Abstract
This study examines various influential factors aimed at identifying crashes involving senior drivers. We consider variables reflecting the individual characteristics of drivers, crash types, roadway conditions, and environments, along with latent effects related to space and time, as potential identifiers of senior driver-related crashes in Daejeon. To this end, we employ Bayesian multilevel logistic regression models to unveil the random influences arising from spatial dependency and temporal autocorrelation after accounting for the fixed effects by explanatory variables. The result reveals that the overall annual trend, violations, and intersections appear to be positively associated with the log odds of the probability. Conversely, snow and icy roadway surface conditions, along with several crash types, are estimated to lower the log odds. The latent spatial effects dominate overall residual fluctuation, suggesting the benefits of our adopted model. We argue that our approach lays the foundations for designing safety policies for senior drivers.
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