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Data-driven solutions to transportat...

Wang, Yinhai,

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Data-driven solutions to transportation problems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-driven solutions to transportation problems/ [edited by] Yinhai Wang, Ziqiang Zeng.
其他作者:	Wang, Yinhai,
出版者:	Amsterdam :Elsevier, : 2019.,
面頁冊數:	1 online resource :ill.
標題:	Transportation - Mathematical models. -
電子資源:	https://www.sciencedirect.com/science/book/9780128170267
ISBN:	9780128170274 (electronic bk.)

Data-driven solutions to transportation problems
Data-driven solutions to transportation problems[electronic resource] /[edited by] Yinhai Wang, Ziqiang Zeng. - First edition. - Amsterdam :Elsevier,2019. - 1 online resource :ill.

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.

ISBN: 9780128170274 (electronic bk.)Subjects--Topical Terms:

713382
Transportation
--Mathematical models.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: HE192.5

Dewey Class. No.: 388.015118

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520 $a 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.
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