| 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. |
