IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v51y2013icp62-66.html
   My bibliography  Save this article

Partial link flow observability in the presence of initial sensors: Solution without path enumeration

Author

Listed:
  • Ng, ManWo

Abstract

Recently, a new methodology (“synergistic sensor location”) has been introduced to efficiently determine all link flows in a road network by using only a subset of the link flow measurements. In this paper, we generalize this previous work by solving the following problem: Suppose that one is only interested in a subset of the link flows, and that certain link flows are known a priori. At a minimum, what link flows are needed to be able to uniquely determine the desired link flows? An algorithm is presented that does not require the need for path enumeration.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transe:v:51:y:2013:i:c:p:62-66
    DOI: 10.1016/j.tre.2012.12.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554512001020
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2012.12.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hu, Shou-Ren & Peeta, Srinivas & Chu, Chun-Hsiao, 2009. "Identification of vehicle sensor locations for link-based network traffic applications," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 873-894, September.
    2. 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.
    3. Ng, ManWo & Waller, S. Travis, 2010. "Reliable evacuation planning via demand inflation and supply deflation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(6), pages 1086-1094, November.
    4. Duanmu, Jun & Chowdhury, Mashrur & Taaffe, Kevin & Jordan, Craig, 2012. "Buffering in evacuation management for optimal traffic demand distribution," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(3), pages 684-700.
    5. Ng, ManWo & Waller, S. Travis, 2010. "A computationally efficient methodology to characterize travel time reliability using the fast Fourier transform," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1202-1219, December.
    6. Szeto, W. Y. & Lo, Hong K., 2004. "A cell-based simultaneous route and departure time choice model with elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 593-612, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu, Xinyao & Ma, Shoufeng & Zhu, Ning & Lam, William H.K. & Fu, Hao, 2023. "Ensuring the robustness of link flow observation systems in sensor failure events," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    2. 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.
    3. 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.
    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 & 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.
    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. 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.
    8. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    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. 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.
    5. 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.
    6. Long, Jiancheng & Szeto, W.Y. & Gao, Ziyou & Huang, Hai-Jun & Shi, Qin, 2016. "The nonlinear equation system approach to solving dynamic user optimal simultaneous route and departure time choice problems," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 179-206.
    7. Ng, ManWo & Khattak, Asad & Talley, Wayne K., 2013. "Modeling the time to the next primary and secondary incident: A semi-Markov stochastic process approach," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 44-57.
    8. Ng, ManWo & Diaz, Rafael & Behr, Joshua, 2015. "Departure time choice behavior for hurricane evacuation planning: The case of the understudied medically fragile population," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 215-226.
    9. 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.
    10. Zhaoqi Zang & Xiangdong Xu & Kai Qu & Ruiya Chen & Anthony Chen, 2022. "Travel time reliability in transportation networks: A review of methodological developments," Papers 2206.12696, arXiv.org, revised Jul 2022.
    11. 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.
    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. 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.
    14. 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.
    15. 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.
    16. Kundu, Tanmoy & Sheu, Jiuh-Biing & Kuo, Hsin-Tsz, 2022. "Emergency logistics management—Review and propositions for future research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    17. Yu, Xinyao & Ma, Shoufeng & Zhu, Ning & Lam, William H.K. & Fu, Hao, 2023. "Ensuring the robustness of link flow observation systems in sensor failure events," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    18. 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.
    19. 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.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:51:y:2013:i:c:p:62-66. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.