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Location of turning ratio and flow sensors for flow reconstruction in large traffic networks

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  • Rodriguez-Vega, Martin
  • Canudas-de-Wit, Carlos
  • Fourati, Hassen

Abstract

In this work we examine the problem of minimizing the number of sensors needed to completely recover the vehicular flow in a steady state traffic network. We consider two possible sensor technologies: one that allows the measurement of turning ratios at a given intersection and the other that directly measures the flow in a road. We formulate an optimization problem that finds the optimal location of both types of sensors, such that a minimum number is required. To solve this problem, we propose a method that relies on the structure of the underlying graph, which has a quasi-linear computational complexity, resulting in less computing time when compared to other works in the literature. We evaluate our results using dynamical traffic simulations in synthetic networks.

Suggested Citation

  • Rodriguez-Vega, Martin & Canudas-de-Wit, Carlos & Fourati, Hassen, 2019. "Location of turning ratio and flow sensors for flow reconstruction in large traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 21-40.
    • Handle: RePEc:eee:transb:v:121:y:2019:i:c:p:21-40
    DOI: 10.1016/j.trb.2018.12.005
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    References listed on IDEAS

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    1. Fu, Chenyi & Zhu, Ning & Ling, Shuai & Ma, Shoufeng & Huang, Yongxi, 2016. "Heterogeneous sensor location model for path reconstruction," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 77-97.
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    9. Viti, Francesco & Rinaldi, Marco & Corman, Francesco & Tampère, Chris M.J., 2014. "Assessing partial observability in network sensor location problems," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 65-89.
    10. Castillo, Enrique & Calviño, Aida & Lo, Hong K. & Menéndez, José María & Grande, Zacarías, 2014. "Non-planar hole-generated networks and link flow observability based on link counters," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 239-261.
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    Cited by:

    1. Rodriguez-Vega, Martin & Canudas-de-Wit, Carlos & Fourati, Hassen, 2021. "Average density estimation for urban traffic networks: Application to the Grenoble network," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 21-43.
    2. Xiaoqi Wang & Heng Ma & Xiaohan Qi & Ke Gao & Shengnan Li, 2022. "Study on the Distribution Law of Coal Seam Gas and Hydrogen Sulfide Affected by Abandoned Oil Wells," Energies, MDPI, vol. 15(9), pages 1-19, May.
    3. Li, Li & Jabari, Saif Eddin, 2019. "Position weighted backpressure intersection control for urban networks," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 435-461.

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