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Data-driven solutions to transportat...

Wang, Yinhai,

Data-driven solutions to transportation problems

紀錄類型:	書目-語言資料,印刷品 : 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:

785264
Transportation
--Mathematical models.Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: HE192.5

Dewey Class. No.: 388.015118

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245 0 0 $a Data-driven solutions to transportation problems $h [electronic resource] / $c [edited by] Yinhai Wang, Ziqiang Zeng.
250 $a First edition.
260 $a Amsterdam : $b Elsevier, $c 2019.
300 $a 1 online resource : $b ill.
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.
588 $a Online resource; title from PDF title page (EBSCO, viewed December 4, 2018).
650 0 $a Transportation $x Mathematical models. $3 785264
655 4 $a Electronic books. $2 local $3 554714
700 1 $a Wang, Yinhai, $e editor. $3 1293209
700 1 $a Zeng, Ziqiang $c (Assistant professor), $e editor. $3 1293210
856 4 0 $u https://www.sciencedirect.com/science/book/9780128170267

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