Multimodal crash frequency modeling: Multivariate space-time models with alternate spatiotemporal interactions. (April 2018)

• Record Type: Journal Article
• Authors: Cheng, Wen; Gill, Gurdiljot Singh; Ensch, John L.; Kwong, Jerry; Jia, Xudong
• Is Part Of: Accident analysis and prevention. Volume 113(2018)
• Page Start: 159
• Page End: 170
• Publication Date: 2018-04
• Subjects: Multimodal approach -- Multivariate space-time models -- Mode-varying coefficients -- Time-varying spatial random effects -- Site ranking; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2018.01.034
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Three multivariate crash frequency models for four crash modes at the county level. Spatial and temporal correlations with space-time interactions. A set of criteria employed for the assessment of model fit and site ranking. Mode-varying coefficients justified for influential factors in such cases. Model with time-varying spatial random effects demonstrated superior performance at crash prediction and site ranking. Abstract: Enhancement of safety for all transportation mode users plays an essential role in the implementation of multimodal transportation systems. Compared with crash frequency 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 aimed 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 evaluation criteria pertaining to predictive accuracy based on criteria assessing the training and test errors. The model with time-varying spatial random effects demonstrated superior performance for training and test errors. However, due to the significant increase in number of effective parameters that were utilized for model development, this model appeared to have the largest value of deviance information criterion (DIC). In terms of the comparison between models based on site ranking performance, the time-varying spatial random effects model demonstrated the best performance in both site consistency and method consistency. In other words, the superiority of the model's predictive performance could be transferred to yield more accurate result at site ranking.