Analyzing the Effect of Hurricane Helene on Freight Movement in Western North Carolina Using Telematic Data

远程信息处理 卡车 运输工程 弹性(材料科学) 地理空间分析 地理 布线(电子设计自动化) 洪水(心理学) 可靠性(半导体) 极端天气 自然灾害 环境资源管理 气候变化 工程类 交通基础设施 毒物控制 索引(排版) 地图学 气象学 环境科学
作者
Nana Duah,Steven Xiaochun Jiang,Om Prakash Yadav,Hieu Trung Nguyen,Salvador Hernández
出处
期刊:Transportation Research Record [SAGE Publishing]
标识
DOI:10.1177/03611981261457112
摘要

Hurricane-induced disruptions pose significant risks to freight transportation systems, particularly in mountainous regions with limited redundancy. This study investigates the effect of Hurricane Helene on freight movement in Western North Carolina using Global Positioning System-based telematics data from heavy commercial vehicles. Freight activity was temporally segmented into before, during, and after hurricane periods to evaluate changes in truck movement, corridor performance, rerouting behavior, and recovery patterns. A geospatial analysis framework integrating telematics trajectories, Shuttle Radar Topography Mission elevation data, and official roadway closure information was developed to identify disrupted freight corridors and adaptive routing responses. The results show substantial reductions in freight activity along I-40, particularly near the Pigeon River Gorge, where severe flooding and infrastructure failure caused prolonged disruptions. Freight traffic was rerouted toward higher-elevation corridors, including I-240, demonstrating the role of topography in shaping the resilience of the network during extreme weather events. Corridor performance analysis further revealed the deterioration in travel conditions during the hurricane period, with median travel times and travel time index values increasing substantially along the affected routes. Although freight movement partially recovered after the event, traffic intensity and reliability metrics indicated that corridor operations remained below pre-event conditions in heavily affected areas. The findings demonstrate the effectiveness of telematics data in capturing real time freight system disruption and recovery dynamics under extreme weather conditions. This study provides a foundation for developing policy strategies and decision support tools to improve freight network reliability under future climate risks.
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