Perception Updating Based Stochastic Dynamic Assignment Model

Traffic network systems endogenously display both stochasticities and dynamics with vehicle flows formed from aggregated travelers in response to their previous experiences and the information provided by Advanced Traveler Information Systems (ATIS). In consideration of the day-to-day trip-making decisions of travelers, a perception-updating-based stochastic dynamic assignment model is proposed to describe the evolution of the traffic network flow pattern. In this model, the route flows are treated explicitly as random variables and the distribution of them is proved to asymptotically converge to a stationary probability distribution. A simulation algorithm is developed for implementing the model. Numerical results under two scenarios, with and without ATIS, are also provided for comparing the stochastic dynamics of route flows.