SURROGATE SAFETY ANALYSIS-LEVERAGING ADVANCED TECHNOLOGIES FOR SAFER ROADS

This article presents a thorough review of traffic safety research, concentrating on surrogate safety assessment methods. It explores how advanced technologies can innovate approaches to enhance road safety. Surrogate safety assessment utilizes near misses, traffic conflicts, and other precursors to predict potential hazards proactively. Incorporating state-of-the-art technologies like connected vehicles, advanced sensors, and simulators, this study aims to establish a robust framework for surrogate safety analysis. It includes a comparative analysis of various surrogate safety indicators to assess their effectiveness in predicting and preventing traffic incidents, with a focus on vulnerable road users. By integrating machine learning and deep learning techniques, this research enhances the predictive capabilities of surrogate safety models, facilitating proactive safety interventions. The findings contribute to ongoing discussions on transportation safety, offering insights into the evolving field of surrogate safety analysis. They aim to guide policymakers, urban planners, and transportation professionals in implementing evidence-based strategies to mitigate safety risks and improve overall road safety.