai, H.-H., Wang, H.-S., Lin, C.-P., Wu, M.-C., Chen, J.-F., 2016. Travel mode, transportation-related physical activity, and risk of overweight in Taiwanese adults. J. Transp. Heal. 3, 220–225. Lindström, M., 2008. Means of transportation to work and overweight and obesity. : A Popul.-based study South. Swed. 46, 22–28, http://dx.doi.org/ 10.1016/j.ypmed.2007.07.012. Litman, T., 2013. Evaluating Active Transport Benefits and Costs. Victoria Transport Policy Institute. Litman, T., 2010. Evaluating public transportation health benefits. Transportation. Long, J.S., 1997. Regression models for categorical and limited dependent variables. Am. J. Sociol. http://dx.doi.org/10.1086/231290. Lundberg, U., Lindfors, P., 2002. Psychophysiological reactions to telework in female and male white-collar workers. J. Occup. Health Psychol. 7, 354–364, http://dx.doi.org/10.1037/1076-8998.7.4.354. MacDonald, J.M., Stokes, R.J., Cohen, D.A., Kofner, A., Ridgeway, G.K., 2010. The effect of light rail transit on body mass index and physical activity. Am. J. Prev. Med. 39, 105–112, http://dx.doi.org/10.1016/j.amepre.2010.03.016. Macintyre, S., 2001. Housing tenure and car access: further exploration of the nature of their relations with health in a UK setting. J. Epidemiol. Community Health 55, 330–331, http://dx.doi.org/10.1136/jech.55.5.330. Mann, S., Holdsworth, L., 2003. The psychological impact of teleworking: stress, emotions and health. New Technol. Work Employ. 18, 196–211, http://dx. doi.org/10.1111/1468-005X.00121. McGovern, L., Miller, G., Hughes-Cromwick, P. 2014. The Relative Contribution of Multiple Determinants to Health Outcomes. Project HOPE. Merom, D., van der Ploeg, H.P., Corpuz, G., Bauman, A.E., 2010. Public health perspectives on household travel surveys active travel between 1997 and 2007. Am. J Prev. Med 39, 113–121, http://dx.doi.org/10.1016/j.amepre.2010.04.007. Morris, E.A., 2015. Should we all just stay home? Travel, out-of-home activities, and life satisfaction. Transp. Res. Part A Policy Pract. 78, 519–536, http://dx. doi.org/10.1016/j.tra.2015.06.009. Oja, P., Vuori, I., Paronen, O., 1998. Daily walking and cycling to work: their utility as health-enhancing physical activity. Patient Educ. Couns. http://dx.doi. org/10.1016/S0738-3991(98)00013-5. Oliveira, R., Moura, K., Viana, J., Tigre, R., Sampaio, B., 2015. Commute duration and health: empirical evidence from Brazil. Transp. Res. Part A Policy Pract. 80, 62–75, http://dx.doi.org/10.1016/j.tra.2015.07.020. Rabl, A., de Nazelle, A., 2012. Benefits of shift from car to active transport. Transp. Policy 19, 121–131, http://dx.doi.org/10.1016/j.tranpol.2011.09.008. Rissel, C., Petrunoff, N., Wen, L.M., Crane, M., 2014. Travel to work and self-reported stress: findings from a workplace survey in south west Sydney, Australia. J. Transp. Health 1, 50–53, http://dx.doi.org/10.1016/j.jth.2013.09.001. Rojas-Rueda, D., de Nazelle, A., Teixid??, O., Nieuwenhuijsen, M.J., 2012. Replacing car trips by increasing bike and public transport in the greater Barcelona metropolitan area: a health impact assessment study. Environ. Int. 49, 100–109, http://dx.doi.org/10.1016/j.envint.2012.08.009. Rundle, A., Diez Roux, A.V., Freeman, L.M., Miller, D., Neckerman, K.M., Weiss, C.C., 2007. The urban built environment and obesity in New York City: a multilevel analysis. Am. J. Health Promot 21, 326–334, http://dx.doi.org/10.4278/0890-1171-21.4s.326. Sælensminde, K., 2004. Cost – benefit analyses of walking and cycling track networks taking into account insecurity, health effects and external costs of motorized traffic. 38, 593–606, http://dx.doi.org/10.1016/j.tra.2004.04.003. Samimi, A., Mohammadian, A.K., 2010. Health impacts of urban development and transportation systems. J. Urban Plan. Dev. 136, 208–213, http://dx.doi. org/10.1061/(ASCE)UP.1943-5444.0000020. Schauder, S. a, Foley, M.C., 2015. The relationship between active transportation and health. J. Transp. Health 2, 1–7, http://dx.doi.org/10.1016/j. jth.2015.06.006. Scheepers, C.E., Wendel-Vos, G.C.W., den Broeder, J.M., van Kempen, E.E.M.M., van Wesemael, P.J.V., Schuit, A.J., 2014. Shifting from car to active transport: a systematic review of the effectiveness of interventions. Transp. Res. Part A Policy Pract. 70, 264–280, http://dx.doi.org/10.1016/j.tra.2014.10.015. Schrank, D., Eisele, B., Lomax. T., Bak, J. 2015. Urban Mobility Scorecard. Texas A&M Transp. Institute. 39;5. Sener, I.N., Lee, R.J., Elgart, Z., 2016. Potential health implications and health cost reductions of transit-induced physical activity. J. Transp. Heal. 3, 133–140. Shields, M., Gorber, S.C., Janssen, I., Tremblay, M.S., Shields, M., Gorber, S.C., Janssen, I., Tremblay, M.S. 2011. Correction equations for adults Bias in selfreported estimates of obesity in Canadian health surveys: an update on correction equations for adults. Singer, J.E., Lundberg, U., Frankenhaeuser, M., 1974. Stress on the Train: A Study of Urban Commuting. Psychological Laboratories, University of Stockholm.

