Stochastic User Equilibrium Quasi-Dynamic Traffic Assignment Model with Time-Varying Demand

Currently, there is a large amount of literature studying dynamic traffic assignment but applying dynamic traffic assignment methods to large-scale networks tends to incur excessive computational costs and consumes a lot of time. The static traffic assignment also does not consider the traffic variation over time leading to a large difference between the assignment results and the reality. This paper adopts a quasi-dynamic assignment method and proposes a new method for calculating residual traffic demand, which considers the propagation of residual traffic demand between time periods and also establishes a Logit-based stochastic user equilibrium assignment model considering the different familiarity of travelers with the road network. Finally, the application of the model is illustrated through the Braess network. The research results enrich the basic theory of traffic science and can provide a reference basis for the formulation of traffic policies.