Exploring the nonlinear effects of built environment characteristics on customized bus service
mode - bus, mode - demand responsive transit, place - asia, land use - impacts, land use - urban density, ridership - behaviour
Machine learning, Demand-responsive transit, Built environment, Travel behavior, Gradient boosting
The high failure rate of demand-responsive transit (DRT) systems suggests that DRT services are viable only in selected areas. However, few studies have quantitatively examined how built environment characteristics affect DRT use. Applying gradient boosting decision trees to the data of customized bus service (CBS, a type of DRT) in Dalian, we investigate the nonlinear association between the built environment and CBS use, controlling for demographics and service features. Local accessibility at the residence and workplace are the most important correlates of CBS use, followed by the proximity of workplace to bus stop. Some built environment variables (including distance from workplace to transit stops, distance from residence to business centers, and population density) influence CBS use differently from traditional transit use observed in the literature. Furthermore, built environment variables show salient threshold associations with CBS use, guiding planners to design the CBS system efficiently.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Wang, J., Cao, X., Liu, K., & Wang, D. (2023). Exploring the nonlinear effects of built environment characteristics on customized bus service. Transportation Research Part D: Transport and Environment, Vol. 114, 103523.
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