Optimizing UAV traffic monitoring routes during rush hours considering spatiotemporal variation of monitoring demand

Abstract

Dynamic changes in traffic conditions cause spatiotemporal variation in traffic monitoring demand. It is, therefore, necessary to conduct efficient road monitoring to identify dynamic abnormal situations, especially in peak traffic periods. Recently, unmanned aerial vehicles (UAVs) have become an attractive solution to this problem. However, UAV monitoring routes suffer from time limitations during peak traffic hours. To optimize UAV monitoring routes during rush hours, we develop a route planning method incorporating spatiotemporal variations in monitoring demand, in which we introduce a team orienteering arc routing problem with time-varying profits (TOARP-TP) and construct a corresponding mathematical model. The TOARP-TP is an extension of an already existing routing problem, team orienteering arc routing problem (TOARP). An iterated local search (ILS)-based algorithm is designed to solve the large instances of this problem. To verify the proposed method, we conduct sets of numerical experiments with the Sioux Falls road network in South Dakota, US, and a case study is applied using Wuhan, Hubei, PRC. The results demonstrate the efficiency and practicality of our method in optimizing UAV traffic monitoring routes during rush hours. Furthermore, we discuss a strategy for scenario determination and method selection in UAV route planning.

Keywords:

• UAV routing problem
• spatiotemporal demand
• TOARP
• time-dependent profits
• traffic monitoring

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available with the identifier at the public link (https://doi.org/10.5281/zenodo.6002873).

Additional information

Funding

This work was supported by grants from the National Key Research and Development Program of China (Nos. 2018YFB2100501 and 2019YFB2102903), the National Nature Science Foundation of China (NSFC) Program (No. 42071380), and the Science and Technology Project of Ministry of Housing and Rural-Urban Development of China (No. 2019-R-020).

Notes on contributors

Ke Wang

Ke Wang received a B.Sc. degree in geographical information system from the China University of Petroleum, China, in 2009, an M.S. degree in cartography and geographical information engineering from China University of Petroleum, China, in 2012, and a Ph.D. degree in photogrammetry and remote sensing from Wuhan University, China, in 2016. He is currently an associate professor at the China University of Geosciences in Wuhan, China. His research interests include the Geospatial Sensor Web, smart city sensing, spatio-temporal optimization, and intelligent computing.

Qianqian Wu

Qianqian Wu received a B.Sc. degree in physical geography and resources environment from China University of Geosciences (Wuhan) in 2019. She is currently working towards a master’s degree at the School of Geography and Information Engineering, China University of Geoscience in Wuhan, Hubei. Her current research interests include optimization modelling, optimization algorithms, and sensor node configuration in sensor networks.

Xiangting He

Xiangting He received the B.Sc. degree in geographic information systems from Shanxi Agricultural University in 2016. She is currently pursuing a master's degree with the Faculty of Information Engineering, China University of Geosciences, Wuhan, Hubei. Her current research interests include front-end geographic data visualization technology, database docking, and data interaction.

Chuli Hu

Chuli Hu received a B.Sc. degree in resources environment management from the Anhui University of Architecture, China, in 2008, an M.S. degree in geographical information systems from Wuhan University, China, in 2010, and a Ph.D. degree in photogrammetry and remote sensing from Wuhan University, China, in 2013. He is currently an associate professor at the China University of Geosciences in Wuhan, China. His research interests include sensor integration management and smart city sensing.

Nengcheng Chen

Nengcheng Chen received a B.Sc. degree in geodesy from Wuhan Technical University of Surveying and Mapping, China, in 1997, an M.S. degree in geographical information systems from Wuhan University, China, in 2000, and a Ph.D. degree in photogrammetry and remote sensing from Wuhan University in 2003. Currently, he is a professor of geographic information science at the State Key Lab for Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University in Wuhan, Hubei, China. His research interests include Smart Planet, Sensor Web, Semantic Web, Digital Antarctica, Smart City, and Web GIS.