國立虎尾科技大學 |

Logic-Driven Traffic Big Data Analytics = Methodology and Applications for Planning /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Logic-Driven Traffic Big Data Analytics/ by Shaopeng Zhong, Daniel (Jian) Sun.
Reminder of title:	Methodology and Applications for Planning /
Author:	Zhong, Shaopeng.
other author:	Sun, Daniel (Jian).
Description:	XXII, 280 p. 96 illus., 82 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data Engineering. -
Online resource:	https://doi.org/10.1007/978-981-16-8016-8
ISBN:	9789811680168

Logic-Driven Traffic Big Data Analytics = Methodology and Applications for Planning /
Zhong, Shaopeng.

Logic-Driven Traffic Big Data AnalyticsMethodology and Applications for Planning /[electronic resource] :by Shaopeng Zhong, Daniel (Jian) Sun. - 1st ed. 2022. - XXII, 280 p. 96 illus., 82 illus. in color.online resource.

Logic driven traffic big data analytics: An introduction -- Statistical models and methods -- Spatial-temporal distribution model for travel origin-destination based on multi-source data -- Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data -- A ride-sourcing group prediction model based on convolutional neural network.

This book starts from the relationship between urban built environment and travel behavior and focuses on analyzing the origin of traffic phenomena behind the data through multi-source traffic big data, which makes the book unique and different from the previous data-driven traffic big data analysis literature. This book focuses on understanding, estimating, predicting, and optimizing mobility patterns. Readers can find multi-source traffic big data processing methods, related statistical analysis models, and practical case applications from this book. This book bridges the gap between traffic big data, statistical analysis models, and mobility pattern analysis with a systematic investigation of traffic big data’s impact on mobility patterns and urban planning.

ISBN: 9789811680168

Standard No.: 10.1007/978-981-16-8016-8doiSubjects--Topical Terms:

1226308
Data Engineering.

LC Class. No.: T57.6-.97

Dewey Class. No.: 658.403

Logic-Driven Traffic Big Data Analytics = Methodology and Applications for Planning /
LDR:02587nam a22004215i 4500 001 1093826
003 DE-He213
005 20220429012220.0
007 cr nn 008mamaa
008 221228s2022 si | s |||| 0|eng d
020 $a 9789811680168 $9 978-981-16-8016-8
024 7 $a 10.1007/978-981-16-8016-8 $2 doi
035 $a 978-981-16-8016-8
050 4 $a T57.6-.97
072 7 $a KJT $2 bicssc
072 7 $a KJMD $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJMD $2 thema
082 0 4 $a 658.403 $2 23
100 1 $a Zhong, Shaopeng. $e author. $0 (orcid)0000-0001-5871-9817 $1 https://orcid.org/0000-0001-5871-9817 $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1401825
245 1 0 $a Logic-Driven Traffic Big Data Analytics $h [electronic resource] : $b Methodology and Applications for Planning / $c by Shaopeng Zhong, Daniel (Jian) Sun.
250 $a 1st ed. 2022.
264 1 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a XXII, 280 p. 96 illus., 82 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Logic driven traffic big data analytics: An introduction -- Statistical models and methods -- Spatial-temporal distribution model for travel origin-destination based on multi-source data -- Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data -- A ride-sourcing group prediction model based on convolutional neural network.
520 $a This book starts from the relationship between urban built environment and travel behavior and focuses on analyzing the origin of traffic phenomena behind the data through multi-source traffic big data, which makes the book unique and different from the previous data-driven traffic big data analysis literature. This book focuses on understanding, estimating, predicting, and optimizing mobility patterns. Readers can find multi-source traffic big data processing methods, related statistical analysis models, and practical case applications from this book. This book bridges the gap between traffic big data, statistical analysis models, and mobility pattern analysis with a systematic investigation of traffic big data’s impact on mobility patterns and urban planning.
650 2 4 $a Data Engineering. $3 1226308
650 1 4 $a Operations Research and Decision Theory. $3 1366301
650 0 $a Engineering—Data processing. $3 1297966
650 0 $a Operations research. $3 573517
700 1 $a Sun, Daniel (Jian). $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1401826
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811680151
776 0 8 $i Printed edition: $z 9789811680175
776 0 8 $i Printed edition: $z 9789811680182
856 4 0 $u https://doi.org/10.1007/978-981-16-8016-8
912 $a ZDB-2-BUM
912 $a ZDB-2-SXBM
950 $a Business and Management (SpringerNature-41169)
950 $a Business and Management (R0) (SpringerNature-43719)