Dynamic OD estimation using three phase traffic flow theory

Advanced Transportation Management and Information Systems (ATMIS) can use dynamic origin-destination (OD) demand models to make short-term predictions regarding developments in traffic states. However, existing dynamic OD prediction models do not achieve this reliably for two main reasons. First, this is a bi-level system that consists of a traffic flow process at the lower level and a dynamic OD process at the upper level. Due to the inherent non-convexity of bi-level systems, it is difficult to guarantee that any calculated solution is globally optimal. In this paper, we propose a new traffic flow model that uses real-time traffic data, such as traffic flows, speed and occupancy, collected from vehicle detectors, to address the difficulties that arise in existing bi-level programming formulations. Second, in order to estimate a dynamic OD demand between on and off-ramps on the freeways, a traffic flow model is needed to estimate the proportion of traffic moving between them. In this paper, we present a dynamic traffic estimation model based on Kerner's 1 three-phase traffic theory, which represents the complexity of traffic phenomena based on phase transitions between free-flow, synchronized flow and moving jam phases, and on their complex nonlinear spatio-temporal features. The present model explains and estimates traffic congestion in terms of speed breakdown, phase transition and queue propagation. We show how a genetic algorithm can be used to solve this to estimate dynamic OD flows and the associated link, on and off-ramp flows during each time interval using traffic data collected from vehicle detection systems implemented on Korean freeways.