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Data-driven solutions to transportat...

Wang, Yinhai,

Data-driven solutions to transportation problems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-driven solutions to transportation problems[edited by] Yinhai Wang, Ziqiang Zeng.
other author:	Wang, Yinhai,
Published:	Amsterdam :Elsevier,2019.
Description:	1 online resource :ill.
Subject:	TransportationMathematical models.
Online resource:	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:

338104
Transportation
--Mathematical models.Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: HE192.5

Dewey Class. No.: 388.015118

Data-driven solutions to transportation problems
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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 338104
655 4 $a Electronic books. $2 local. $3 214472
700 1 $a Wang, Yinhai, $e editor. $3 872833
700 1 $a Zeng, Ziqiang $c (Assistant professor), $e editor. $3 872834
856 4 0 $u https://www.sciencedirect.com/science/book/9780128170267