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Optimization of traffic sensor location for complete link flow observability in traffic network considering sensor failure

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  • Salari, Mostafa
  • Kattan, Lina
  • Lam, William H.K.
  • Lo, H.P.
  • Esfeh, Mohammad Ansari

Abstract

The full link flow observability problem is to identify the minimum set of traffic sensors to be installed in links in a road traffic network. The sensors are used to both monitor the flow of observed links and to provide flow information for the link flow inference of unobserved links. Unavoidably, the traffic sensors deployed in a traffic network are subject to failure which leads to missing the link flow observation of observed links as well as the inability to infer the link flow of unobserved links. This study aims to identify the minimum set of links in a traffic network to be instrumented with two different types of counting sensors (basic and advanced sensors) to reach full link flow observability while minimizing the effect of sensor failure on the link flow inference of unobserved links. Mathematically, we formulate two objective functions including min-max and min-sum functions. The first function attempts to minimize the maximum effect of sensor failure on the link flow inference of unobserved links while the second one minimizes the expected number of unobserved links where flow cannot be inferred due to the failure of sensors. We select the genetic algorithm (GA) as a well-known heuristic to solve the proposed optimization model. The results recommend minimizing the number of sensors required for the link flow inference of each unobserved link as well as installing advanced sensors on links involved in the link flow inference of multiple unobserved links. We also develop a new objective function to reflect that links in a traffic network can be either minor or major roads with different levels of importance. The results suggest installing more advanced sensors on the major roads as well as minimizing the number of major roads included in the set of unobserved links. Concerning the availability of route flow information in a network, we consider the effect of this information on evaluating the sensor deployment in a network. To maintain full link flow observability of a traffic network if any sensor fails, we study the location and type of additional sensors introduced as redundant sensors, which are more than the minimum required for full link flow observability. Finally, we discuss the applicability of the proposed model for the partial observability problem in which the full link flow observability conditions are not satisfied.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:121:y:2019:i:c:p:216-251
    DOI: 10.1016/j.trb.2019.01.004
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Chootinan, Piya & Chen, Anthony, 2011. "Confidence interval estimation for path flow estimator," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1680-1698.
    6. 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.
    7. 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.
    8. David Morrison & Susan Martonosi, 2015. "Characteristics of optimal solutions to the sensor location problem," Annals of Operations Research, Springer, vol. 226(1), pages 463-478, March.
    9. 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.
    10. Mínguez, R. & Sánchez-Cambronero, S. & Castillo, E. & Jiménez, P., 2010. "Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 282-298, February.
    11. 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.
    12. 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.
    13. Lucio Bianco & Giuseppe Confessore & Monica Gentili, 2006. "Combinatorial aspects of the sensor location problem," Annals of Operations Research, Springer, vol. 144(1), pages 201-234, April.
    14. Ehlert, Anett & Bell, Michael G.H. & Grosso, Sergio, 2006. "The optimisation of traffic count locations in road networks," Transportation Research Part B: Methodological, Elsevier, vol. 40(6), pages 460-479, July.
    15. Lucio Bianco & Giuseppe Confessore & Pierfrancesco Reverberi, 2001. "A Network Based Model for Traffic Sensor Location with Implications on O/D Matrix Estimates," Transportation Science, INFORMS, vol. 35(1), pages 50-60, February.
    16. 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.
    17. 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.
    18. Li, Xiaopeng & Ouyang, Yanfeng, 2011. "Reliable sensor deployment for network traffic surveillance," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 218-231, January.
    19. Chen, Anthony & Chootinan, Piya & Recker, Will, 2009. "Norm approximation method for handling traffic count inconsistencies in path flow estimator," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 852-872, September.
    20. Xuesong Zhou & George F. List, 2010. "An Information-Theoretic Sensor Location Model for Traffic Origin-Destination Demand Estimation Applications," Transportation Science, INFORMS, vol. 44(2), pages 254-273, May.
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