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Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data

Author

Listed:
  • Hui Chen

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
    Department of Infrastructure Development, National Development and Reform Commission, Beijing 100045, China)

  • Zhaoming Chu

    (Research Institute for Road Safety of MPS, Beijing 100062, China)

  • Chao Sun

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

Abstract

Since traffic origin-destination (OD) demand is a fundamental input parameter of urban road network planning and traffic management, multisource data are adopted to study methods of integrated sensor deployment and traffic demand estimation. A sensor deployment model is built to determine the optimal quantity and locations of sensors based on the principle of maximum link and route flow coverage information. Minimum variance weighted average technology is used to fuse the observed multisource data from the deployed sensors. Then, the bilevel maximum likelihood traffic demand estimation model is presented, where the upper-level model uses the method of maximum likelihood to estimate the traffic demand, and the lower-level model adopts the stochastic user equilibrium (SUE) to derive the route choice proportion. The sequential identification of sensors and iterative algorithms are designed to solve the sensor deployment and maximum likelihood traffic demand estimation models, respectively. Numerical examples demonstrate that the proposed sensor deployment model can be used to determine the optimal scheme of refitting sensors. The values estimated by the multisource data fusion-based traffic demand estimation model are close to the real traffic demands, and the iterative algorithm can achieve an accuracy of 10 −3 in 20 s. This research has significantly promoted the effects of applying multisource data to traffic demand estimation problems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13057-:d:687775
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    References listed on IDEAS

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    1. He, Sheng-xue, 2013. "A graphical approach to identify sensor locations for link flow inference," Transportation Research Part B: Methodological, Elsevier, vol. 51(C), pages 65-76.
    2. Spiess, Heinz, 1987. "A maximum likelihood model for estimating origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 21(5), pages 395-412, October.
    3. Sang Nguyen & Clermont Dupuis, 1984. "An Efficient Method for Computing Traffic Equilibria in Networks with Asymmetric Transportation Costs," Transportation Science, INFORMS, vol. 18(2), pages 185-202, May.
    4. Simonelli, Fulvio & Marzano, Vittorio & Papola, Andrea & Vitiello, Iolanda, 2012. "A network sensor location procedure accounting for o–d matrix estimate variability," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1624-1638.
    5. Yang, Hai & Zhou, Jing, 1998. "Optimal traffic counting locations for origin-destination matrix estimation," Transportation Research Part B: Methodological, Elsevier, vol. 32(2), pages 109-126, February.
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    Cited by:

    1. 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.
    2. 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.

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