Modeling highway traffic volumes

Abstract

Most traffic management and optimization tasks, such as accident detection or optimal vehicle routing, require an ability to adequately model, reason about and predict irregular and stochastic behavior. Our goal is to create a probabilistic model of traffic flows on highway networks that is realistic from the point of applications and at the same time supports efficient learning and inference. We study several multivariate probabilistic models and analyze their respective strengths. To balance accuracy and efficiency, we propose a novel learning model, mixture of Gaussian trees, and show its advantages in learning and inference. AU models are evaluated on real-world traffic flow data from highways of the Pittsburgh area. © Springer-Verlag Berlin Heidelberg 2007.