[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v35y2001i7p695-716.html
   My bibliography  Save this article

Stochastic modeling and real-time prediction of vehicular lane-changing behavior

Author

Listed:
  • Sheu, Jiuh-Biing
  • Ritchie, Stephen G.
Abstract
Time-varying lane-changing fractions and queue lengths are important lane traffic characteristics which may exhibit significant changes in the presence of a lane-blocking incident. This paper describes a stochastic system modeling approach to estimate time-varying lane-changing fractions and queue lengths for real-time incident management on surface streets. A discrete-time nonlinear stochastic model, which consists of recursive equations, measurement equations, and boundary constraints, is proposed to characterize inter-lane and intra-lane traffic state variables during incidents. To estimate lane-changing fractions and other state variables of the model, a recursive estimation algorithm is developed which primarily involves an extended Kalman filter, truncation, normalization, and a queue-updating procedure. Lane traffic counts are the sole input data used in this method. These data can be readily collected from conventional point detectors. The proposed model was calibrated using video-based data, then tested using simulated data from the TRAF-NETSIM simulation model, Version 5.0, as well as real video-based data sets. Preliminary test results indicate the feasibility of employing the proposed approach to estimate time-varying mandatory lane-changing fractions as well as queue lengths during incidents. The estimated lane-changing fractions and queue lengths can be used not only in better understanding the phenomena of incident-related inter-lane and intra-lane traffic characteristics, but also in developing real-time incident management technologies. Moreover, it is hoped that the results of this study might contribute to future research in related areas such as incident traffic prediction, incident-responsive traffic control and management, and automatic road congestion warning systems for further use in advanced transportation management and information systems.

Suggested Citation

  • Sheu, Jiuh-Biing & Ritchie, Stephen G., 2001. "Stochastic modeling and real-time prediction of vehicular lane-changing behavior," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 695-716, August.
    • Handle: RePEc:eee:transb:v:35:y:2001:i:7:p:695-716
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191-2615(00)00015-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sheu, Jiuh-Biing & Yang, Hai, 2008. "An integrated toll and ramp control methodology for dynamic freeway congestion management," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4327-4348.
    2. Coifman, Benjamin, 2006. "Extracting More Information from the Existing Freeway Traffic Monitoring Infrastructure," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt34n479gz, Institute of Transportation Studies, UC Berkeley.
    3. Bert van Wee, 2009. "Self‐Selection: A Key to a Better Understanding of Location Choices, Travel Behaviour and Transport Externalities?," Transport Reviews, Taylor & Francis Journals, vol. 29(3), pages 279-292, January.
    4. Yuan, Renteng & Abdel-Aty, Mohamed & Gu, Xin & Zheng, Ou & Xiang, Qiaojun, 2023. "A unified modeling framework for lane change intention recognition and vehicle status prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    5. Sheu, Jiuh-Biing, 2007. "Microscopic modeling and control logic for incident-responsive automatic vehicle movements in single-automated-lane highway systems," European Journal of Operational Research, Elsevier, vol. 182(2), pages 640-662, October.
    6. Sheu, Jiuh-Biing, 2006. "A composite traffic flow modeling approach for incident-responsive network traffic assignment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 461-478.
    7. Maria Johansson & Catharina Sternudd & Mattias Kärrholm, 2016. "Perceived urban design qualities and affective experiences of walking," Journal of Urban Design, Taylor & Francis Journals, vol. 21(2), pages 256-275, April.
    8. Zheng, Zuduo, 2014. "Recent developments and research needs in modeling lane changing," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 16-32.
    9. Liu, Ronghui & Van Vliet, Dirck & Watling, David, 2006. "Microsimulation models incorporating both demand and supply dynamics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(2), pages 125-150, February.
    10. Jiuh-Biing Sheu, 2003. "A Stochastic Modeling Approach to Real-Time Prediction of Queue Overflows," Transportation Science, INFORMS, vol. 37(1), pages 97-119, February.
    11. Sheu, Jiuh-Biing, 2004. "A sequential detection approach to real-time freeway incident detection and characterization," European Journal of Operational Research, Elsevier, vol. 157(2), pages 471-485, September.
    12. 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.
    13. Jiuh-Biing Sheu, 2003. "Erratum: A Stochastic Modeling Approach to Real-Time Prediction of Queue Overflows," Transportation Science, INFORMS, vol. 37(2), pages 230-252, May.
    14. Jianrong Cai & Zhixue Li & Yinghong Xiao & Zhaoming Zhou & Qiong Long & Jie Yu & Jinfan Zhang & Lei Zhang, 2023. "Reversible Lane Optimization of the Urban Road Network Considering Adjustment Time Constraints," Sustainability, MDPI, vol. 15(2), pages 1-11, January.
    15. Chai, Linguo & Liu, Xiangyan & ShangGuan, Wei & Wang, Jian & Cai, Baigen, 2023. "Parallel spatiotemporal slot-based heterogeneous vehicle hybrid coordinating method at intersections under intelligent network environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).

    More about this item

    Statistics

    Access and download statistics

    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:eee:transb:v:35:y:2001:i:7:p:695-716. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    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.