A space-time network-based modeling framework for dynamic unmanned aerial vehicle routing in traffic incident monitoring applications

Jisheng Zhang, Limin Jia, Shuyun Niu, Fan Zhang, Lu Tong, Xuesong Zhou

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)13874-13898
Number of pages25
JournalSensors (Switzerland)
Volume15
Issue number6
DOIs
StatePublished - Jun 12 2015

Keywords

  • Lagrangian relaxation
  • Route planning
  • Space-time network
  • Traffic sensor network
  • Unmanned aerial vehicle

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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