Dynamic identification of short-term and longer-term hazardous locations using a conflict-based real-time extreme value safety model

A novel and effective approach to safety management requires evaluating the safety of locations over short time periods (e.g. minutes). Unlike traditional methods that are based on aggregate crash records over a few years, crash proneness in this approach reflects short-time durations and is related to dynamic traffic changes and dangerous driving events. This paper proposes a new approach to dynamically assess the crash proneness of traffic conditions within a very short time (e.g., signal cycle length) and to dynamically identify high-risk locations. Using a Bayesian hierarchal Extreme Value Theory (EVT) model, the short-term crash risk metrics, risk of crash (ROC), and return level (RL), are calculated using traffic conflict data. A short-term hazardous location identification and ranking framework is developed based on crash-risk threshold exceedances for every shortterm analysis period. By further investigating the variation in short-term crash risk, longer-term hazardous location identification and ranking metrics such as the longerterm crash risk index (LTCRI) and the percent of time exceeding (PTE) were developed. Using these metrics, a framework is proposed by which hazardous intersections can be dynamically classified and ranked in both the short-term and the longer-term. This ranking may be dynamically updated as more data becomes available. The proposed framework was applied to a trajectory dataset consisting of 47 signalized intersections obtained from a UAV-based dataset. Conflicts were identified from vehicle trajectories and were used to compute the proposed short-term and longer-term metrics. The intersections within the network were then ranked based on the proposed framework. This study demonstrates the importance of investigating short-term fluctuations in crash risk that may otherwise be lost to averaging in longer-term analysis and proposes a simple and practical solution. & COPY; 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a novel approach to safety management by evaluating the safety of locations over short time periods. Unlike traditional methods based on aggregate crash records, this approach considers short-term durations and dynamic traffic changes. The proposed framework uses Bayesian hierarchal Extreme Value Theory model to calculate short-term crash risk metrics and identifies high-risk locations. It also investigates the variation in short-term crash risk and develops longer-term hazardous location identification and ranking metrics.