Variable Speed Limit Control to Mitigate Freeway Congestion

  • Author / Creator
    Hadiuzzaman, Md
  • Over the past few decades, several active traffic control methods have been developed and implemented to mitigate freeway congestion. Among them, Variable Speed Limit (VSL) is considered the most efficient control method. In addition, the latest advances in Intelligent Transportation Systems (ITS) have made it feasible to implement predictive freeway control. The successful implementation of such control requires an accurate macroscopic traffic flow model that can predict all the important traffic dynamics.

    To avoid violation of the equilibrium traffic state assumption and to improve traffic state prediction accuracy in the VSL control situation, this research proposes a 2nd order model, DynaTAM-VSL, which drops parameterization of the METANET’s FD; instead, it includes speed limit-dependent parameters in the speed and density dynamics. The validation results with the 20-s loop detector data confirmed that, compared to the existing models, the proposed model better simulates traffic flow. With the validated model, this research investigates the impact of control parameters and demand levels on total travel time and throughput under the coordinated VSL control and determined a range of the demand / bottleneck capacity ratio, when VSL simultaneously improves both of the mobility parameters, which resolved the existing paradoxical results.

    This research also proposes an isolated VSL control strategy that aims at avoiding capacity drop at recurrent freeway bottlenecks. To evaluate the effectiveness of the control strategy, a base model of the 11-km test site: Whitemud Drive (WMD), Edmonton is calibrated within a microscopic traffic flow simulator to reproduce real-world traffic conditions, while the control strategy is implemented to evaluate its impact. The sensitivity analysis of the control strategy on safety constraints and VSL update frequencies demonstrates promising results to support practical implementation.

    Considering its flexible use in macroscopic simulation, a 1st order traffic flow model, CTM-VSL, is proposed. Unlike the 2nd order models, it is parsimonious: it only includes parameters that can be estimated using routinely available point detector data. However, the model is valid only for the condition of perfect compliance by drivers to VSL control, since it shares same properties of the CTM model. To update the storage capacity of an upstream segment of a VSL sign, a real-time queue estimation model is proposed. Despite the simple structure of the CTM-VSL model, the VSL control shows comparable results with the DynaTAM-VSL in terms of improving mobility parameters.

    Finally, this research distinguishes the relative contributions of driver compliance levels (CLs) and a predictive VSL control with different CLs to improve traffic flows. Several CL-to-VSL strategies are modeled with a fixed co-efficient of variance of speeds obtained from static speed limit on WMD. The CLs include speed distributions for aggressive, compliant, and defensive drivers. It is proven that the mobility benefits from the VSL control are not at the expense of increased collision probability and vice-versa.

  • Subjects / Keywords
  • Graduation date
    Spring 2014
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
  • Specialization
    • Transportation Engineering
  • Supervisor / co-supervisor and their department(s)
  • Examining committee members and their departments
    • Chen, Yongsheng (City of Edmonton)
    • El-Basyouny, Karim (Department of Civil and Environmental Engineering)
    • Kim, Amy (Department of Civil and Environmental Engineering)
    • Liu, Jinfeng (Department of Chemical and Materials Engineering)
    • Zhong, Ming (Wuhan University of Technology)