01763462

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https://annualmeeting.myt...nlineProgram/Details/15795

Extensive research efforts have been put forth to improve the prediction of crash frequencies by employing the spatio-temporal models. Despite the large number of studies exploring various spatial and temporal effects, there is still a lack of conclusive findings related with the performance of spatio-temporal interactions. The current study bridges the gap by performing a comprehensive comparison of different spatio-temporal interactions with distinct temporal treatments in crash frequency models. Fifteen spatio-temporal models were developed which can be clustered from different perspectives: (1) three groups of models based on temporal treatments containing linear time trend, autoregressive-1 (AR1), and random walk-1 (RW1); (2) models with and without spatio-temporal interaction terms; (3) four different types of interactions including both structured and unstructured spatial or temporal random effects. To estimate the model parameters, the present study employed a fast Bayesian inference approach, or, Integrated Nested Laplace Approximation (INLA). The predictive accuracy of alternative models was assessed by employing various evaluation criteria which include deviance information criterion (DIC), log pseudo marginal likelihoods (LPML), and Probability Integral Transform (PIT). The results illustrated that the models with spatiotemporal interaction perform better than the models without spatiotemporal interactions. The dynamic temporal effects, RW1 and AR1, were found to perform almost the same, with both being superior to the non-dynamic parametric linear trend. With respect to the average performance among all interactions, the interaction of both unstructured spatial and temporal effects was found to outperform others.

This paper was sponsored by TRB committee ACS20 Standing Committee on Safety Performance Analysis.

TRBAM-21-01915

English

25p

2021

Transportation Research Board 100th Annual Meeting

Digital/other

Figures; Maps; References; Tables

Bayes' theorem; Crash rates; Laplace transforms; Predictive models; Spatial analysis

Highways; Planning and Forecasting; Safety and Human Factors

Transportation Research Board Annual Meeting 2021 Paper #TRBAM-21-01915

TRIS, TRB, ATRI

Dec 23 2020 11:01AM