A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle Routing in Traffic Incident Monitoring Applications

Sensors

Jisheng Zhang⁴  Limin Jia⁵  Shuyun Niu²  Fan Zhang²  Lu Tong³  Xuesong Zhou¹ 

[1] School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA;Research Institute of Highway Ministry of Transport, No. 8 Xitucheng Rd., Haidian District, Beijing 100088, China; E-Mails:;School of Traffic and Transportation, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China; E-Mail:;School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; E-Mail:;State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China; E-Mail:

关键词: unmanned aerial vehicle;    traffic sensor network;    space-time network;    lagrangian relaxation;    route planning;   

DOI  :  10.3390/s150613874

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.