Deriving and validating a new modified two-dimensional surrogate safety measure to investigate driving risk across different passenger vehicle types.

Driving risk assessment is crucial for advancing transportation safety, particularly in the context of increasing vehicle automation and electrification. This study proposes a modified two-dimensional time-to-collision (2D-MTTC) measure that incorporates relative acceleration in two-dimensional space to better characterize driving risk across different types of passenger vehicles. The proposed 2D-MTTC is validated through a two-stage validation process using different datasets to show its effectiveness and performance compared with the benchmark two-dimensional time-to-collision (2D-TTC): i) macro-level validation by computing the correlation between conflict risk and historical crash records of the road network; and ii) micro-level validation by detecting critical risk events in real conflicts and crash trajectories. After validation, the driving risk captured by 2D-MTTC between different types of passenger vehicles is examined using the Third Generation Simulation dataset (TGSIM). Specifically, the identified driving risks are compared between automated vehicles (AVs) and human-driven vehicles (HDVs), as well as between electric-based automated vehicles (EAVs) and internal combustion engine-based automated vehicles (ICEAVs). The results demonstrate the effectiveness of 2D-MTTC and its improved performance relative to 2D-TTC in identifying driving risks and capturing their duration and intensity. The findings further reveal that the driving risk characteristics among AVs and HDVs, EAVs and ICEAVs are significantly different and urge the consideration of vehicle types in proactive safety analysis.