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Recursive Estimation of Traffic Variables: Section Density and Average Speed

Author

Listed:
  • N. E. Nahi

    (University of Southern California, Los Angeles, California)

  • A. N. Trivedi

    (University of Southern California, Los Angeles, California)

Abstract

Simultaneous recursive estimators for aggregate variables, section density, and mean speed, are derived. The estimators are simple and easily suitable for digital implementation. Performances of the estimators are evaluated utilizing the real traffic data obtained over a three-quarter mile section of the Long Island Expressway, New York, and data obtained from digital simulation of freeway traffic.

Suggested Citation

  • N. E. Nahi & A. N. Trivedi, 1973. "Recursive Estimation of Traffic Variables: Section Density and Average Speed," Transportation Science, INFORMS, vol. 7(3), pages 269-286, August.
    • Handle: RePEc:inm:ortrsc:v:7:y:1973:i:3:p:269-286
    DOI: 10.1287/trsc.7.3.269
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

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