Estimating real-time crash risk at signalized intersections: A Bayesian Generalized Extreme Value approach

The current reactive road safety assessment cannot assess real-time crash risk at signalized intersections, and as such, the real-time risk mitigation strategies could not be developed. This shortcoming is mainly attributed to a lack of a proper methodological framework that could reveal insights into the real-time crash risk using the crash signatures or precursor of crashes, otherwise known as traffic conflicts, near misses, or surrogate safety measures. This study proposes a traffic conflict-based crash estimation technique to estimate real-time crash risk at signalized intersections. In particular, a Bayesian modeling framework is employed to estimate crash risk in realtime from traffic conflicts identified by modified time-to-collision (MTTC). A Block Maxima approach corresponding to generalized extreme value distribution is used to identify traffic extremes at a micro (i.e., traffic signal cycle) level. Then, the traffic flow at the signal cycle level is included as a covariate in the model to explain time-varying crash risk across different cycles. The unobserved heterogeneity associated with the crash risk of different cycles is also addressed within the Bayesian framework. The proposed framework is tested using a total of 96 h of traffic movement video data from a signalized intersection in Queensland, Australia. A comparison between the estimated crashes and the historical crash records demonstrates the suitability of the developed model for crash risk estimations. The crash risk of each signal cycle is identified by generating a different generalized extreme value distribution for each traffic signal cycle. Further statistical analyses reveal that crash risk varies within the different periods of a typical day, with higher crash risks found during the morning and evening peak periods. This study concludes the efficacy of the proposed real-time framework in estimating the rear-end crash risk at the micro-level, allowing proactive safety management and the development of risk mitigation strategies.

Automated summary

The current reactive road safety assessment lacks a proper methodological framework to assess real-time crash risk at signalized intersections, leading to a deficiency in developing real-time risk mitigation strategies. This study proposes a traffic conflict-based crash estimation technique using a Bayesian modeling framework to estimate crash risk in real-time. The proposed framework is tested using 96 hours of traffic movement video data from a signalized intersection in Queensland, Australia, and demonstrates the suitability of the model for crash risk estimations. The study concludes that the proposed real-time framework effectively estimates rear-end crash risk at the micro-level, enabling proactive safety management and the development of risk mitigation strategies.