Please cite this article as: Tajalli, M., Hajbabaie, A., On the relationships between commuting mode choice and public health. Journal of Transport & Health (2017), http://dx.doi.org/10.1016/j.jth.2016.12.007i

M. Tajalli, A. Hajbabaie / Journal of Transport & Health ∎ (∎∎∎∎) ∎∎∎–∎∎∎

11

Spinks, W.A., 2002. A survey of home-based workers in Japan: emerging health issues. J. Occup. Health 44, 248–253, http://dx.doi.org/10.1539/joh.44.248. Stokes, H.H., 1997. Specifying and Diagnostically Testing Econometric Models. Greenwood Publishing Group. Stokols, D., Novaco, R.W., Stokols, J., Campbell, J., 1978. Traffic congestion, type A behavior, and stress. J. Appl. Psychol. 63, 467–480, http://dx.doi.org/ 10.1037/0021-9010.63.4.467. Tainio, M., 2015. Burden of disease caused by local transport in Warsaw. Pol. J. Transp. Health 2, 423–433, http://dx.doi.org/10.1016/j.jth.2015.06.005. Tsunoda, K., Kitano, N., Kai, Y., Tsuji, T., Soma, Y., Jindo, T., Yoon, J., Okura, T., 2015. Transportation mode usage and physical, mental and social functions in older Japanese adults. J. Transp. Health 2, 44–49, http://dx.doi.org/10.1016/j.jth.2014.10.003. Vuori, I., Oja, P., 1999. The health potential of physical activity through transport by walking and cycling. A Sci. Rev. Prep. Chart. Transp. Environ. Health. Wener, R.E., Evans, G.W., 2011. Comparing stress of car and train commuters. Transp. Res. Part F Traffic Psychol. Behav. 14, 111–116, http://dx.doi.org/ 10.1016/j.trf.2010.11.008. Wener, R.E., Evans, G.W., 2007. A morning stroll. Environ. Behav. http://dx.doi.org/10.1177/0013916506295571. Williams, G.A., Murphy, J., Hill, R., 2008. A latent class analysis of commuters ’ transportation mode and relationships with commuter stress commuting – its impact on well-being. Control. Yan, C.-Y., Jiang, R., Gao, Z.-Y., Shao, H., 2015. Effect of speed limits in degradable transport networks. Transp. Res. Part C Emerg. Technol. 56, 94–119. http:// dx.doi.org/10.1016/j.trc.2015.03.042.

Please cite this article as: Tajalli, M., Hajbabaie, A., On the relationships between commuting mode choice and public health. Journal of Transport & Health (2017), http://dx.doi.org/10.1016/j.jth.2016.12.007i