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Sensors 2015, 15(6), 13874-13898; doi:10.3390/s150613874

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

1
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2
Research Institute of Highway Ministry of Transport, No. 8 Xitucheng Rd., Haidian District, Beijing 100088, China
3
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China
4
School of Traffic and Transportation, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China
5
School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Gonzalez Toro
Received: 18 April 2015 / Revised: 1 June 2015 / Accepted: 4 June 2015 / Published: 12 June 2015
(This article belongs to the Special Issue UAV Sensors for Environmental Monitoring)
View Full-Text   |   Download PDF [1349 KB, uploaded 12 June 2015]   |  

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. View Full-Text
Keywords: unmanned aerial vehicle; traffic sensor network; space-time network; lagrangian relaxation; route planning unmanned aerial vehicle; traffic sensor network; space-time network; lagrangian relaxation; route planning
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, J.; Jia, L.; Niu, S.; Zhang, F.; Tong, L.; Zhou, X. A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle Routing in Traffic Incident Monitoring Applications. Sensors 2015, 15, 13874-13898.

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