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 language | English (US) |
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Pages (from-to) | 13874-13898 |
Number of pages | 25 |
Journal | Sensors (Switzerland) |
Volume | 15 |
Issue number | 6 |
DOIs | |
State | Published - 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