Identifying High Crash Risk Highway Segments Using Jerk-Cluster Analysis

摘要

The state of the practice for municipal traffic agencies identifying high-risk road segments has been to use data on prior crashes. While historical traffic crash data is valuable in improving roadway safety, it relies on prior observations rather than future crash likelihoods. Recently, however, researchers have developed predictive crash methods based on "abnormal driving events." These include abrupt and atypical vehicle movements indicative of crash avoidance maneuvers and/or near-crashes, especially on highways. Due to limited data, the previous research only tested the crash-jerk ratio function on highways but not on other types of roads. This paper describes research that used naturalistic driving data collected from global positioning system (GPS) sensors to locate high concentrations of abrupt and atypical vehicle movements based on vehicle acceleration and vehicle rate of change of acceleration (jerk) on two interrupted highways. Statistical analyses revealed that clusters of high magnitude jerk events while decelerating were significantly correlated to long-term crash rates at these locations. These significant and consistent relationships between jerks and crashes suggest that such observational data can be used as surrogate measures of safety and as a way of predicting safety problems, further improving crash prediction models.