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On-Line Estimation of Traffic Densities from Time-Series of Flow and Speed Data

Author

Listed:
  • Denos C. Gazis

    (IBM Thomas J. Watson Research Center, Yorktown Heights, New York)

  • Charles H. Knapp

    (The University of Connecticut, Storrs, Connecticut)

Abstract

A method is discussed for estimating the number of vehicles on a section of a roadway from speed and flow measurements at the entrance and exit points of the section. The method consists of obtaining rough estimates of this count at regular intervals, and then filtering random errors of these estimates by means of a sequential correction scheme. Emphasis is placed on economy in instrumentation and data processing.

Suggested Citation

  • Denos C. Gazis & Charles H. Knapp, 1971. "On-Line Estimation of Traffic Densities from Time-Series of Flow and Speed Data," Transportation Science, INFORMS, vol. 5(3), pages 283-301, August.
    • Handle: RePEc:inm:ortrsc:v:5:y:1971:i:3:p:283-301
    DOI: 10.1287/trsc.5.3.283
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    Cited by:

    1. Coifman, Benjamin, 2003. "Estimating density and lane inflow on a freeway segment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(8), pages 689-701, October.
    2. Jabari, Saif Eddin & Liu, Henry X., 2013. "A stochastic model of traffic flow: Gaussian approximation and estimation," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 15-41.
    3. Zheng, Fangfang & Jabari, Saif Eddin & Liu, Henry X. & Lin, DianChao, 2018. "Traffic state estimation using stochastic Lagrangian dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 143-165.
    4. Herrera, Juan C & Bayen, Alexandre M, 2007. "Traffic flow reconstruction using mobile sensors and loop detector data," University of California Transportation Center, Working Papers qt6v40f0bs, University of California Transportation Center.
    5. Yuan, Yun & Zhang, Zhao & Yang, Xianfeng Terry & Zhe, Shandian, 2021. "Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 88-110.
    6. Herrera, Juan C. & Bayen, Alexandre M., 2010. "Incorporation of Lagrangian measurements in freeway traffic state estimation," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 460-481, May.
    7. Bojovic, Nebojsa J., 2002. "A general system theory approach to rail freight car fleet sizing," European Journal of Operational Research, Elsevier, vol. 136(1), pages 136-172, January.
    8. Coifman, Benjamin & Varaiya, Pravin, 2002. "Deployment and Evaluation of Real-Time Vehicle Reidentification from an Operations Perspective," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6tp5w2gt, Institute of Transportation Studies, UC Berkeley.
    9. Wang, Yibing & Papageorgiou, Markos, 2005. "Real-time freeway traffic state estimation based on extended Kalman filter: a general approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(2), pages 141-167, February.
    10. Guardiola, I.G. & Leon, T. & Mallor, F., 2014. "A functional approach to monitor and recognize patterns of daily traffic profiles," Transportation Research Part B: Methodological, Elsevier, vol. 65(C), pages 119-136.
    11. Ngoduy, D., 2008. "Applicable filtering framework for online multiclass freeway network estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 599-616.
    12. Huang, Hai-Jun & Xia, Tian & Tian, Qiong & Liu, Tian-Liang & Wang, Chenlan & Li, Daqing, 2020. "Transportation issues in developing China's urban agglomerations," Transport Policy, Elsevier, vol. 85(C), pages 1-22.

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