“…Yazdanpanah and Hosseinlou (2017) also found that low BMI scores exhibited links to a greater amount of intended public transport use. Another study reported that using the subway to commute was related to a reduced probability of obesity and diabetes compared to using personal vehicles (Tajalli & Hajbabaie, 2017).…”
Using data from the Houston Travel-Related Activity in Neighborhoods (TRAIN) study, this study examined how various factors affect whether individuals intend to use newly opened light-rail transit (LRT) lines in Houston. The Houston TRAIN study is a natural experiment on the effect of new LRT lines on both transit use and physical activity. A mixed binary logit model was developed based on a dichotomous dependent variable and rich set of variables, including sociodemographic factors, health status, travel behavior and technology, and attitudes and perceptions. The mixed model also allowed accounting for the unobserved heterogeneity across individuals in their sensitivity to observed variables. The results indicated the important role of various factors influencing the decision on intent to use the new LRT lines. In general, demographics mattered but to a lower extent than psychological or personality-related variables. For example, attitudes and perceptions toward the public transit system and consciousness of physical activities derived by using public transit were important factors. Personal health constraints negatively influenced intention to use, while experience with the public transport system was among the positive indicators. The findings show the potential of future interventions in this community to promote use of the new system, such as educational campaigns that improve perceptions of public transit use and clarify the benefits of being active. While providing growing evidence that cognitive variables are important in measuring intention to use public transit, the
“…Yazdanpanah and Hosseinlou (2017) also found that low BMI scores exhibited links to a greater amount of intended public transport use. Another study reported that using the subway to commute was related to a reduced probability of obesity and diabetes compared to using personal vehicles (Tajalli & Hajbabaie, 2017).…”
Using data from the Houston Travel-Related Activity in Neighborhoods (TRAIN) study, this study examined how various factors affect whether individuals intend to use newly opened light-rail transit (LRT) lines in Houston. The Houston TRAIN study is a natural experiment on the effect of new LRT lines on both transit use and physical activity. A mixed binary logit model was developed based on a dichotomous dependent variable and rich set of variables, including sociodemographic factors, health status, travel behavior and technology, and attitudes and perceptions. The mixed model also allowed accounting for the unobserved heterogeneity across individuals in their sensitivity to observed variables. The results indicated the important role of various factors influencing the decision on intent to use the new LRT lines. In general, demographics mattered but to a lower extent than psychological or personality-related variables. For example, attitudes and perceptions toward the public transit system and consciousness of physical activities derived by using public transit were important factors. Personal health constraints negatively influenced intention to use, while experience with the public transport system was among the positive indicators. The findings show the potential of future interventions in this community to promote use of the new system, such as educational campaigns that improve perceptions of public transit use and clarify the benefits of being active. While providing growing evidence that cognitive variables are important in measuring intention to use public transit, the
“…[18][19][20][21][22][23][24] Ongoing research aims to understand the reasons why urban sprawl is correlated with obesity. Data suggest that longer times spent travelling to work are negatively associated with healthy behaviour in general, [25][26][27][28][29][30] possibly because of a trade-off between the time spent travelling and the time spent on healthy behaviour (eg cooking whole food and exercising). 29 This is supported by a cohort study from Adelaide showing that the distance of one's residence from the city centre was associated with abdominal circumference.…”
Section: 'Obesogenic Environments': What Is the Evidence?mentioning
“…In this section, we used the Wald test to examine whether or not adding car ownership and commuting variables into the models can significantly improve the prediction of individual SWB [49]. Table 6 presents the results of Wald test for all cognitive and affective SWB variables.…”
How and to what extent household car ownership and commuting behavior affect individual subjective well-being (SWB) is of great interest for urban and transportation planning. Increasing attention has been paid to the associations between car ownership, commuting and SWB. However, only a limited number of studies examined the effects of travel-related factors on both cognitive and affective SWB aspects. This research empirically investigated the relationships from the two SWB aspects. Furthermore, we extend the modeling of generic cognitive SWB to several specific measures (e.g., satisfaction with life compared to a specific group of people, degree of free choice, social position, and social equality) to explore how car ownership and commuting behavior contribute to individual SWB. Drawing on the data derived from the 2014 China Labor-Force Dynamics Survey, a set of ordered probit models based on Bayesian inference are estimated. The findings point out that household car ownership has a significant effect on cognitive SWB but a limited influence on affective SWB. It appears that commuting time is significantly and negatively associated with individuals’ cognitive and affective well-being, whereas a positive correlation is found between the commuting by bicycle and affective SWB. The effects of commuting time and transportation modes on different measured satisfactions with life have no big differences. Finally, results of the Wald tests indicate that incorporating household car ownership and commuting behavior into the modeling framework can significantly improve the prediction accuracy of individual SWB.
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