PURPOSES : This study aimed to identify factors affecting the duration of traffic incidents in tunnel sections, as accidents in tunnels tend to cause more congestion than those on main roads. Survival analysis and a Cox proportional hazards model were used to analyze the determinants of incident clearance times. METHODS : Tunnel traffic accidents were categorized into tunnel access sections versus inner tunnel sections according to the point of occurrence. The factors affecting duration were compared between main road and tunnel locations. The Cox model was applied to quantify the effects of various factors on incident duration time by location. RESULTS : Key factors influencing mainline incident duration included collision type, driver behavior and gender, number of vehicles involved, number of accidents, and post-collision vehicle status. In tunnels, the primary factors identified were collision type, driver behavior, single vs multi-vehicle involvement, and vehicles stopping in the tunnel after collisions. Incidents lasted longest when vehicles stopped at tunnel entrances and exits. In addition, we hypothesize that incident duration in tunnels is longer than in main roads due to the reduced space for vehicle handling. CONCLUSIONS : These results can inform the development of future incident management strategies and congestion mitigation for tunnels and underpasses. The Cox model provided new insights into the determinants of incident duration times in constrained tunnel environments compared to open main roads.

본 연구는 유고로 인한 대기행렬, 통행시간과 같은 혼잡정보를 예측하여 제공하는 것을 목표로 하며, 이것은 교통시설 이용자와 운영자 모두에게 효율적인 대안선택 및 운영을 위한 중요한 요소로 활용된다. 이러한 예측된 사고영향 정보의 제공으로 인하여, 이용자는 유고 구간에 대한 정보를 사전에 인지하여 지체를 최소화 할 수 있고, 운영자는 현재 유고영향을 받을 것으로 예상되는 구간을 효율적으로 관리할 수 있을 것이다. 본 연구에서는 연속류 본선구간에서 단기예측기법을 적용한 유고영향 예측모형을 제안하였다. 본 연구에서 제안한 모형은 MARE를 통하여 상대적인 오차를 비교분석하여, 예측력이 뛰어난 모형을 정립하였다. 본 연구를 시작으로 미시적인 사고영향 예측 모형이 개발된다면 사고발생 시 지체를 최소화하고 사회적인 비용을 줄일 수 있을 것이다.