An Analytical Dynamic Traffic Assignment Model with Probabilistic Travel Times and Travelers' Perceptions

Dynamic traffic assignment (DTA) has been a topic of substantial research during the past decade. While DTA is gradually maturing, many aspects of DTA still need improvements, especially regarding its formulation and solution capabilities under the transportation environment impacted by the Advanced Transportation Management and Information Systems (ATMIS). It is necessary to develop a set of DTA models to acknowledge the fact that the traffic network itself is probabilistic and uncertain, and different classes of travelers respond differently under uncertain environment, given different levels of traffic information. This paper aims to advance the state-of-the-art in DTA modeling in the sense that the proposed model captures the travelers(tm) decision making among discrete choices in a probabilistic and uncertain environment, in which both probabilistic travel times and random perception errors that are specific to individual travelers, are considered. Travelers(tm) route choices are assumed to be made with the objective of minimizing perceived disutilities at each time. These perceived disutilities depend on the distribution of the variable route travel times, the distribution of individual perception errors and the individual traveler(tm)s risk taking nature at each time instant. We formulate the integrated DTA model through a variational inequality (VI) approach. Subsequently, we discuss the solution algorithm for the formulation. Experimental results are also given to verify the correctness of solutions obtained.