MACHINE LEARNING SOLUTIONS FOR TRANSPORTATION NETWORKS: Learning the behavior of traffic flow - Softcover

Singliar, Tomas

Softcover

ISBN 10: 3639171608 ISBN 13: 9783639171600

Publisher: VDM Verlag, 2009

Synopsis

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.

About this title

About the Author

Tomas specializes in machine learning and anomaly detection, especially by means of graphical probability models. He obtained his PhD from University of Pittsburgh in 2008, authored papers on inference in graphical models, modeling of large sensor data sets and anomaly detection and served on program committees of several major AI conferences.

Edition details

• Publisher: VDM Verlag
• Publication date: 2009
• Language: English
• ISBN 10: 3639171608
• ISBN 13: 9783639171600
• Binding: Paperback
• Number of pages: 128