Examining causal factors of traffic conflicts at intersections using vehicle trajectory data

Abstract

Conflict severity results from the complex interactions between the roadway and environmental characteristics and the vehicle motion. Understanding how and to what extent a vehicle is influenced by roadway and surrounding road users during a conflict is helpful in analyzing the causal mechanisms of collisions, thus providing insights into roadway safety improvement countermeasures. This study utilized the NGSIM vehicle trajectory datasets to investigate the causal factors in conflicts at intersections by analyzing roadway-to-vehicle and vehicle-to-vehicle interactions. In order to remove the outliers and white noise existing in the raw data, vehicle trajectories were reconstructed by applying discrete wavelet transform and Kalman filtering (KF). Generalized time-to-collision was adopted to detect and measure the severity of conflicts, by which 1127 conflict events were extracted. Path analysis (PA) models were then established to determine in exactly which ways the roadway-to-vehicle and vehicle-to-vehicle interactions were related to conflict severity. Various roadway and environmental characteristics such as traffic flow average speed, percentage of trucks, and intersection skew angle were included in the models. The results indicate that the roadway and environmental characteristics have both direct and indirect effects on conflict severity. In the indirect effects, the kinematics of conflicting vehicles such as the average and standard deviation of speed, plays an intermediate role in linking roadway factors and conflict outcome. The framework of this study can be used to assess roadway readiness for both human-driven and automated vehicles.