Numerous extant studies are dedicated to enhancing the safety of active transportation modes, but very few studies are devoted to safety analysis surrounding transit stations, which serve as an important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditionally autoregressive (MCAR) priors with a distance-oriented neighboring weight matrix. For this purpose, transit-station-centered data in Los Angeles County were used for model development. Feature selection relying on both random forest and correlation analyses was employed, which leads to different covariate inputs to each of the two jointed models, resulting in increased model flexibility. Utilizing an Integrated Nested Laplace Approximation (INLA) algorithm and various evaluation criteria, the results demonstrate that models with a correlation effect between pedestrians and bicyclists perform much better than the models without such an effect. The joint models also aid in identifying significant covariates contributing to the safety of each of the two active transportation modes. The research results can furnish transportation professionals with additional insights to create safer access to transit and thus promote active transportation.
Bicycle and Pedestrian Issues
Digital Object Identifier
Mineta Transportation Institute URL
Active transportation, transit station, traffic safety, built environment, correlation analysis
Yongping Zhang, Wen Cheng, and Xudong Jia. "Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas" Mineta Transportation Institute Publications (2021). https://doi.org/10.31979/mti.2021.1920