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Assessing partial observability in network sensor location problems

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  • Viti, Francesco
  • Rinaldi, Marco
  • Corman, Francesco
  • Tampère, Chris M.J.

Abstract

The quality of information on a network is crucial for different transportation planning and management applications. Problems focusing on where to strategically extract this information can be broadly subdivided into observability problems, which rely on the topological properties of the network, and flow-estimation problems, where (prior) information on observed flows is needed to identify optimal sensor locations.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:70:y:2014:i:c:p:65-89
    DOI: 10.1016/j.trb.2014.08.002
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    References listed on IDEAS

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    Cited by:

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    2. Hänseler, Flurin S. & Bierlaire, Michel & Scarinci, Riccardo, 2016. "Assessing the usage and level-of-service of pedestrian facilities in train stations: A Swiss case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 106-123.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Wu, Xin & Nie, Lei & Xu, Meng & Zhao, Lili, 2019. "Distribution planning problem for a high-speed rail catering service considering time-varying demands and pedestrian congestion: A lot-sizing-based model and decomposition algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 61-89.
    12. Martínez-Díaz, Margarita & Pérez, Ignacio, 2015. "A simple algorithm for the estimation of road traffic space mean speeds from data available to most management centres," Transportation Research Part B: Methodological, Elsevier, vol. 75(C), pages 19-35.
    13. 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.
    14. Yang, Yudi & Fan, Yueyue & Wets, Roger J.B., 2018. "Stochastic travel demand estimation: Improving network identifiability using multi-day observation sets," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 192-211.
    15. Bagloee, Saeed Asadi & Sarvi, Majid & Wolshon, Brian & Dixit, Vinayak, 2017. "Identifying critical disruption scenarios and a global robustness index tailored to real life road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 98(C), pages 60-81.
    16. Rinaldi, Marco, 2018. "Controllability of transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 381-406.

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