Comprehensive Assessment of Temporal Treatments in Crash Prediction Models

Abstract

This study conducted a comprehensive comparison of temporal treatments employed in crash prediction models. Nine groups of methodological approaches based on different ways of addressing temporal correlations, including the newly proposed time adjacency matrix, were developed. Moreover, three types of models were developed for each group in terms of spatial dependency. Finally, ten different assessment criteria were utilized for the evaluation purpose. All models and performance-checking criteria applied to 8 years of county-level crash counts in California. The modeling results illustrated that the space–time models consistently enhanced the precision associated with the intercepts. The serial and spatial correlations also appeared to be statistically significant. In terms of model complexity, the models with spatial correlations outperformed the ones without considering spatially structured heterogeneity, and the models accounting for the temporal dependency revealed more benefits compared with those without temporal treatments. The opposite trends were found by prediction-pertinent criteria based on the aggregation results, even though the first-order autoregressive process space–time models with spatiotemporal interaction claimed the first place of prediction in most cases. The correlation analysis among all ten criteria illustrated that the efficiency in reducing the effective number of parameters tended to have larger impacts on the value of deviance information criterion than did the mean deviance, which demonstrated the statistically significant correlations with all other prediction-related measures.