Estimating the influence of disruption on highway networks using GPS data

被引:5
|
作者
Yang, Zhenzhen [1 ,3 ]
Liu, Feng [2 ]
Gao, Ziyou [1 ]
Sun, Huijun [1 ]
Zhao, Jiandong [1 ]
Janssens, Davy [2 ]
Wets, Geert [2 ]
机构
[1] Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China
[2] Hasselt Univ, Transportat Res Inst, Wetenschapspk 5,Bus 6, B-3590 Diepenbeek, Belgium
[3] Beijing PalmGo Infotech Co Ltd, Beijing 100085, Peoples R China
基金
中国国家自然科学基金;
关键词
Highway networks; Disruption; Traffic flows; Congestion; The Bayesian theory; The thrice-standard-error principle; VULNERABILITY ANALYSIS; ROBUSTNESS; LINKS;
D O I
10.1016/j.eswa.2021.115994
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Incidents, such as natural disasters, public events, and holidays, often cause problems to highways, even paralyze the operation of the whole networks, leading to a serious threat to travel efficiency and safety of the public. To provide better transport management and plans for emergencies, it is important to quickly and accurately identify such incidents and estimate their disruptive effects on the networks. To this end, a novel approach has been proposed in this paper, which is based on the Bayesian theory and thrice-standard-error principle while utilizing vehicle GPS data. Two important indicators, including traffic flows and congestion indexes, along with their change ratios, are built to detect the incidents and evaluate the extent of the disruption. The specific disrupted and detour roads are further determined. The proposed method has been tested using two real-world events in China, and the potential and effectiveness of this technique are demonstrated. With more and more vehicles being equipped with GPS devices worldwide, the designed method can be easily transferable to other countries, paving a way for the adoption of the approach for a more spatial-temporal sensitive highway network disruption analysis method that supports the establishment of a more resilient transport system for emergencies.
引用
收藏
页数:16
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