IDEAS home Printed from https://ideas.repec.org/p/cdl/itsrrp/qt1pv3m9f4.html
   My bibliography  Save this paper

Real-time Density Estimation on Freeway with Loop Detector and Probe Data

Author

Listed:
  • Qiu, Tony Z.
  • Lu, Xiao-Yun
  • Chow, Andy H. F.
  • Shladover, Steven

Abstract

Density, speed and flow are the three critical parameters for traffic analysis. Traffic management and control with high performance require accurate estimation/prediction of distance mean speed and density for large spatial and temporal coverage. Speed, including time mean speed and distance mean speed, and flow estimation are relatively easy to be measured and estimated in the practical site, but accurate density estimation is very difficult. Inductive loop detector systems have been widely deployed, it makes better sense to fully adopt available infrastructure to achieve required traffic measurement. As a new promising technology for transportation system, Vehicle Infrastructure Integration (VII) is developing rapidly with the market penetration of cell phone and GPS systems. This report proposed a method for real-time estimation of density using synchronized loop detector data and VII probe vehicle data. Berkeley Highway Laboratory (BHL) loop detector data and the field collected Probe Vehicle data have been used in the method validation. Density estimated from the vehicle-by-vehicle trajectory tracking in Next Generation Simulation (NGSIM) data has also been used as the second data source for validating the algorithm. Comparison of the two results – that form the loop and VII probe vehicle data and that from NGSIM data, showed that they are very close except a small offset which needs further investigation.

Suggested Citation

  • Qiu, Tony Z. & Lu, Xiao-Yun & Chow, Andy H. F. & Shladover, Steven, 2009. "Real-time Density Estimation on Freeway with Loop Detector and Probe Data," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt1pv3m9f4, Institute of Transportation Studies, UC Berkeley.
  • Handle: RePEc:cdl:itsrrp:qt1pv3m9f4
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/1pv3m9f4.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Andrew Kurkjian & Stanley B. Gershwin & Paul K. Houpt & Alan S. Willsky & E. Y. Chow & C. S. Greene, 1980. "Estimation of Roadway Traffic Density on Freeways Using Presence Detector Data," Transportation Science, INFORMS, vol. 14(3), pages 232-261, August.
    Full references (including those not matched with items on IDEAS)

    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. Coifman, Benjamin A. & Mallika, Ramachandran, 2007. "Distributed surveillance on freeways emphasizing incident detection and verification," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(8), pages 750-767, October.
    2. Li, Baibing, 2010. "Bayesian inference for vehicle speed and vehicle length using dual-loop detector data," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 108-119, January.
    3. Bekiaris-Liberis, Nikolaos & Roncoli, Claudio & Papageorgiou, Markos, 2017. "Highway traffic state estimation per lane in the presence of connected vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 1-28.
    4. Coifman, Benjamin & Ponnu, Balaji & El Asmar, Paul, 2023. "LWR and shockwave analysis - Failures under a concave fundamental diagram and unexpected induced disturbances," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    5. Li, Baibing, 2009. "On the recursive estimation of vehicular speed using data from a single inductance loop detector: A Bayesian approach," Transportation Research Part B: Methodological, Elsevier, vol. 43(4), pages 391-402, May.
    6. 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.
    7. Xu, Chengcheng & Liu, Pan & Wang, Wei & Li, Zhibin, 2014. "Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 58-70.
    8. Coifman, Benjamin, 2015. "Empirical flow-density and speed-spacing relationships: Evidence of vehicle length dependency," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 54-65.
    9. Dailey, D. J., 1999. "A statistical algorithm for estimating speed from single loop volume and occupancy measurements," Transportation Research Part B: Methodological, Elsevier, vol. 33(5), pages 313-322, June.
    10. Jin, Wen-Long, 2010. "A kinematic wave theory of lane-changing traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 1001-1021, September.
    11. Blake Davis & Ang Ji & Bichen Liu & David Levinson, 2020. "Moving Array Traffic Probes," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    12. Li, Xiang & Sun, Jian-Qiao, 2017. "Studies of vehicle lane-changing dynamics and its effect on traffic efficiency, safety and environmental impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 41-58.
    13. Bakibillah, A.S.M. & Tan, Yong Hwa & Loo, Junn Yong & Tan, Chee Pin & Kamal, M.A.S. & Pu, Ziyuan, 2022. "Robust estimation of traffic density with missing data using an adaptive-R extended Kalman filter," Applied Mathematics and Computation, Elsevier, vol. 421(C).

    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:cdl:itsrrp:qt1pv3m9f4. 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: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucbus.html .

    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.