TY  - JOUR
PY  - 2023//
TI  - Dynamic identification of short-term and longer-term hazardous locations using a conflict-based real-time extreme value safety model
JO  - Analytic methods in accident research
A1  - Ghoul, Tarek
A1  - Sayed, Tarek
A1  - Fu, Chuanyun
SP  - e100262
EP  - e100262
VL  - 37
IS  - 
N2  - 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 short-term analysis period. By further investigating the variation in short-term crash risk, longer-term hazardous location identification and ranking metrics such as the longer-term 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2213-6657
UR  - http://dx.doi.org/10.1016/j.amar.2022.100262
ID  - ref1
ER  -