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

Singliar, Tomas

 
9783639171600: MACHINE LEARNING SOLUTIONS FOR TRANSPORTATION NETWORKS: Learning the behavior of traffic flow
Couverture souple
ISBN 10 : 3639171608 ISBN 13 : 9783639171600
Editeur : 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.

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Présentation de l'éditeur

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.

Biographie de l'auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag
Date d'édition
2009
Langue
anglais
ISBN 10 
3639171608
ISBN 13 
9783639171600
Reliure
Broché
Nombre de pages
128
Coordonnées du fabricant
non disponible
Personne responsable
non disponible

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9781243602053: Machine Learning Solutions for Transportation Networks

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ISBN 10 :  1243602058 ISBN 13 :  9781243602053
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Couverture souple