Evaluation of conventional surrogate indicators of safety for connected and automated vehicles in car following at signalized intersections

The driving behaviors of connected and automated vehicles (CAVs) will differ from those of human-driven vehicles (HDVs) because the CAVs’ driving decisions are controlled by computers. Because of the limited amount of crash data for CAVs, researchers have relied on surrogate measures of safety to assess their safety impacts. However, they often use the same safety indicators for CAVs that were used for HDVs, raising questions about the adequacy of the safety indicators for CAVs. This study aims to investigate the suitability of using conventional safety indicators for CAVs. To achieve this, we evaluated eight safety indicators used for CAVs in the literature: time-to-collision (TTC), post-encroachment time (PET), time-exposed TTC, time-integrated TTC, deceleration rate to avoid a crash (DRAC), crash-potential index, rear-end-collision risk index, and potential index for collision with urgent deceleration (PICUD). For the evaluation, we first simulate CAVs on an approaching lane of signalized intersections using the acceleration-control algorithm. The algorithm replaces the HDV trajectories with CAVs for mixed simulations where HDVs and CAVs coexist. Analyzing the simulation output, we examined the safety indicators for the various car-following scenarios and the CAV proportions. The findings suggest that PET and PICUD can yield different safety implications for CAVs because of their small-gap car-following characteristics. Ignoring such characteristics may lead to interpreting the small-gap car-following situations as simply dangerous traffic interactions for CAVs. The car-following experiments indicate that TTC, PET, and DRAC are insufficient in measuring the safety implications when successive vehicles operate at similar speeds for CAVs.

Mots clés

operations; traffic simulation; automated/autonomous/connected vehicles; surrogate safety measures; safety; transportation safety management systems; safety effects of connected/automated vehicles