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

Zhang J, Jia L, Niu S, Zhang F, Tong L, Zhou X - Sensors (Basel) (2015)

Bottom Line: 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.Several examples are used to demonstrate the results of proposed models in UAVs' route planning for small and medium-scale networks.

View Article: PubMed Central - PubMed

Affiliation: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China. zjs2107@163.com.

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.

No MeSH data available.


Related in: MedlinePlus

Optimal route of UAV from GAMS with a total flying time constraint of 15 time units.
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sensors-15-13874-f008: Optimal route of UAV from GAMS with a total flying time constraint of 15 time units.

Mentions: The UAV’s route planning problem is implemented and solved by a commercial solution solver, General Algebraic Modeling System (GAMS) using the model we developed in previous sections. The optimal routing plan of UAV is shown in Figure 8, where the vertical axis shows the space dimension with the horizontal axis for the time dimension. Given the total flying time constraint of 15 time units, the optimal route of UAV for this example is: node 1 (time 1) node 2 (time 2) node 3 (time 3) node 4 (time 4) node 5 (time 5–time 8) node 4 (time 9) node 3 (time 10) node 2 (time 11) node 1 (time 12–time 16). Due to the flying time constraint, the optimal solution cannot cover No. 1 incident, and the UAV arrives at node 5 after time 2, since No. 2 incident start and it does not reach to node 6 and node 7, while node 6 is covered by the fixed sensor.


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

Zhang J, Jia L, Niu S, Zhang F, Tong L, Zhou X - Sensors (Basel) (2015)

Optimal route of UAV from GAMS with a total flying time constraint of 15 time units.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507631&req=5

sensors-15-13874-f008: Optimal route of UAV from GAMS with a total flying time constraint of 15 time units.
Mentions: The UAV’s route planning problem is implemented and solved by a commercial solution solver, General Algebraic Modeling System (GAMS) using the model we developed in previous sections. The optimal routing plan of UAV is shown in Figure 8, where the vertical axis shows the space dimension with the horizontal axis for the time dimension. Given the total flying time constraint of 15 time units, the optimal route of UAV for this example is: node 1 (time 1) node 2 (time 2) node 3 (time 3) node 4 (time 4) node 5 (time 5–time 8) node 4 (time 9) node 3 (time 10) node 2 (time 11) node 1 (time 12–time 16). Due to the flying time constraint, the optimal solution cannot cover No. 1 incident, and the UAV arrives at node 5 after time 2, since No. 2 incident start and it does not reach to node 6 and node 7, while node 6 is covered by the fixed sensor.

Bottom Line: 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.Several examples are used to demonstrate the results of proposed models in UAVs' route planning for small and medium-scale networks.

View Article: PubMed Central - PubMed

Affiliation: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China. zjs2107@163.com.

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.

No MeSH data available.


Related in: MedlinePlus