MACHINE LEARNING SOLUTIONS FOR TRANSPORTATION NETWORKS

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This thesis brings a collection of novel models and methods that result from a new look at practical problems in transportation through the prism of newly available sensor data. From this data, we build a model of traffic flow inspired by macroscopic flow models. Unlike traditional such models, our model deals with uncertainty of measurement and unobservability of certain important quantities and incorporates on-the-fly observations more easily. Having a predictive distribution of traffic state enables the application of powerful decision-making machinery to the traffic domain. Secondly, a new method for detecting accidents and other adverse events is described. Data collected from highways enables us to bring supervised learning approaches to incident detection. However, a major hurdle to performance of supervised learners is the quality of data which contains systematic biases varying from site to site. We build a dynamic Bayesian network framework that learns and rectifies these biases, leading to improved supervised detector performance with little need for manually tagged data. The realignment method applies generally to virtually all forms of labeled sequential data.