Machine Learning for Transportation Research and Applications (ePub)
(Sprache: Englisch)
Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle...
sofort als Download lieferbar
eBook (ePub)
112.10 €
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
Produktdetails
Produktinformationen zu „Machine Learning for Transportation Research and Applications (ePub)“
Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle challenging transportation problems. This textbook
is designed for college or graduate-level students in transportation or closely related fields to study and understand fundamentals in machine learning. Readers will learn how to develop and apply various types of machine learning models to transportation-related problems. Example applications include traffic sensing, data-quality control, traffic prediction, transportation asset management, traffic-system control and operations, and traffic-safety analysis.
is designed for college or graduate-level students in transportation or closely related fields to study and understand fundamentals in machine learning. Readers will learn how to develop and apply various types of machine learning models to transportation-related problems. Example applications include traffic sensing, data-quality control, traffic prediction, transportation asset management, traffic-system control and operations, and traffic-safety analysis.
- Introduces fundamental machine learning theories and methodologies
- Presents state-of-the-art machine learning methodologies and their incorporation into transportation domain knowledge
- Includes case studies or examples in each chapter that illustrate the application of methodologies and techniques for solving transportation problems
- Provides practice questions following each chapter to enhance understanding and learning
- Includes class projects to practice coding and the use of the methods
Autoren-Porträt von Yinhai Wang, Zhiyong Cui, Ruimin Ke
Yinhai Wang - Ph.D., P.E., Professor, Transportation Engineering, University of Washington, USA. Dr. Yinhai Wang is a fellow of both the IEEE and American Society of Civil Engineers (ASCE). He also serves as director for Pacific Northwest Transportation Consortium (PacTrans), USDOT University Transportation Center for Federal Region 10, and the Northwestern Tribal Technical Assistance Program (NW TTAP) Center. He earned his Ph.D. in transportation engineering from the University of Tokyo (1998) and a Master in ComputerScience from the UW (2002). Dr. Wang's research interests include traffic sensing, transportation data science, artificial intelligence methods and applications, edge computing, traffic operations and simulation, smart urban mobility, transportation safety, among others.
Bibliographische Angaben
- Autoren: Yinhai Wang , Zhiyong Cui , Ruimin Ke
- 2023, 252 Seiten, Englisch
- Verlag: Elsevier Science & Techn.
- ISBN-10: 0323996809
- ISBN-13: 9780323996808
- Erscheinungsdatum: 19.04.2023
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: ePub
- Größe: 22 MB
- Mit Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
Kopierschutz
Dieses eBook können Sie uneingeschränkt auf allen Geräten der tolino Familie lesen. Zum Lesen auf sonstigen eReadern und am PC benötigen Sie eine Adobe ID.
Kommentar zu "Machine Learning for Transportation Research and Applications"
Schreiben Sie einen Kommentar zu "Machine Learning for Transportation Research and Applications".
Kommentar verfassen