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

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  • 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
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    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).

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