This study aims to provide a basis for selecting the appropriate traffic-flow evaluation indicators by quantitatively analyzing the relative importance of such indicators in mixed traffic environments in which automated vehicles (AVs) and conventional vehicles coexist. As AV technology progresses and its adoption increases, establishing reliable evaluation criteria that accurately reflect the characteristics and performance of traffic systems under transitional conditions is crucial. Thus, approximately 40 domestic and international studies were reviewed in this study, from which 45 evaluation indicators were identified. These indicators were classified into three major categories: mobility, safety, and environment. Five frequently used and representative indicators were selected from each category based on the appearance frequency and relevance. An analytic hierarchy process survey was conducted with a group of transportation experts to derive the relative importance (weights) of both the major categories and individual indicators. The analysis revealed that safety (0.53676) was the most important category, followed by mobility (0.34795) and environment (0.11528). After combining the weights of the categories and sub-indicators, the top three indicators, i.e., time to collision (TTC), time exposed to TTC, and deceleration rate to avoid crashes, appeared to be safety related and associated directly with the collision risk. These findings suggest that, in the early stages of AV deployment, traffic evaluations should prioritize safety considerations over mobility or environmental factors to ensure the successful integration of AVs into existing traffic systems.

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.