Surrogate Safety Measures: Review and Assessment in Real-World Mixed Traditional and Autonomous Vehicle Platoons

Abstract

Surrogate safety measures (SSMs) are critical tools for evaluating the safety performance of mixed traffic. Crashes are rare events, and historical crash data are scarce for mixed traffic that includes autonomous and/ or connected vehicles. Recent safety review papers focus on traditional human-driven vehicles (TVs) and do not encompass advanced technology vehicles such as autonomous vehicles (AVs), connected vehicles (CVs), and connected-autonomous vehicles (CAVs). This study examines the development, implementation, and shortcomings of SSMs and SSM-based models used for mixed traffic safety evaluation. We review the current relevant literature and apply a case study analysis using a real-world mixed traffic dataset. The study summarizes the fundamental SSM guiding concepts, as well as their most significant metrics including threshold values employed in the past for SSMs and SSM-based models. Primary benefits and limitations of examined SSMs and SSM-based models are also underlined. This review reveals significant gaps in the literature that might guide future research paths in SSM-based mixed traffic safety assessment. Critical gaps include the absence of robust SSM threshold selection criteria, the suitability of current SSMs in mixed traffic research, microsimulation modeling that lacks proper calibration and validation, and the absence of a framework for selecting or combining multiple SSMs.

Keywords

• Mixed traffic
• surrogate safety measures
• real-world mixed traffic dataset
• autonomous vehicles
• connected vehicles
• connected-autonomous vehicles