Dynamic OD estimation using three phase traffic flow theory

Abstract

AbstractAdvanced 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. Copyright © 2010 John Wiley & Sons, Ltd.