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A Stochastic Modeling Approach to Real-Time Prediction of Queue Overflows

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  • Jiuh-Biing Sheu

    (Institute of Traffic and Transportation, National Chiao Tung University, 4F, 114 Chung Hsiao W. Road, Sec. 1, Taipei, Taiwan 10012)

Abstract

Queue overflow is a critical issue in developing queue prediction technologies for applications in Advanced Transportation Management System (ATMS). Conventional queue prediction methods, however, are limited to incident-free queue length prediction where traffic arrivals can be readily obtained using detectors. Despite the problems posed by queue overflow, studies addressing queue-overflow issues, or for predicting queue overflows beyond detectors, appear inadequate. This paper describes an advanced methodology which uses a stochastic system modeling approach and random processes for predicting queue lengths beyond detectors in real time. Lane changing is taken into account in developing the queue-overflow prediction model because lane changing accompanies queue overflow in most cases. A discrete-time, nonlinear stochastic system is specified for modeling the queues and lane changes beyond detectors during queue-overflow occurrence. The noise terms of the recursive equations of the model account for the effects of queues and a variety of arriving volumes on vehicular lane-changing maneuvers during queue-overflow occurrence. The unknown traffic arrivals beyond detectors are predicted employing random processes. In addition, a recursive estimation algorithm for predicting real-time queue overflows is developed utilizing the extended Kalman filtering technique. Preliminary test results indicate that the proposed methodology is promising for real-time prediction of queue overflows. The predicted queue overflows can be used not only in understanding the phenomenon of lane traffic patterns during queue-overflow occurrence, but also in developing related advanced technologies such as real-time road traffic congestion control and management systems.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ortrsc:v:37:y:2003:i:1:p:97-119
    DOI: 10.1287/trsc.37.1.97.12816
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    References listed on IDEAS

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    1. Sheu, Jiuh-Biing & Chou, Yi-Hwa & Shen, Liang-Jen, 2001. "A stochastic estimation approach to real-time prediction of incident effects on freeway traffic congestion," Transportation Research Part B: Methodological, Elsevier, vol. 35(6), pages 575-592, July.
    2. 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.
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    Citations

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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. Comert, Gurcan, 2013. "Effect of stop line detection in queue length estimation at traffic signals from probe vehicles data," European Journal of Operational Research, Elsevier, vol. 226(1), pages 67-76.
    3. Jiuh-Biing Sheu, 2002. "A Stochastic Optimal Control Approach to Real-time, Incident-Responsive Traffic Signal Control at Isolated Intersections," Transportation Science, INFORMS, vol. 36(4), pages 418-434, November.
    4. 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.

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