Bike sharing systems have been established in several cities across North America. An objective of all bike sharing programs is to maximize the number of trips to and from bike share stations. The purpose of this research is to identify correlates of bike station activity, with special emphases on the association of trips to and from bike stations with the number of nearby businesses and jobs. Using data on 2011 trips from Nice Ride stations in Minneapolis-St. Paul, we introduce three ordinary least square regression models to evaluate the marginal effects of the presence of businesses on annual total station trips, trip origins and trip destinations. Our models include 19 variables in four general categories, including, in addition to the presence of different types of businesses and jobs, sociodemographic, built environment, and transportation infrastructure variables that are used as controls. Our result shows the number of trips at Nice Ride stations is positively and significantly associated with food-related destinations near the station and job accessibility but not with general retail establishments. Use of bike share stations also is correlated with race, age, proximity to the central business district, proximity to water, accessibility to trails, and distance to other bike share stations. This research is important for planners, academics, and policymakers because the findings will facilitate the understanding of bike share operations, help planners locate new stations, evaluate the potential of implementing new bike share programs, assess economic activity associated with bike share trips, and minimize costs of operations.
IntroductionAlthough travel behavior is expected to influence personal health, few studies have examined associations with mental health. This study examines associations between commute patterns and mental health using survey data in 11 Latin American cities.MethodsUsing a survey conducted by the Development Bank of Latin America in 2016, we measured the presence of depressive symptoms using the 10-item Center for Epidemiologic Studies Depression (CESD-10) screening scale. We used multilevel non-linear models to estimate the magnitude of the associations between commute patterns and depression risk, adjusting for socio-demographic and neighborhood characteristics.ResultsWe found that, on average, every 10 more minutes of commuting time is associated with 0.5% (p = 0.011) higher probability of screening positively for depression. Furthermore, when decomposing commuting time into free-flow time and delay time, we found that delay and not free-flow time, were associated with depression. Specifically, every 10 additional minutes of traffic delay is associated with 0.8% (p = 0.037) higher probability of screening positively for depression. When examining differences by travel mode, we find that users of formal transit (e.g. subway or bus rapid transit) are 4.8% (p = 0.040) less likely to be screened positively for depression than drivers. In addition, not having transit stops within a 10-min walk from home is associated with higher probability of screening positively for depression.ConclusionsOur findings provide preliminary evidence that better access to mass transit systems and less congestion may be linked to better mental health among urban residents.
Although residential crowding has many well-being implications, its connection to mental health is yet to be widely examined. Using survey data from 1613 residents in Beijing, China, we find that living in a crowded place – measured by both square metres per person and persons per bedroom – is significantly associated with a higher risk of depression. We test for the mechanisms of such associations and find that the residential crowding–depression link arises through increased living space-specific stress rather than increased life stress. We also identify the following subgroups that have relatively stronger residential crowding–depression associations: females, those living with children, those not living with parents, and those living in non-market housing units. Our findings show that inequality in living space among urban residents not only is an important social justice issue but also has health implications.
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