Until all vehicles are equipped with autonomous driving technology, there will inevitably be mixed traffic conditions that consist of autonomous vehicles (AVs) and manual vehicles (MVs). Interactions between AVs and MVs have a negative impact on traffic flow. Cloverleaf interchanges (ICs) have a high potential to cause traffic accidents owing to merging and diverging. Analyzing the driving safety of cloverleaf ICs in mixed traffic flows is an essential element of proactive traffic management to prevent accidents. This study proposes a comprehensive simulation approach that integrates driving simulation (DS) and traffic simulation (TS) to effectively analyze vehicle interactions between AVs and MVs. The purpose of this study is to identify hazardous road spots for a freeway cloverleaf IC by integrating DS and TS in mixed traffic flow. The driving behavior data of MVs collected through a DS were used to implement vehicle maneuvering based on an intelligent driver model in the TS. The driving behavior of the AVs was implemented using the VISSIM parameters of the AVs presented in the CoEXist project. Additionally, the market penetration rate of AVs, ranging from 10% to 90% in 10% increments, was considered in the analysis. Deceleration rate to avoid crashes was adopted as the evaluation indicator, and pinpointing hazardous spot technique was used to derive hazardous road spots for the cloverleaf IC. The most hazardous road spot was identified in the deceleration lane where greater speed changes were observed. Hazardous road spots moved downstream within the deceleration lane as traffic volumes increased based on level of service. The number of AVs decelerating stably increased as traffic increased, thereby improving the safety of the deceleration lane. These results can be used to determine the critical point of warning information provision for preventing accidents when introducing AVs.

일반차량과 자율주행차량이 혼재하는 상황에서 발생가능한 미래 재난상황에 대한 관리방안 준비가 필요하다. 특히 재난 상황 중 안 개 발생 시 시야 확보가 어려운 일반차량 운전자와 센서기반 자율주행차량의 주행 특성이 다를 수 있다. 해당 상황에서의 문제점을 도출하고 이를 극복하기 위해 혼합교통류 관리 방안을 제안하고자 한다. 본 연구에서는 다양한 재난 상황 중 안개를 연구 대상으로 설정하였다. 과거 기상 상황별 일반차량을 주행 특성을 이력자료로 분석한 후, 안전한 교통흐름을 유지하기 위하여 자율주행차에게 정 보를 제공하는 방안을 제안한다.

PURPOSES : This study is to initiated to estimate the impact of mixed traffic flow on expressway section according to the market penetration rate(MPR) of automated vehicles(AVs) using a enhanced intelligent driver model(EIDM). METHODS : To this end, microscopic traffic simulation and EIDM were used to implement mixed traffic flow on basic expressway section and simulation network was calibrated to understand the change of impact in mixed traffic flow due to the MPR of AVs. Additionally, MOEs of mobility aspects such as average speed and travel time were extracted and analyzed. RESULTS : The result of the impact of mobility MOEs by MPR and level of service indicated that 100% MPR of AVs normally affect positive impact on expressway at all level of service. However, it was analyzed that improvements in the level of service from LOS A to C are minimal until the MPR of AVs reaches 75% or higher. CONCLUSIONS : This research shows that impact of MPR of AVs using EIDM of mixed traffic flow on basic expressway. Increasing MPR of AVs affects positive impact on expressway at all level of services. However, MPR from 25% to 75% of AVs in LOS A to C shows minimal impacts. Therefore, to maximize the effectiveness of AVs, appropriate traffic operation and management strategies are necessary.