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A graphical approach to identify sensor locations for link flow inference

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  • He, Sheng-xue

Abstract

Information of link flows in a traffic network becomes increasingly critical in contemporary transportation practice and researches. The network sensor installation is carried out to supply such information. In this paper, we present a graphical approach to determine the smallest subset of links in a traffic network for counting sensor installation, so as to infer the flows on all remaining links. The elegant assumption-free character of the problem introduced by Hu, Peeta and Chu is still kept in this approach. This study points out the topological tree feature of solutions that makes it possible for traffic management agencies to easily and flexibly select links for sensor installation in practice. Addressing from the same graphical perspective, we provide solutions to four other important problems about sensor locations. The preceding two problems are, in traffic networks that already have sensors installed on some links, to identify the subset of links on which link flows can be inferred from sensor measurements and to determine the smallest subset of links on which counting sensors also need to be installed so as to infer link flows on all remaining non-equipped links. The third is to identify the optimal locations for a given number of sensors so as to infer flows on as many links as possible by gradually enlarging the number of links included in circuits. The last one is to determine the smallest subset of links on which to install sensors, in such a way that it becomes possible at the same time to satisfy prior requirements and infer the flows on all remaining links, through building a minimum spanning tree. These methods can be applied to all kinds of long-term planning and link-based applications in traffic networks.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:51:y:2013:i:c:p:65-76
    DOI: 10.1016/j.trb.2013.02.006
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    Cited by:

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    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Owais, Mahmoud & Moussa, Ghada S. & Hussain, Khaled F., 2019. "Sensor location model for O/D estimation: Multi-criteria meta-heuristics approach," Operations Research Perspectives, Elsevier, vol. 6(C).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. An, Kun & Xie, Siyang & Ouyang, Yanfeng, 2018. "Reliable sensor location for object positioning and surveillance via trilateration," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 956-970.
    17. 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.
    18. Abdullah Alshehri & Mahmoud Owais & Jayadev Gyani & Mishal H. Aljarbou & Saleh Alsulamy, 2023. "Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    19. Rinaldi, Marco, 2018. "Controllability of transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 381-406.

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