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Synergistic sensor location for link flow inference without path enumeration: A node-based approach

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  • Ng, ManWo

Abstract

Sensors are becoming increasingly critical elements in contemporary transportation systems, gathering essential (real-time) traffic information for the planning, management and control of these complex systems. In a recent paper, Hu, Peeta and Chu introduced the interesting problem of determining the smallest subset of links in a traffic network for counting sensor installation, in such a way that it becomes possible to infer the flows on all remaining links. The problem is particularly elegant because of its limited number of assumptions. Unfortunately, path enumeration was required, which – as recognized by the authors – is infeasible for large-scale networks without further simplifying assumptions (that would destroy the assumption-free nature of the problem). In this paper, we present a reformulation of this link observability problem, requiring only node enumeration. Using this node-based approach, we prove a conjecture made by Hu, Peeta and Chu by deriving an explicit relationship between the number of nodes and links in a transportation network, and the minimum number of sensors to install in order to be able to infer all link flows. In addition, we demonstrate how the proposed method can be employed for road networks that already have sensors installed on them. Numerical examples are presented throughout.

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  • Ng, ManWo, 2012. "Synergistic sensor location for link flow inference without path enumeration: A node-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 781-788.
  • Handle: RePEc:eee:transb:v:46:y:2012:i:6:p:781-788
    DOI: 10.1016/j.trb.2012.02.001
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    4. Siripirote, Treerapot & Sumalee, Agachai & Ho, H.W. & Lam, William H.K., 2015. "Statistical approach for activity-based model calibration based on plate scanning and traffic counts data," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 280-300.
    5. 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.
    6. 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.
    7. Saif Eddin Jabari & Laura Wynter, 2016. "Sensor placement with time-to-detection guarantees," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(4), pages 415-433, December.
    8. Yang, Yudi & Fan, Yueyue, 2015. "Data dependent input control for origin–destination demand estimation using observability analysis," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 385-403.
    9. Hadavi, Majid & Shafahi, Yousef, 2016. "Vehicle identification sensor models for origin–destination estimation," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 82-106.
    10. Rinaldi, Marco & Viti, Francesco, 2017. "Exact and approximate route set generation for resilient partial observability in sensor location problems," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 86-119.
    11. Ng, ManWo, 2013. "Partial link flow observability in the presence of initial sensors: Solution without path enumeration," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 51(C), pages 62-66.
    12. Lo, Hong K. & Chen, Anthony & Castillo, Enrique, 2016. "Robust network sensor location for complete link flow observability under uncertaintyAuthor-Name: Xu, Xiangdong," Transportation Research Part B: Methodological, Elsevier, vol. 88(C), pages 1-20.
    13. 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.
    14. 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.
    15. Zhu, Ning & Fu, Chenyi & Zhang, Xuanyi & Ma, Shoufeng, 2022. "A network sensor location problem for link flow observability and estimation," European Journal of Operational Research, Elsevier, vol. 300(2), pages 428-448.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. Rinaldi, Marco, 2018. "Controllability of transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 381-406.

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