Usage
  • 24 views
  • 23 downloads

Development and Evaluation of Model-Based Adaptive Signal Control for Congested Arterial Traffic

  • Author / Creator
    Liu, Gang
  • Under congested conditions, the road traffic states of different arterial links will interact with each other; therefore, it is necessary to understand the behavior of traffic corridors and to investigate corridor-wide traffic coordinated control strategies. In order to achieve this, traffic flow models are applied in signal control to predict future traffic states. Optimization tools are used to search for the best sequence of future control decisions, based on predictions by traffic flow models. A number of model-based adaptive control strategies have been presented in the literature and have been proved effective in practice. However, most studies have modeled the traffic dynamic either at a link-based level or at an individual movement-based level. Moreover, the efficiency of corridor-wide coordination algorithms for congested large-scale networks still needs to be further improved. A hierarchical control structure is developed to divide the complex control problem into different control layers: the highest level optimizes the cycle length, the mid layer optimizes the offsets, and the Model Predictive Control (MPC) procedure is implemented in the lowest layer to optimize the split. In addition, there is an extra multi-modal priority control layer to provide priority for different travel modes. Firstly, MPC is applied to optimize the signal timing plans for arterial traffic. The objectives are to increase the throughput. A hybrid urban traffic flow model is proposed to provide relatively accurate predictions of the traffic state dynamic, which is capable of simulating queue evolutions among different lane groups in a specific link. Secondly, this study expands the dynamic queue concept to the corridor-wide coordination problem. The ideal offset and boundary offsets to avoid spillback and starvation are found based on the shockwave profiles at each signalized intersection. A new multi-objective optimization model based on the preemptive goal programming is proposed to find the optimal offset. Thirdly, the priority control problem is formulated into a multi-objective optimization model, which is solved with a Non-dominated Sorting Genetic Algorithm. Pareto-optimal front results are presented to evaluate the trade-off among different objectives and the most appropriate solution is chosen with high-level information. Performance of the new adaptive controller is verified with software-in-the-loop simulation. The applied simulation environment contains VISSIM with the ASC/3 module as the simulation environment and the control system as the solver. The simulation test bed includes two arterial corridors in Edmonton, Alberta. The simulation network was well calibrated and validated. The simulation results show that the proposed adaptive control methods outperform actuated control in increasing throughput, decreasing delay, and preventing queue spillback.

  • Subjects / Keywords
  • Graduation date
    2015-11
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3736M938
  • 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
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Civil and Environmental Engineering
  • Specialization
    • Transportation Engineering
  • Supervisor / co-supervisor and their department(s)
    • Qiu, Tony Z. (Department of Civil and Environmental Engineering)
  • Examining committee members and their departments
    • Kumar, Amit (Department of Mechanical Engineering)
    • El-Basyouny, Karim (Department of Civil and Environmental Engineering)
    • Liu, Jinfeng (Department of Chemical and Materials Engineering)
    • Kim, Amy (Department of Civil and Environmental Engineering)