The main objective of this study was to evaluate the safety effects caused by altering the lengths of deceleration and acceleration lanes at rest areas on expressways in Korea. Although general conclusions can be found through crash-based safety analysis, to examine more specific optimal conditions considering various traffic conditions, this study proposes a novel framework to explore and evaluate crash-based and simulation-based safety performances. For this purpose, the safety performance function (SPF) and crash modification factor (CMF) were developed to reflect real-world safety impacts. To consider nonlinear trends of the parameters, nonlinearizing link functions were introduced into the analysis. Two types of simulation analyses were conducted to (1) find the combination of surrogate safety measures (SSMs) that best fit with the crash-based safety performance results and (2) determine the optimal lengths of deceleration lane and acceleration lanes for different traffic conditions. The results showed that the best length of deceleration lane of a rest area is between 240 and 260 m, depending on the traffic conditions. The results also indicated that the optimal length of acceleration lane of a rest area is between 385 and 400 m, depending on the traffic parameters. The findings of this study could be used to determine the safety solutions with a micro-traffic simulator.

Current Automated Vehicle (AV) technologies still face challenges in operating safely across diverse road environments, as existing infrastructure is not yet fully adapted to AV-specific requirements. While many previous studies have relied on simulations, real-world data is crucial for accurately assessing AV safety and understanding the impact of road characteristics. To address this gap, this study analyzes human-driven vehicle (HDV) crashes and AV failures using machine learning and explainable AI (XAI), providing insights into how road design can be improved to facilitate AV integration into existing infrastructure. Using XGBoost-based frequency modeling, the study achieved accuracy ranging from 0.6389 to 0.9770, depending on the specific model. The findings indicate that road geometry and traffic characteristics play a significant role in road safety, while the impact of road infrastructure varies across different road classifications. In particular, traffic characteristics were identified as key contributors to HDV crashes, whereas road geometry was the most critical factor in AV failures. By leveraging real-world AV failure data, this study overcomes the limitations of simulation-based research, improving the reliability of safety assessments. It provides a comprehensive evaluation of road safety across different road types and traffic flow conditions while simultaneously analyzing HDV crashes and AV failures. The findings offer critical insights into the challenges of mixed-traffic environments, where AVs and HDVs must coexist, highlighting the need for adaptive road design and infrastructure strategies to enhance safety for all road users.