Asymmetric Driver Behaviour-Based Algorithms for Estimating Real-Time Freeway Operational Capacity

  • Author / Creator
  • To mitigate recurrent and non-recurrent congestion, and to make full use of limited roadway capacity, numerous Active Traffic Demand Management (ATDM) strategies have been proposed, developed and implemented. Segment capacity, a basic input of ATDM predictive models, has been commonly considered a fixed value; however, this consideration does not allow for the probability that complex segment capacity may vary as prevailing traffic conditions vary. Limited research was found that develops analytical models for real-time capacity estimation. This thesis proposes an asymmetric driver behaviour-based algorithm to model multi-lane traffic flow dynamics. By considering car-following and lane-changing behaviours at critical freeway segments, i.e. active bottlenecks and Variable Speed Limit (VSL)-controlled segments, the proposed method obtains real-time freeway operational capacity estimation. The model parameters have been calibrated with field observations taken in Edmonton, Alberta, Canada. The results show that the proposed algorithm accurately estimates real-time operational capacity at complex freeway segments.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • 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
    • Department of Civil and Environmental Engineering
  • Specialization
    • Transportation Engineering
  • Supervisor / co-supervisor and their department(s)
    • Zhi-jun Qiu, Deparment of Civil and Environmental Engineering
  • Examining committee members and their departments
    • Amy kim, Deparment of Civil and Environmental Engineering
    • Majid Khabbazian, Deparment of Electrical and Computer Engineering
    • Zhi-jun Qiu, Deparment of Civil and Environmental Engineering