Purchase textbook

Data Analytics for Intelligent Transportation Systems,

Edition 1

Editors: Edited by Mashrur Chowdhury, Amy Apon and Kakan Dey

Publication Date: 04 Apr 2017

0 Reviews

Legal Considerations

Unknown accessibility

Note

This product relies on 3rd party tooling which may impact the accessibility features visible in inspection copies. All accessibility features mentioned would be present in the purchased version of the title.

Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce.

It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. Users will learn how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning.

Key Features

Includes case studies in each chapter that illustrate the application of concepts covered
Presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies
Contains contributors from both leading academic and commercial researchers
Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications

About the author

Edited by Mashrur Chowdhury, Eugene Douglas Mays Professor of Transportation, Clemson University, USA.; Amy Apon, Professor and Chair, Computer Science Division, Clemson University, USA and Kakan Dey, Assistant Professor, West Virginia University, USA

Chapter 1. Characteristics of Intelligent Transportation Systems and Its Relationship With Data Analytics

Abstract
1.1 Intelligent Transportation Systems as Data-Intensive Applications
1.2 Big Data Analytics and Infrastructure to Support ITS
1.3 ITS Architecture: The Framework of ITS Applications
1.4 Overview of ITS Applications
1.5 Intelligent Transportation Systems Past, Present, and Future
1.6 Overview of Book: Data Analytics for ITS Applications
Exercise Problems
References

Chapter 2. Data Analytics: Fundamentals

Abstract
2.1 Introduction
2.2 Functional Facets of Data Analytics
2.3 Evolution of Data Analytics
2.4 Data Science
2.5 Tools and Resources for Data Analytics
2.6 Future Directions
2.7 Chapter Summary and Conclusions
2.8 Questions and Exercise Problems
References

Chapter 3. Data Science Tools and Techniques to Support Data Analytics in Transportation Applications

Abstract
3.1 Introduction
3.2 Introduction to the R Programming Environment for Data Analytics
3.3 Research Data Exchange
3.4 Fundamental Data Types and Structures: Data Frames and List
3.5 Importing Data from External Files
3.6 Ingesting Online Social Media Data
3.7 Big Data Processing: Hadoop MapReduce
3.8 Summary
3.9 Exercises
References

Chapter 4. The Centrality of Data: Data Lifecycle and Data Pipelines

Abstract
4.1 Introduction
4.2 Use Cases and Data Variability
4.3 Data and its Lifecycle
4.4 Data Pipelines
4.5 Future Directions
4.6 Chapter Summary and Conclusions
4.7 Exercise Problems and Questions
References

Chapter 5. Data Infrastructure for Intelligent Transportation Systems

Abstract
5.1 Introduction
5.2 Connected Transport System Applications and Workload Characteristics
5.3 Infrastructure Overview
5.4 Higher-Level Infrastructure
5.5 Low-Level Infrastructure
5.6 Chapter Summary and Conclusions
References

Chapter 6. Security and Data Privacy of Modern Automobiles

Abstract
6.1 Introduction
6.2 Connected Vehicle Networks and Vehicular Applications
6.3 Stakeholders and Assets
6.4 Attack Taxonomy
6.5 Security Analysis
6.6 Security and Privacy Solutions
6.7 Future Research Directions
6.8 Summary and Conclusions
6.9 Exercises
References

Chapter 7. Interactive Data Visualization

Abstract
7.1 Introduction
7.2 Data Visualization for Intelligent Transportation Systems
7.3 The Power of Data Visualization
7.4 The Data Visualization Pipeline
7.5 Classifying Data Visualization Systems
7.6 Overview Strategies
7.7 Navigation Strategies
7.8 Visual Interaction Strategies
7.9 Principles for Designing Effective Data Visualizations
7.10 A Case Study: Designing a Multivariate Visual Analytics Tool
7.11 Chapter Summary and Conclusions
7.12 Exercises
7.13 Sources for More Information
References

Chapter 8. Data Analytics in Systems Engineering for Intelligent Transportation Systems

Abstract
8.1 Introduction
8.2 Background
8.3 Development Scenario
8.4 Summary and Conclusion
8.5 Exercises
8.6 Appendix A
References

Chapter 9. Data Analytics for Safety Applications

Abstract
9.1 Introduction
9.2 Overview of Safety Research
9.3 Safety Analysis Methods
9.4 Safety Data
9.5 Issues and Future Directions
9.6 Chapter Summary and Conclusions
9.7 Exercise Problems and Questions
References

Chapter 10. Data Analytics for Intermodal Freight Transportation Applications

Abstract
10.1 Introduction
10.2 Descriptive Data Analytics
10.3 Predictive Data Analytics
10.4 Summary and Conclusions
10.5 Exercise Problems
10.6 Solution to Exercise Problems
References

Chapter 11. Social Media Data in Transportation

Abstract
11.1 Introduction to Social Media
11.2 Social Media Data Characteristics
11.3 Social Media Data Analysis
11.4 Application of Social Media Data in Transportation
11.5 Future Research Issues/Challenges for Data Analytics-Enabled Social Media Data
11.6 Summary
11.7 Conclusions
11.8 Exercise Problems
References

Chapter 12. Machine Learning in Transportation Data Analytics

Abstract
12.1 Introduction
12.2 Machine Learning Methods
12.3 Understanding Data
12.4 Machine Learning Algorithms for Data Analytics
12.5 An Example
12.6 Summary
12.7 Questions and Solutions
References
Appendix

ISBN: 9780128097151

Page Count: 344

Retail Price :

Chilamkurti, Intelligent Vehicular Network and Communications: Fundamentals, Architectures and Solutions, Morgan Kaufmann, Aug 2016, 9780128092668, $89.95
Kala, On-Road Intelligent Vehicles: Motion Planning for Intelligent Transportation Systems, Butterworth-Heinemann, May 2016, 9780128037294, $110.00
Chen, Vehicular Communications and Networks: Architectures, Protocols, Operation and Deployment, Woodhead, Apr 2015, 978178242211, $215.00
Chen and Li, Advances in Intelligent Vehicles, Academic Press, Dec 2013, 9780123971999, $125.00