Exploring the nonlinear effects of built environment characteristics on customized bus service

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.

Automated summary

The high failure rate of demand-responsive transit (DRT) systems indicates that DRT services are only feasible in selected areas. However, there have been few studies that quantitatively examine the impact of built environment characteristics on DRT use. This study uses gradient boosting decision trees to analyze the data of customized bus service (CBS, a type of DRT) in Dalian, and investigates the nonlinear relationship between the built environment and CBS use while controlling for demographics and service features. The study finds that local accessibility at the residence and workplace are the most important factors influencing CBS use, followed by the proximity of the workplace to bus stops. Some built environment variables have different impacts on CBS use compared to traditional transit observed in the literature. Additionally, the study identifies threshold associations between built environment variables and CBS use, providing guidance for efficient CBS system design.