@article{ref1,
title="A space-time network-based modeling framework for dynamic unmanned aerial vehicle routing in traffic incident monitoring applications",
journal="Sensors (Basel)",
year="2015",
author="Zhang, Jisheng and Jia, Limin and Niu, Shuyun and Zhang, Fan and Tong, Lu and Zhou, Xuesong",
volume="15",
number="6",
pages="13874-13898",
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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s150613874",
url="http://dx.doi.org/10.3390/s150613874"
}