Preventing Snow-Induced Traffic Isolation Through Data-Driven Control: Toward Resilient and Sustainable Highway Management

This study develops a data-driven framework to prevent traffic isolation on snow-affected highways by analyzing vehicle detection system (VDS) data collected over the past decade in the Yeongdong region of the Republic of Korea. Specifically, we used hourly traffic volume and average travel speed between interchange to interchange (IC-IC) segments on days with cumulative snowfall exceeding 30 cm, enabling the identification of critical thresholds that trigger congestion and isolation under extreme snow conditions. By examining the correlation between hourly snowfall intensity, traffic volume, and travel speed, we identified critical thresholds that signal the onset of traffic congestion and isolation, where traffic congestion refers to temporary flow deterioration with average speeds falling below 40 km/h, and traffic isolation denotes and operational breakdown characterized by average travel speeds falling below 20 km/h and prolonged loss of roadway functionality. Results indicated that when snowfall intensity exceeded 2 cm per hour, traffic congestion generally emerged once hourly volumes surpassed 1500 vehicles, whereas traffic isolation became likely when volumes exceeded 2200 vehicles per hour. Building on these findings, this study proposes adaptive traffic control measures that can be proactively implemented during snowstorm conditions. The proposed framework further provides a basis for determining the optimal timing of intervention before isolation occurs, thereby preventing operational breakdowns and enhancing both the resilience and sustainability of winter highway operations.