Description

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.

Publication Date

2-2021

Publication Type

Report

Topic

Transit and Passenger Rail

Digital Object Identifier

10.31979/mti.2021.1920

MTI Project

1920

Mineta Transportation Institute URL

https://transweb.sjsu.edu/research/1920-Mutlimodal-Safety

Keywords

Active transportation, transit station, traffic safety, built environment, correlation analysis

Disciplines

Transportation

Additional Files
1920-Zhang-RB-Mutlimodal-Safety.pdf (1443 kB)
Research Brief
Download

Additional files available below

Share

COinS