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

• Author / Creator

  Luo,Ying

• 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

  • Asymmetric Driver Behaviour
  • Variable Speed Limit
  • Bottleneck capacity drop

• Graduation date

  Fall 2013

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3N58CV27

• 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.