Mobility Data-Driven Urban Traffic Monitoring (Paperback, 2021)

1.        Introduction

1.1 Overview

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

2.2 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