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<p>This study looks at how Surrogate Safety Measures (SSMs) are used to evaluate traffic safety at signalized crossings when there is mixed traffic. Real-time flexibility, integration with Connected and Automated Vehicles (CAVs), and data-driven modeling are critical areas that still require investigation. Most SSM models were developed for human-driven vehicles and require modifications for automated environments. Future research should explore how CAVs influence key conflict metrics such as Time-to-Collision and Post Encroachment Time. The integration of Internet of Things technologies, LiDAR, and sensor-based systems presents opportunities to enhance real-time traffic safety assessments. A significant challenge is the lack of standardized SSM thresholds, leading to inconsistencies in risk assessment models. Addressing this requires large-scale meta-analyses and adaptive threshold-setting methodologies. Additionally, traditional SSM methodologies may not fully capture complex traffic interactions. Machine learning and deep learning techniques can enhance safety assessments by dynamically adjusting to real-world conditions. This review systematically analyzes the limitations of current SSM methodologies and proposes AI-driven frameworks for improved predictive accuracy, contributing to proactive safety interventions and more intelligent traffic management strategies. </p>

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A PRISMA review of the use of surrogate safety measures at signalized intersections with a focus on its application in mixed traffic condition

  • 期刊论文

  • 发表日期：

    2025年5月13日

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  • Avantika Singh,
  • Sachin Dass

摘要

This study looks at how Surrogate Safety Measures (SSMs) are used to evaluate traffic safety at signalized crossings when there is mixed traffic. Real-time flexibility, integration with Connected and Automated Vehicles (CAVs), and data-driven modeling are critical areas that still require investigation. Most SSM models were developed for human-driven vehicles and require modifications for automated environments. Future research should explore how CAVs influence key conflict metrics such as Time-to-Collision and Post Encroachment Time. The integration of Internet of Things technologies, LiDAR, and sensor-based systems presents opportunities to enhance real-time traffic safety assessments. A significant challenge is the lack of standardized SSM thresholds, leading to inconsistencies in risk assessment models. Addressing this requires large-scale meta-analyses and adaptive threshold-setting methodologies. Additionally, traditional SSM methodologies may not fully capture complex traffic interactions. Machine learning and deep learning techniques can enhance safety assessments by dynamically adjusting to real-world conditions. This review systematically analyzes the limitations of current SSM methodologies and proposes AI-driven frameworks for improved predictive accuracy, contributing to proactive safety interventions and more intelligent traffic management strategies.