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An Information-Theoretic Sensor Location Model for Traffic Origin-Destination Demand Estimation Applications

Author

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  • Xuesong Zhou

    (Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, Utah 84112)

  • George F. List

    (Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, North Carolina 27695)

Abstract

To design a transportation sensor network, the decision maker needs to determine what sensor investments should be made, as well as when, how, where, and with what technologies. This paper focuses on locating a limited set of traffic counting stations and automatic vehicle identification (AVI) readers in a network, so as to maximize the expected information gain for the subsequent origin-destination (OD) demand estimation problem. The proposed sensor design model explicitly takes into account several important error sources in traffic OD demand estimation, such as the uncertainty in historical demand information, sensor measurement errors, as well as approximation errors associated with link proportions. Based on a mean square measure, this paper derives analytical formulations to describe estimation variance propagation for a set of linear measurement equations. A scenario-based (SB) stochastic optimization procedure and a beam search algorithm are developed to find suboptimal point and point-to-point sensor locations subject to budget constraints. This paper also provides a number of illustrative examples to demonstrate the effectiveness of the proposed methodology.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ortrsc:v:44:y:2010:i:2:p:254-273
    DOI: 10.1287/trsc.1100.0319
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    References listed on IDEAS

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    1. Bierlaire, Michel, 2002. "The total demand scale: a new measure of quality for static and dynamic origin-destination trip tables," Transportation Research Part B: Methodological, Elsevier, vol. 36(9), pages 837-850, November.
    2. 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.
    3. Maher, M. J., 1983. "Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach," Transportation Research Part B: Methodological, Elsevier, vol. 17(6), pages 435-447, December.
    4. 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.
    5. Yang, Hai & Iida, Yasunori & Sasaki, Tsuna, 1991. "An analysis of the reliability of an origin-destination trip matrix estimated from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 25(5), pages 351-363, October.
    6. Yang, Hai & Zhou, Jing, 1998. "Optimal traffic counting locations for origin-destination matrix estimation," Transportation Research Part B: Methodological, Elsevier, vol. 32(2), pages 109-126, February.
    7. Sherali, Hanif D. & Desai, Jitamitra & Rakha, Hesham, 2006. "A discrete optimization approach for locating Automatic Vehicle Identification readers for the provision of roadway travel times," Transportation Research Part B: Methodological, Elsevier, vol. 40(10), pages 857-871, December.
    8. Cascetta, Ennio, 1984. "Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator," Transportation Research Part B: Methodological, Elsevier, vol. 18(4-5), pages 289-299.
    9. Jon Lee, 1998. "Constrained Maximum-Entropy Sampling," Operations Research, INFORMS, vol. 46(5), pages 655-664, October.
    10. Bell, M. G. H., 1985. "Variances and covariances for origin-destination flows when estimated by log-linear models," Transportation Research Part B: Methodological, Elsevier, vol. 19(6), pages 497-507, December.
    11. Cascetta, Ennio & Nguyen, Sang, 1988. "A unified framework for estimating or updating origin/destination matrices from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 22(6), pages 437-455, December.
    12. K. Ashok & M. E. Ben-Akiva, 2000. "Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin–Destination Flows," Transportation Science, INFORMS, vol. 34(1), pages 21-36, February.
    13. Van Zuylen, Henk J. & Willumsen, Luis G., 1980. "The most likely trip matrix estimated from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 281-293, September.
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    Cited by:

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    2. Fu, Hao & Lam, William H.K. & Shao, Hu & Kattan, Lina & Salari, Mostafa, 2022. "Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    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. 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.
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    9. Yong Lin, 2023. "Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System," Sustainability, MDPI, vol. 15(2), pages 1-30, January.
    10. Fu, Hao & Lam, William H.K. & Shao, Hu & Ma, Wei & Chen, Bi Yu & Ho, H.W., 2022. "Optimization of multi-type sensor locations for simultaneous estimation of origin-destination demands and link travel times with covariance effects," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 19-47.
    11. Li, Ruolin & Mehr, Negar & Horowitz, Roberto, 2023. "Submodularity of optimal sensor placement for traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 171(C), pages 29-43.
    12. Xing, Tao & Zhou, Xuesong & Taylor, Jeffrey, 2013. "Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: An information-theoretic modeling approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 66-90.
    13. Deng, Wen & Lei, Hao & Zhou, Xuesong, 2013. "Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 132-157.
    14. 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.
    15. 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.
    16. Hyoshin (John) Park & Ali Haghani & Song Gao & Michael A. Knodler & Siby Samuel, 2018. "Anticipatory Dynamic Traffic Sensor Location Problems with Connected Vehicle Technologies," Service Science, INFORMS, vol. 52(6), pages 1299-1326, December.
    17. 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.
    18. Liu, Jiangtao & Zhou, Xuesong, 2019. "Observability quantification of public transportation systems with heterogeneous data sources: An information-space projection approach based on discretized space-time network flow models," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 302-323.
    19. Shi An & Lina Ma & Jian Wang, 2020. "Optimization of Traffic Detector Layout Based on Complex Network Theory," Sustainability, MDPI, vol. 12(5), pages 1-22, March.
    20. 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.

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