title: A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle Routing in Traffic Incident Monitoring Applications.
abstract: It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs) start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network- based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs' route planning for small and medium-scale networks.
authors.affiliation: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China. zjs2107@163.com.|Research Institute of Highway Ministry of Transport, No. 8 Xitucheng Rd., Haidian District, Beijing 100088, China. zjs2107@163.com.
authors.forename: Jisheng
authors.initials: J
authors.lastname: Zhang
authors.affiliation: State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China. lmjia@vip.sina.com.
authors.forename: Limin
authors.initials: L
authors.lastname: Jia
authors.affiliation: Research Institute of Highway Ministry of Transport, No. 8 Xitucheng Rd., Haidian District, Beijing 100088, China. nsy@itsc.cn.
authors.forename: Shuyun
authors.initials: S
authors.lastname: Niu
authors.affiliation: Research Institute of Highway Ministry of Transport, No. 8 Xitucheng Rd., Haidian District, Beijing 100088, China. zhangfan@itsc.cn.
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
authors.affiliation: School of Traffic and Transportation, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China. ltong@bjtu.edu.cn.
authors.forename: Lu
authors.initials: L
authors.lastname: Tong
authors.affiliation: School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA. xzhou74@asu.edu.
authors.forename: Xuesong
authors.initials: X
authors.lastname: Zhou
keywords: lagrangian relaxation; route planning; space-time network; traffic sensor network; unmanned aerial vehicle