Policy-based stochastic dynamic traffic assignment models and algorithms

Stochasticity is prevalent in transportation networks in general, and traffic networks in particular. We develop a policy-based stochastic dynamic traffic assignment (DTA) model and related solution algorithms. The DTA model works in a stochastic time-dependent network where link travel times are time-dependent random variables, Routing policies rather than paths are used as users´ routing choices. A routing policy is a decision rule which specifies what node to take next out of current node based on current time and realized link travel times. We first give a conceptual framework for the DTA model. We then develop generic models for the routing policy generation problem, users´ policy choice problem and dynamic network loading problem, which are the three major components of the overall DTA model. We then present a heuristic algorithm to solve the proposed policy-based DTA model. Using an example, we show that policy-based DTA models have solutions different, in expected travel times than the path-based models which are commonly used in the literature.