Unmanned aerial vehicle scheduling problem for traffic monitoring
Introduction
With the rapid propulsion of urbanization and the surge in car ownership in recent years, the contradiction of urban traffic supply and demand turns worse, which results the corresponding traffic congestion and traffic safety issues. As of 2016, car ownership in China had reached 285 million. According to China Highway Network, a vertical website of China Highway Academy, which focuses on information communication and service in the construction, management and planning of Chinese highways, 15 of the major cities with more than one million citizens lost nearly ten billion China Yuan per day due to traffic congestion and management problems in China. Traffic jams are rather common in many cities in China. Beijing, Shanghai and other large cities are becoming ‘blocked’ cities. An adverse road condition renders the car the most time-consuming mode of travel; this trend is spreading to second- and third-tier cities in China.
How to prevent and reduce traffic problems using reasonable method is the focus of this study. Road traffic information is the basis for the implementation of traffic planning and traffic control. Existing urban road traffic monitoring devices, such as vehicle induction loops, traffic cameras and infrared monitors, can collect required traffic flow and speed data. As traffic inspection equipment are not installed on a number of express roads, primary arterial roads, secondary arterial roads and other road segments, traffic information cannot always be obtained. Unmanned aerial vehicles (UAVs), which are commonly known as drones, constitute a new type of monitoring equipment. Equipped with different imaging sensors, UAVs can capture target images, and the monitored images can be transmitted in real time to a control station via a wireless transmission system. Recent developments in aviation, microelectronics, computers, navigation, communications, sensors and other related technologies have yielded continuous improvements in the performance of UAVs. Information collection is one of the most important applications of UAVs (Xia, Batta, & Nagi, 2017) and has been extensively employed in meteorological exploration, disaster monitoring, geological surveys, environmental monitoring, avalanche detection and other fields. The use of UAVs as a mean of collecting traffic data offers numerous advantages, such as the capacity to observe wide areas (covering 300–500 square meters at a height of 150–300 m), low cost, excellent flexibility, high efficiency and real-time operations (Harwin & Lucieer, 2012).
In this paper, UAVs serve as a traditional traffic monitor auxiliary or supplementary method for collecting road traffic information. In this vein, given a fleet of UAVs and monitoring requirements, it is critical to optimize the routing of UAVs in multiple periods in order to minimize operating costs. The remainder of this paper is organized as follows: Section 2 reviews related works; Section 3 elaborates on the problem background; and the model is formulated in Section 4. Local branching based solution methods are developed in Section 5. The numerical experiments are illustrated in Section 6, and the conclusions are presented in the last section.
Section snippets
Related works
UAV-related studies have become a popular research topic in recent years. Salvo, Caruso, and Scordo (2014) presented a method to appraise actual traffic flow situations in urban areas using UAVs to achieve accurate traffic research. Guerriero, Surace, Loscrí, and Natalizio (2014) proposed a distributed system of autonomous UAVs and presented a mathematical formulation of the problem as a multi-criteria optimization model based on a vehicle routing problem with soft time window (VRPTW)
Problem description
The problem in this study relates to dynamically allocating a fleet of UAVs to a traffic network for monitoring demand arcs in multiple periods. Fig. 1 shows the main process of using UAVs for road traffic monitoring. First, the locations and number of road sections are determined to be monitored and use them as monitoring tasks for UAVs. Then, these monitoring tasks are entered in control platform in order to assign which routes each UAV should monitor. Subsequently, airborne high-definition
Model formulation
In this section, we formulate a mixed integer programming model for UAV scheduling in traffic monitoring. This model is modified based on the classical CARP model of Golden and Wong (1981) to adapt to the background of UAVs monitoring traffic. Further, we extend the problem to multiple periods. Limited battery capacity will affect the flight endurance of UAVs, which may affect their monitoring capability. As most UAVs that are suitable for traffic flow monitoring are battery-powered, relevant
Algorithmic strategies
The mathematical model presented in Section 4 can be immediately solved by the CPLEX solver for small-scale problem instances. However, for other large-scale instances, the model is too intractable to be directly solved by the CPLEX. Thus, the local branching based method is considered to solve the formulated model.
Model application and experiments
In this section, the proposed model and solution algorithms are applied to a practical problem of traffic flow monitoring in Shanghai, China. Some numerical experiments are conducted on a PC (Intel Core i5, 1.70 GHz; Memory, 8 G) by CPLEX 12.6.1 (Visual Studio 2015, C#) to validate the feasibility of the proposed model and the effectiveness of the solution method.
Conclusions
This study proposes a mixed integer programming model for traffic monitoring, which optimizes the decisions of UAV scheduling with the objective of minimizing the total expected costs of operation for uncertain monitoring demands. Some advances and contributions of this study are as follows:
- (1)
The UAVs are introduced as a supplementary means of urban road traffic inspection equipment to improve the accuracy of traffic monitoring. As mobile sensors, UAVs are more suitable for monitoring demand arcs
Acknowledgements
The authors would like to thank the Associate Editor, and reviewers for their valuable comments and constructive suggestions, which have greatly improved the quality of this paper. In this research, Lu Zhen is sponsored by the National Natural Science Foundation of China (71422007 and 71671107); Shuaian Wang is sponsored by Environment and Conservation Fund Project 92/2017.
References (26)
- et al.
A genetic algorithm-Taguchi based approach to inventory routing problem of a single perishable product with transshipment
Computers & Industrial Engineering
(2017) - et al.
Safety and security management with Unmanned Aerial Vehicle (UAV) in oil and gas industry
Procedia Manufacturing
(2015) Dynamic UAV-based traffic monitoring under uncertainty as a stochastic arc-inventory routing policy
International Journal of Transportation Science and Technology
(2016)- et al.
A multi-objective approach for unmanned aerial vehicle routing problem with soft time windows constraints
Applied Mathematical Modelling
(2014) - et al.
Evolutionary algorithms for periodic arc routing problems
European Journal of Operational Research
(2005) - et al.
An inventory–routing problem with the objective of travel time minimization
European Journal of Operational Research
(2014) - et al.
Location-arc routing problem: heuristic approaches and test instances
Computers & Operations Research
(2014) - et al.
The periodic capacitated arc routing problem with irregular services
Discrete Applied Mathematics
(2013) - et al.
The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery
Transportation Research Part C: Emerging Technologies
(2015) - et al.
Location arc routing problem with inventory constraints
Computers & Operations Research
(2016)
The synchronized arc and node routing problem: Application to road marking
Computers & Operations Research
Urban Traffic Analysis through an UAV
Procedia - Social and Behavioral Sciences
Inventory rebalancing and vehicle routing in bike sharing systems
European Journal of Operational Research
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