Application of surrogate safety measures in higher levels of automated vehicles simulation studies: A review of the state of the practice

OBJECTIVE: Surrogate safety measures (SSMs) are developed and applied as alternatives or complements of safety analyses mainly due to important road crash data availability and reliability limitations. Automated vehicles (AVs) have recently emerged as a prominent solution to mitigate transport externalities and increase road traffic safety. Due to the novelty of the technology and the lack of real-world data, traffic simulation combined with SSMs is the most common approach to quantify their impact. This study aims to provide an overview of the state of the practice and, more specifically, examine the applicability of applied SSMs on higher levels of AVs (HAVs). METHODS: The methodological approach consists of a comprehensive literature search, which aims to provide an overview of the applied SSMs, followed by a critical assessment of the findings. RESULTS: In total, 17 studies and 11 different SSMs were identified and reviewed. Findings suggest that available SSMs are suitable measures to appropriately estimate the relative safety performance of HAVs and indicate their potential implications due to their expected rule-based driving behavior. However, in some cases, it was noticed that they could not efficiently capture the technological capabilities of HAVs, e.g., shorter headways and faster reaction times, which may lead to false alarms. CONCLUSIONS: Despite the available evidence, there are still significant gaps and certain limitations, as no comparisons between different measures exist, or the validity of the applied measures could not be assessed based on historical road crash data. This work aims to help researchers and practitioners choose the most appropriate SSMs to evaluate HAVs' safety performance. Finally, several research gaps are identified, and recommendations for potential future research directions are presented.