Buch, Englisch, 69 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 143 g
Buch, Englisch, 69 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 143 g
Reihe: SpringerBriefs in Computer Science
ISBN: 978-981-16-2240-3
Verlag: Springer Nature Singapore
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.Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
1. Introduction
1.1 Overview
1.2 Background1.2.1 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 description2.2 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