A Hybrid Deployable Dynamic Traffic Assignment Framework for Robust Online Route Guidance

Randomness in time-dependent origin-destination (O-D) demands and/or network supply conditions, and the computational tractability of potential solution methodologies are two major concerns for the online deployment of dynamic traffic assignment (DTA) under real-time traffic management systems. Most existing DTA models ignore these concerns and/or make unrealistic assumptions, precluding their online applicability. In this paper, a hybrid approach consisting of offline and online strategies is proposed to address the online stochastic dynamic traffic assignment problem. The basic idea is to address the computationally intensive components offline, while efficiently and effectively reacting to the unfolding conditions online. The offline component seeks a robust initial solution vis-à-vis randomness in O-D demands using historical O-D demand data. Termed the offline a priori solution, it is updated dynamically online based on unfolding O-D demands and incidents. The framework circumvents the need for accurate O-D demand and incident likelihood prediction models online, while exploiting historical O-D demand and incident data offline. Results of simulation experiments highlight the robustness of the hybrid approach with respect to online variations in O-D demand, its ability to address incident situations effectively, and its online efficiency.