1. School of Automotive and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China;
2. School of Transportation Engineering, Tongji University, Shanghai 201804, China;
3. Tianjin Municipal Engineering Research Institute, Tianjin 300074, China
A UAV Allocation Method for Traffic Surveillance in Sparse Road Network
LIU Xiao-feng1,2, GAO Li-mei3, GUANG Zhi-wei1, SONG Yu-qing1
1. School of Automotive and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China;
2. School of Transportation Engineering, Tongji University, Shanghai 201804, China;
3. Tianjin Municipal Engineering Research Institute, Tianjin 300074, China
摘要Unmanned aerial vehicle (UAV) technology was introduced in traffic surveillance in sparse road networks, and a UAV allocation method with/without UAV continuous flight distance constraint was proposed. First, the method of choosing the surveillance targets was proposed. The UAV traffic surveillance problem without maximum flight distance constraint was then formulated as a traveling salesman problem, and the simulated annealing algorithm was introduced to solve this problem. As for UAV traffic surveillance problem with continuous flight distance constraint, the K-means clustering algorithm was used to divide the UAV surveillance area into multiple sub-zones to convert this problem into UAV traffic surveillance scenarios without continuous flight distance constraint. Finally, taking the Korla-Kuqa expressway of Xinjiang and its road network as the example, the proposed UAV-based traffic surveillance allocation method for sparse road networks was demonstrated and validated using several field experiments. The experimental results show that UAV is an effective and useful tool for traffic surveillance in the sparse road networks of China's western regions.
Abstract：Unmanned aerial vehicle (UAV) technology was introduced in traffic surveillance in sparse road networks, and a UAV allocation method with/without UAV continuous flight distance constraint was proposed. First, the method of choosing the surveillance targets was proposed. The UAV traffic surveillance problem without maximum flight distance constraint was then formulated as a traveling salesman problem, and the simulated annealing algorithm was introduced to solve this problem. As for UAV traffic surveillance problem with continuous flight distance constraint, the K-means clustering algorithm was used to divide the UAV surveillance area into multiple sub-zones to convert this problem into UAV traffic surveillance scenarios without continuous flight distance constraint. Finally, taking the Korla-Kuqa expressway of Xinjiang and its road network as the example, the proposed UAV-based traffic surveillance allocation method for sparse road networks was demonstrated and validated using several field experiments. The experimental results show that UAV is an effective and useful tool for traffic surveillance in the sparse road networks of China's western regions.
基金资助:Supported by the National High Technology R&D Program of China (863 Program) (No.2009AA11Z220)
通讯作者:
LIU Xiao-feng, liulifun@21cn.com
E-mail: liulifun@21cn.com
引用本文:
刘晓峰, 高丽梅, 关志伟, 宋裕庆. 稀疏路网条件下的无人飞机交通监控部署方法[J]. Journal of Highway and Transportation Research and Development, 2013, 7(2): 81-87.
LIU Xiao-feng, GAO Li-mei, GUANG Zhi-wei, SONG Yu-qing. A UAV Allocation Method for Traffic Surveillance in Sparse Road Network. Journal of Highway and Transportation Research and Development, 2013, 7(2): 81-87.
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2013, Vol. 7 
