Identification of Candidate Sections for Exclusive Freight Truck Road to Support Eco-Friendly Policy Making: A Screening Method Using Traffic Big Data

초록

This study proposes a methodology for identifying candidate sections for dedicated freight truck lanes by integrating transportation big data with traffic simulation techniques. Utilizing real-world traffic volume data from the Korea Transport Institute’s View-T system, the analysis evaluates traffic and environmental factors across the national road network. Thirteen key indicators?including freight vehicle ratio, average speed, congestion intensity, and pollutant emissions?were selected, normalized using Min-Max scaling, and aggregated into a composite Screening Score. Based on this score, high-priority road segments were systematically identified. The prioritization process was further refined by incorporating road attributes such as road classification, with weighted adjustments reflecting freight traffic volume to better capture freight-dense corridors. To assess the potential impacts of implementing dedicated freight lanes, top-ranked sections underwent microscopic traffic simulation analysis using VISSIM, evaluating metrics such as changes in average speed, congestion relief, emissions reduction, and traffic safety improvements. This study presents a practical and data-driven analytical framework that supports both strategic screening and simulation-based policy assessment for freight infrastructure planning.