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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yiming Li & Zeyang Cheng & Xinpeng Yao & Zhiqiang Kong & Zijian Wang & Mengfei Liu, 2023. "Multi-Objective Optimal Deployment of Road Traffic Monitoring Cameras: A Case Study in Wujiang, China," Sustainability, MDPI, vol. 15(15), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:12011-:d:1210857
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    References listed on IDEAS

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    1. Salari, Mostafa & Kattan, Lina & Lam, William H.K. & Lo, H.P. & Esfeh, Mohammad Ansari, 2019. "Optimization of traffic sensor location for complete link flow observability in traffic network considering sensor failure," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 216-251.
    2. Li, Xiaopeng & Ouyang, Yanfeng, 2011. "Reliable sensor deployment for network traffic surveillance," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 218-231, January.
    3. David Morrison & Susan Martonosi, 2015. "Characteristics of optimal solutions to the sensor location problem," Annals of Operations Research, Springer, vol. 226(1), pages 463-478, March.
    4. Dongya Li & Wei Wang & De Zhao, 2022. "A Practical and Sustainable Approach to Determining the Deployment Priorities of Automatic Vehicle Identification Sensors," Sustainability, MDPI, vol. 14(15), pages 1-22, August.
    5. Hui Chen & Zhaoming Chu & Chao Sun, 2021. "Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data," Sustainability, MDPI, vol. 13(23), pages 1-11, November.
    6. Fu, Chenyi & Zhu, Ning & Ma, Shoufeng, 2017. "A stochastic program approach for path reconstruction oriented sensor location model," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 210-237.
    7. Shi An & Lina Ma & Jian Wang, 2020. "Optimization of Traffic Detector Layout Based on Complex Network Theory," Sustainability, MDPI, vol. 12(5), pages 1-22, March.
    8. Xiaopeng Li & Yanfeng Ouyang, 2012. "Reliable Traffic Sensor Deployment Under Probabilistic Disruptions and Generalized Surveillance Effectiveness Measures," Operations Research, INFORMS, vol. 60(5), pages 1183-1198, October.
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