Strategic dynamic traffic assignment incorporating travel demand uncertainty

Dynamic traffic assignment (DTA) research has advanced greatly in terms of deployability, computational feasibility, and representing complex temporal phenomena. There have also been substantial contributions regarding various aspects of stochasticity within DTA. However, there are persisting limitations in terms of approaches which are both computationally tractable and provide more detailed representation of stochastic aspects. This paper explores the application of a novel Strategic User Equilibrium DTA (StrDTA) modelling framework, which captures the impact of users making a priori route choice decisions based on the knowledge of a range of possible travel demand scenarios (e.g. differing days or representative situations). The resulting stochastic DTA problem becomes complex due to the integration of multiple demand scenarios and the algorithmic adjustments necessary to find optimal paths. A new solution framework is proposed which still permits implementation, and a detailed case study is presented for the Sydney Central Business District network. Results demonstrate the importance of accounting for stochasticity in the routing algorithm rather than relying on assumptions of average values.