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.

Autorentext

Zhidan Liu is currently Assistant Professor in the College of Computer Science and Software Engineering, Shenzhen University, China. He received a Ph.D. degree in Computer Science and Technology from Zhejiang University, China, in 2014. Before joining in Shenzhen University, he was Postdoctoral Research Fellow in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. Dr. Liu's research interests include Internet of Things, urban computing, crowdsourcing, and big data analytics. He has published more than 20 research papers in top-tier international journals and conferences, including IEEE/ACM ToN, IEEE TMC, IEEE TITS, IEEE Network Magazine, IEEE IOTJ, ACM MobiSys, IEEE ICDE, ACM/IEEE IPSN, and ACM MobiHoc. He received the Best Paper Award of IEEE ICPADS 2020. Dr. Liu served as Technical Programme Committee Member of IEEE ICDCS, IEEE ICPADS, IEEE ICCCN, IEEE MSN, IEEE MASS, and IEEE HiPC. He is Member of IEEE, ACM, and CCF.

Kaishun Wu is currently Distinguished Professor in the College of Computer Science and Software Engineering, Shenzhen University, China, where he leads the Research Centre of Internet of Things. Professor Wu's research interests include wireless communications and mobile computing. He has co-authored 2 books and published over 100 refereed papers in international leading journals and primer conferences. He is Inventor of 8 US and 100 Chinese pending patents (63 are issued). Professor Wu serves as Associate Editor of IET COMMUNICATIONS, IEEE Access, and Guest Editor of IEEE Network. He is Technical Program Committee Member of IEEE INFOCOM, IEEE ICDCS, IEEE ICC, IEEE Globecom, and so on. He won the best paper awards in IEEE Globecom 2012, IEEE ICPADS 2012, IEEE MASS 2014, IEEE SECON 2018. Professor Wu was selected as Winner of 2012 Hong Kong Young Scientist Award. He was also one of the winners of 2014 Hong Kong ICT Awards: Best Innovation. He received 2014 IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award. He is IET Fellow and IEEE Senior Member.

Inhalt
1. Introduction 1.1 Overview 1.2 Background

1.2.1 Mobility data

1.2.2 Mobility data boosted applications

1.2.3 Intelligent Transportation Systems

1.3 Book structure

2. Urban Traffic Monitoring from Mobility Data 2.1 Problem description

2.2 Typical workflow of urban traffic monitoring

2.2.1 Data collection and preprocessing

2.2.2 Urban traffic modeling

2.2.3 Urban traffic estimation

2.3 Summary

3. A Compressive Sensing based Traffic Monitoring Approach

3.1 Introduction

3.2 Preliminary

3.2.1 Traffic correlation

3.2.2 Compressive sensing

3.3 The proposed approach

3.3.1 Traffic correlation mining

3.3.2 Traffic estimation via compressive sensing

3.3.3 Practical issues and optimizations

3.4 Experimental evaluation

3.5 Summary

4. A Dynamic Correlation Modeling based Traffic Monitoring Approach

4.1 Introduction

4.2 Non-linearity in traffic estimation

4.3 Think like a graph

4.3.1 Graph representation

4.3.2 Dynamic correlation modeling

4.4 Putting things into Spark

4.4.1 Geography-aware graph partitioning

4.4.2 Efficient information propagation

4.4.3 Other optimizations

4.5 Experimental evaluation

4.6 Summary

5. A Crowdsensing based Traffic Monitoring Approach

5.1 Introduction

5.2 Motivation: traffic monitoring with the help of bus riders

5.3 System design

5.3.1 Data collection

5.3.2 Trajectory mapping

5.3.3 Traffic estimation

5.3.4 Online database construction

5.4 Experimental Evaluation

5.5 Summary

6. Conclusion and Future Work

6.1 Conclusions 6.2 Future research directions