Abstract
Spatio-temporal correlation and interaction are significant features of traffic accident data. To account for the spatio-temporal correlation and interaction and the ordinal nature simultaneously, spatio-temporal generalized ordered logit models with different temporal treatments are advocated to examine accident injury severities. In the context of Bayesian inference, we estimate the advocated models and compare them with traditional generalized ordered logit model and spatial generalized ordered logit model using six-year (2014-2019) accident data from the Dongguang section of Guangzhou-Shenzhen Coastal Freeway in China. The results indicate that there are significant spatio-temporal effects and interaction in the accident severity data, and that the spatio-temporal generalized ordered logit models yield low values of deviation information criterion and more reasonable parameter estimates than the generalized ordered logit and spatial generalized ordered logit models do. The estimation results reveal that covariates related to vehicle type, accident type, season, time, horizontal curvature, and bridge have significant effects on accident injury severities. The findings above demonstrate the superiority of the proposed spatio-temporal generalized ordered logit models for predicting accident severities.
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