Citation

Zhang J, Jia L, Niu S, Zhang F, Tong L, Zhou X. Sensors (Basel) 2015; 15(6): 13874-13898.

Affiliation

School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA. xzhou74@asu.edu.

Copyright

(Copyright © 2015, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s150613874

PMID

26076404

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.

Language: en