With rapidly increasing urbanization and motorization in China, the effect of commuting on residents’ subjective well-being (SWB) is likely growing. We used 13,261 individual, 124 city, and 401 neighbourhood samples from the 2014 China Labour-Force Dynamics Survey (CLDS 2014) and applied multilevel mixed-effects ordered probit regressions to investigate the relationship between commuting and SWB. We found huge differences between urban and rural areas in relation to commuting. Urban respondents’ daily average commuting time was 0.56 h while rural respondents’ daily average commuting time was 0.41 h. Further, the daily average commute time for residents living in cities with high urbanization rates (> 70%) was longer than for those living in cities with low urbanization rates (< 70%). The subjective well-being of residents who commute by walking or cycling was significantly lower than that of those who commute by other transportation modes. The regression results indicated that the longer the commute time, the lower the subjective well-being. Among residents who live in rural areas or cities with low urbanization, subjective well-being was more easily affected by commuting time. Commuting time was also found to influence residents’ job satisfaction and family life satisfaction, which in turn influence SWB. China’s current development mode ignores the traffic needs of vulnerable groups. Therefore, future traffic construction should increase its prioritization of these vulnerable transportation groups.
A growing base of research adopts direct demand models to reveal associations between transit ridership and influence factors in recent years. This study is designed to investigate the factors affecting rail transit ridership at both station level and station-to-station level by adopting multiple regression model and multiplicative model respectively, specifically using an implemented Metro system in Nanjing, China, where Metro implementation is on the rise. Independent variables include factors measuring land-use mix, intermodal connection, station context, and travel impedance. Multiple regression model proves 11 variables are significantly associated with Metro ridership at station level: population, employment, business/office floor area, CBD dummy variable, number of major educational sites, entertainment venues and shopping centers, road length, feeder bus lines, bicycle park-and-ride (P&R) spaces, and transfer dummy variable. Results from multiplicative model indicate that factors influencing Metro station ridership may also influence Metro station-to-station ridership, varied by both trip ends (origin/destination) and time of day. In comparison with previous case studies, CBD dummy variable and bicycle P&R are statistically significant to explain Metro ridership in Nanjing. In addition, Metro travel impedance variables have significant influence on station-to-station ridership, representing the basic time-decay relationship in travel distribution. Potential implications of the model results include estimating Metro ridership at station level and station-to-station level by considering the significant variables, recognizing the necessity to establish a cooperative multi-modal transit system, and identifying opportunities for transit-oriented development. 相似文献
