Data-Driven Solutions to Transportation Problems (ePub)
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Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more.
- Synthesizes the newest developments in data-driven transportation science
- Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed
- Useful for both theoretical and technically-oriented researchers
- Autoren: Yinhai Wang , Ziqiang Zeng
- 2018, 299 Seiten, Englisch
- Verlag: Elsevier Science & Techn.
- ISBN-10: 0128170271
- ISBN-13: 9780128170274
- Erscheinungsdatum: 04.12.2018
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: ePub
- Größe: 52 MB
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