Vehicle trajectory reconstruction using automatic vehicle identification and traffic count data

The origin-destination (OD) matrix and the vehicle trajectory data are critical to transportation planning, design, and operation management. On the basis of the deployment of automatic vehicle identification (AVI) technology in urban traffic networks in China, this study proposed a vehicle trajectory reconstruction method for a large-scale network by using AVI and traditional detector data. Particle filter theory was employed as the framework for this method that combines five spatial-temporal trajectory correction factors (i.e., the path consistency, the AVI measurability criterion, the travel time consistency, the gravity flow model, and the path-link flow matching model) to estimate the trajectory of a vehicle. The probabilities of the most likely trajectories are updated by the Bayesian method to approximate the true' trajectory. The traffic network in the Beijing Olympic Park was selected as the test bed and was simulated by using VISSIM to create a complete set of vehicle trajectories. The accuracy of the resulting trajectory reconstruction exceeds 90% when the AVI coverage is only 50%, assuming an AVI detection error of 5% for a closed network and an open network. The average relative error of a static OD matrix is 11.05%. Although the accuracy of reconstruction exceeds 80% when the AVI coverage is between 50% and 40%, the accuracy of a defective product-OD matrix decreases rapidly. The proposed method yields high estimation accuracy for the full trajectories of individual vehicles and the OD matrix, which demonstrates significant potential for traffic-related applications. Copyright (c) 2014 John Wiley & Sons, Ltd.