Investigating the different characteristics of weekday and weekend crashes

• Providing a systematic approach to analyze weekday and weekend crashes at both aggregate and disaggregate levels
• Comparing multivariate Poisson model and Poisson model with correlated random effects to analyze weekday and weekend crashes simultaneously
• Introducing random effects Bayesian logistic regression model to analyze the crash time (weekday vs. weekend) propensity to identify the different characteristics of weekday and weekend crashes.
• Estimating real-time crash prediction models to confirm the distinct occurrence mechanisms of weekday and weekend crashes.

IntroductionThis study provides a systematic approach to investigate the different characteristics of weekday and weekend crashes.MethodWeekend crashes were defined as crashes occurring between Friday 9 p.m. and Sunday 9 p.m., while the other crashes were labeled as weekday crashes. In order to reveal the various features for weekday and weekend crashes, multi-level traffic safety analyses have been conducted. For the aggregate analysis, crash frequency models have been developed through Bayesian inference technique; correlation effects of weekday and weekend crash frequencies have been accounted. A multivariate Poisson model and correlated random effects Poisson model were estimated; model goodness-of-fits have been compared through DIC values. In addition to the safety performance functions, a disaggregate crash time propensity model was calibrated with Bayesian logistic regression model. Moreover, in order to account for the cross-section unobserved heterogeneity, random effects Bayesian logistic regression model was employed.ResultsIt was concluded that weekday crashes are more probable to happen during congested sections, while the weekend crashes mostly occur under free flow conditions. Finally, for the purpose of confirming the aforementioned conclusions, real-time crash prediction models have been developed. Random effects Bayesian logistic regression models incorporating the microscopic traffic data were developed. Results of the real-time crash prediction models are consistent with the crash time propensity analysis. Furthermore, results from these models would shed some lights on future geometric improvements and traffic management strategies to improve traffic safety.Impact on IndustryUtilizing safety performance to identify potential geometric improvements to reduce crash occurrence and monitoring real-time crash risks to pro-actively improve traffic safety.