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 MACHINE LEARNING SOLUTIONS FOR TRANSPORTATION NETWORKS. Learning the behavior of traffic flow
Наименование:

MACHINE LEARNING SOLUTIONS FOR TRANSPORTATION NETWORKS. Learning the behavior of traffic flow

автор Tomas S. издательство Книга по Требованию Математический анализ. Кратные и криволинейные интегралы. Задачи, решения
Cтраниц 128        мягкая обложка       
Аннотация: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. 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. Having a predictive distribution of traffic state enables the application of powerful decision-making machinery to the traffic domain. The realignment method applies generally to virtually all forms of labeled sequential data. However, a major hurdle to performance of supervised learners is the quality of data which contains systematic biases varying from site to site. Data collected from highways enables us to bring supervised learning approaches to incident detection. From this data, we build a model of traffic flow inspired by macroscopic flow models. 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. Secondly, a new method for detecting accidents and other adverse events is described.
Автор: Tomas S.
Издательство: Книга по Требованию
Год:2009
Цена:
2384 руб
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Поиск: MACHINE LEARNING SOLUTIONS FOR TRANSPORTATION NETWORKS. Learning the behavior of traffic flow


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