Evaluating the Performance of Surrogate Safety Measures in Real-Time Crash Risk Prediction Using Pre-Crash Vehicle Trajectories

Abstract

The primary objective of this study was to evaluate the performance of surrogate safety measures (SSMs) for real-time crash risk prediction. Drone recordings were collected from a freeway section in Nanjing, China, over a year. Twenty rear-end crashes and their associated trajectories were obtained. Vehicle trajectories preceding the crash were segmented based on different time periods to represent varying crash conditions. The Extreme Value Theory (EVT) approach combined with a block maxima sampling method was then employed to investigate the generalized extreme value (GEV) distributions of extremely risky events under both non-crash and crash conditions. The prediction performance of SSMs was demonstrated by the differences in GEV distributions under these two conditions. Within the proposed modeling framework, the performances of Time-to-Collision (TTC) and Absolute value of Derivative of Instantaneous Acceleration (ADIA) were examined and compared. The results revealed a decreasing trend in the performances of TTC and ADIA as the prediction time window before a crash increased. For any given length of crash conditions, TTC consistently outperformed ADIA. Notably, TTC’s reliability in crash risk prediction became more uncertain when forecasting crashes more than 2s in advance. For practical application in the crash early warning, this study provided the optimal thresholds for TTC and ADIA. The methods and results in this study have the potential to be used for crash risk assessments in autonomous vehicles.

Keywords: Surrogate safety measures, Extreme value theory, Block maxima, Trajectory data, Real-time crash risk prediction