Inhoudsopgave:
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Transportation issues are often too complicated to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle the challenging transportation problems. Readers will learn how to develop and apply different types of machine learning models to transportation related problems. Example applications include transportation data generations, traffic sensing, transportation mode recognition, transportation system management and control, traffic flow prediction, and traffic safety analysis.\u003cul\u003e \u003cli\u003eIntroduces fundamental machine learning theories and methodologies\u003c/li\u003e \u003cli\u003ePresents state-of-the-art machine learning methodologies and their integrations with transportation domain knowledge\u003c/li\u003e \u003cli\u003eIncludes case studies or examples in each chapter that illustrate the application of methodologies and techniques for solving transportation problems\u003c/li\u003e\u003c/ul\u003e |
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