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Predicting travel time to limit congestion at a highway bottleneck

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  • Davis, L.C.

Abstract

A new method is proposed to predict the travel time on a highway route with a bottleneck caused by an on-ramp. The method takes advantage of the slow variation of the bottleneck throughput when congestion exists. The predicted travel time for a vehicle leaving the origin is given by the current number of vehicles on the route divided by the estimated throughput. The latter is an average of N/T recorded as each vehicle reaches the destination where N is the number of vehicles at the start of the trip and T is the time to complete the trip. Drivers divert to an off-ramp when the predicted travel time exceeds a target value. The target could be historical average travel times of alternative routes or chosen to limit the amount of congestion. Simulations employing three-phase traffic theory show that the travel time converges to the target value and remains close to or below it with the proposed prediction strategy. Strong oscillations in travel time obtained when other strategies are used for diversion do not develop with the new method because the inherent delay is effectively removed.

Suggested Citation

  • Davis, L.C., 2010. "Predicting travel time to limit congestion at a highway bottleneck," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3588-3599.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:17:p:3588-3599
    DOI: 10.1016/j.physa.2010.04.016
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    Citations

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    Cited by:

    1. Davis, L.C., 2012. "Mitigation of congestion at a traffic bottleneck with diversion and lane restrictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1679-1691.
    2. Rehborn, Hubert & Klenov, Sergey L. & Palmer, Jochen, 2011. "An empirical study of common traffic congestion features based on traffic data measured in the USA, the UK, and Germany," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4466-4485.
    3. MacGregor Smith, J. & Cruz, F.R.B., 2014. "M/G/c/c state dependent travel time models and properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 560-579.
    4. Rajesh S. Prabhu Gaonkar & V. Mariappan, 2020. "Transportation time reliability appraisal in maritime context," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 736-746, June.
    5. Shao, Feng & Shao, Hu & Wang, Dongle & Lam, William H.K. & Cao, Shuhan, 2023. "A generative model for vehicular travel time distribution prediction considering spatial and temporal correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    6. Shao, Feng & Shao, Hu & Wang, Dongle & Lam, William H.K., 2024. "A multi-task spatio-temporal generative adversarial network for prediction of travel time reliability in peak hour periods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    7. Rajesh S. Prabhu Gaonkar & V. Mariappan, 0. "Transportation time reliability appraisal in maritime context," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-11.

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