Multi-Objective Optimal Deployment of Road Traffic Monitoring Cameras: A Case Study in Wujiang, China

Yiming Li, Zeyang Cheng (), Xinpeng Yao, Zhiqiang Kong, Zijian Wang and Mengfei Liu
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Yiming Li: Shandong Key Laboratory of Smart Transportation (Preparation), Jinan 250101, China
Zeyang Cheng: School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China
Xinpeng Yao: Shandong Key Laboratory of Smart Transportation (Preparation), Jinan 250101, China
Zhiqiang Kong: School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China
Zijian Wang: Shandong Key Laboratory of Smart Transportation (Preparation), Jinan 250101, China
Mengfei Liu: Shandong Key Laboratory of Smart Transportation (Preparation), Jinan 250101, China

Sustainability, 2023, vol. 15, issue 15, 1-20

Abstract: This study presents a multi-objective optimal framework for deploying traffic monitoring cameras at road networks. Compared with previous studies that focused on addressing single traffic problem such as OD estimation, link flow observation, path flow reconstruction, and travel time estimation, this study aims to address a comprehensive traffic management problem, including crash prevention, traffic violation governance, and traffic efficiency improvement. First, a potential principle for selecting the location of traffic monitoring deployment is determined, taking into account the key signalized intersections, areas prone to traffic congestion, crash-prone spots, and areas prone to traffic violations. Then, a multi-objective optimal model is developed to minimize the ATFM (i.e., average traffic volume of each five minutes), TCF (i.e., traffic crash frequency), and TVF (i.e., traffic violation frequency) while adhering to cost constraints. Finally, RVEA and NSGA-II algorithms are used to solve the multi-objective optimal model, respectively, and a comprehensive metric is proposed to evaluate the deployment schemes. The case study results demonstrate that the solutions obtained by the RVEA algorithm outperform those of the NSGA-II algorithm, and the best traffic monitoring deployment rate is 62.79%, under cost constraints. In addition, the comparison using the FAHP method also illustrates that the RVEA scheme is superior to the NSGA-II scheme. The above research results could potentially be used to optimize the locations of traffic cameras in road networks, which help to improve traffic management.

Keywords: multi-objective optimal design; traffic monitoring cameras; RVEA method; NSGA-II method; deployment rate (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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