Mobility data-driven urban traffic monitoring / Zhidan Liu, Kaishun Wu.
2021
HE369 .L58 2021
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Title
Mobility data-driven urban traffic monitoring / Zhidan Liu, Kaishun Wu.
Author
Liu, Zhidan, author.
ISBN
9789811622410 (electronic bk.)
9811622418 (electronic bk.)
9789811622403
981162240X
9811622418 (electronic bk.)
9789811622403
981162240X
Published
Singapore : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource (xi, 69 pages) : illustrations (chiefly color)
Item Number
10.1007/978-981-16-2241-0 doi
Call Number
HE369 .L58 2021
Dewey Decimal Classification
388.3/10285
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed June 10, 2021).
Added Author
Wu, Kaishun, author.
Series
SpringerBriefs in computer science, 2191-5768
Available in Other Form
Print version: 9789811622403
Print version: 9789811622427
Print version: 9789811622427
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Table of Contents
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.
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.