01664164

Component

Enhancement of safety for all transportation mode users plays an essential role in the implementation of multimodal transportation systems. Compared with crash prediction models dedicated to motorized mode users, the use of these models has been considerably scarce in the multimodal literature. To fill this research gap, the authors aim to develop and evaluate three multivariate space-time models with different temporal trends and spatiotemporal interactions. The model estimates justified the use of mode-varying coefficients for explanatory variables as the impact of these factors varied across different crash modes. Largely a similar set of influential covariates was generated by the three models which indicate their robustness. However, notable differences were observed from the assessment of goodness-of-fit criteria employed in the study. The model with time-varying spatial random effects demonstrated superior performance under various prediction-related criteria. Nonetheless, due to the significant increase in the effective number of parameters that were utilized for model development, this model was inferior to competing models at deviance information criterion (DIC). The results also revealed the effectiveness of various random effects in capturing the unobserved heterogeneity that escapes the covariates.

This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.

18-05889

English

9p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

References (62) ; Tables

Crash rates; Mathematical models; Multimodal transportation; Time intervals

Spatiotemporal analysis

Highways; Safety and Human Factors

Transportation Research Board Annual Meeting 2018 Paper #18-05889

TRIS, TRB, ATRI

Jan 8 2018 11:31AM