Management Science
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Learning with mixtures of trees
Learning with mixtures of trees
Urban traffic modelling and prediction using large scale taxi GPS traces
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
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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. All models are evaluated on real-world traffic flow data from highways of the Pittsburgh area.