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• Bivariate framework of extreme value theory and machine learning for estimating crash frequency by severity level.
• Modified time-to-collision and expected post-collision change in velocity conflict measures are used.
• Anomaly detection/sampling techniques are integrated into the extreme value models.
• Bivariate models accurately estimate severe and non-severe crashes.
Traffic conflict techniques rely heavily on the proper identification of conflict extremes, which directly affects the prediction performance of extreme value models. Two sampling techniques, namely, block maxima and peak over threshold, form the core of these models. Several studies have demonstrated the inefficacy of extreme value models based on these sampling approaches, as their crash estimates are too imprecise, hindering their widespread practical use. Recently, anomaly detection techniques for sampling conflict extremes have been used, but their application has been limited to estimating crash frequency without considering the crash severity aspect. To address this research gap, this study proposes a hybrid model of machine learning and extreme value theory within a bivariate framework of traffic conflict measures to estimate crash frequency by severity level. In particular, modified time-to-collision (MTTC) and expected post-collision change in velocity (Delta-V or ΔV) have been proposed in the hybrid modeling framework to estimate rear-end crash frequency by severity level. Rear-end conflicts were identified through artificial intelligence-based video analytics for three four-legged signalized intersections in Brisbane, Australia, using four days of data. Non-stationary bivariate hybrid generalized extreme value models with different anomaly detection/sampling techniques (isolation forest and minimum covariance determinant) were developed. The non-stationarity of traffic conflict extremes was handled by parameterizing model parameters, including location, scale, and both location and scale parameters simultaneously. The results indicate that the bivariate hybrid models can estimate severe and non-severe crashes when compared with historical crash records, thereby demonstrating the viability of the proposed approach. A comparative analysis of two anomaly techniques reveals that the isolation forest model marginally outperforms the minimum covariance determinant model. Overall, the modeling framework presented in this study advances conflict-based safety assessment, where the severity dimension can be captured via bivariate hybrid models.
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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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