國立高雄大學圖資館 |

Language: English

Back

Mobility data-driven urban traffic m...

Liu, Zhidan.

Mobility data-driven urban traffic monitoring

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mobility data-driven urban traffic monitoringby Zhidan Liu, Kaishun Wu.
Author:	Liu, Zhidan.
other author:	Wu, Kaishun.
Published:	Singapore :Springer Singapore :2021.
Description:	xi, 69 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Traffic monitoringData processing.
Online resource:	https://doi.org/10.1007/978-981-16-2241-0
ISBN:	9789811622410$q(electronic bk.)

Mobility data-driven urban traffic monitoring
Liu, Zhidan.

Mobility data-driven urban traffic monitoring[electronic resource] /by Zhidan Liu, Kaishun Wu. - Singapore :Springer Singapore :2021. - xi, 69 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

Chapter 1 Introduction -- Chapter 2 Urban Traffic Monitoring from Mobility Data -- Chapter 3 A Compressive Sensing based Traffic Monitoring Approach -- Chapter 4 A Dynamic Correlation Modeling based Traffic Monitoring Approach -- Chapter 5 A Crowdsensing based Traffic Monitoring Approach. -Chapter 6 Conclusion and Future Work.

This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring. This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-based urban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale. This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT) Advanced level students studying these topics benefit from this book as well.

ISBN: 9789811622410$q(electronic bk.)

Standard No.: 10.1007/978-981-16-2241-0doiSubjects--Topical Terms:

892161
Traffic monitoring
--Data processing.

LC Class. No.: HE369 / .L58 2021

Dewey Class. No.: 388.310285

Mobility data-driven urban traffic monitoring
LDR:03409nmm a2200349 a 4500 001 598404
003 DE-He213
005 20210518093745.0
006 m d
007 cr nn 008maaau
008 211025s2021 si s 0 eng d
020 $a 9789811622410$q(electronic bk.)
020 $a 9789811622403$q(paper)
024 7 $a 10.1007/978-981-16-2241-0 $2 doi
035 $a 978-981-16-2241-0
040 $a GP $c GP
041 0 $a eng
050 4 $a HE369 $b .L58 2021
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 388.310285 $2 23
090 $a HE369 $b .L783 2021
100 1 $a Liu, Zhidan. $3 892160
245 1 0 $a Mobility data-driven urban traffic monitoring $h [electronic resource] / $c by Zhidan Liu, Kaishun Wu.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a xi, 69 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5768
505 0 $a Chapter 1 Introduction -- Chapter 2 Urban Traffic Monitoring from Mobility Data -- Chapter 3 A Compressive Sensing based Traffic Monitoring Approach -- Chapter 4 A Dynamic Correlation Modeling based Traffic Monitoring Approach -- Chapter 5 A Crowdsensing based Traffic Monitoring Approach. -Chapter 6 Conclusion and Future Work.
520 $a This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring. This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-based urban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale. This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT) Advanced level students studying these topics benefit from this book as well.
650 0 $a Traffic monitoring $x Data processing. $3 892161
650 0 $a Intelligent transportation systems. $3 403795
650 1 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Data Structures and Information Theory. $3 825714
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Mobile Computing. $3 763350
700 1 $a Wu, Kaishun. $3 679541
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in computer science. $3 559641
856 4 0 $u https://doi.org/10.1007/978-981-16-2241-0
950 $a Computer Science (SpringerNature-11645)