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Data Analytics for Intelligent Transportation Systems
- 1st Edition - April 4, 2017
- Editors: Mashrur Chowdhury, Amy Apon, Kakan Dey
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 0 9 7 1 5 - 1
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 0 9 8 5 1 - 6
Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed… Read more
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Request a sales quoteData 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.
- 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
Intelligent Transportation Systems researchers, practitioners, and graduate students in Transportation and Computer Science
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
- No. of pages: 344
- Language: English
- Edition: 1
- Published: April 4, 2017
- Imprint: Elsevier
- Paperback ISBN: 9780128097151
- eBook ISBN: 9780128098516
MC
Mashrur Chowdhury
Mashrur Chowdhury is Eugene Douglas Mays Chaired Professor of Transportation in the Glenn Department of Civil Engineering at Clemson University. He is the Director of USDOT Center for Connected Multimodal Mobility and Co-Director of the Complex Systems, Analytics, and Visualization Institute at Clemson. His research focuses on connected and automated vehicles with an emphasis on their integration within smart cities.
Affiliations and expertise
Eugene Douglas Mays Professor of Transportation, Clemson University, USA.AA
Amy Apon
Dr. Amy Apon has been Professor and Chair of the Computer Science Division in the School of Computing at Clemson University since 2011. She was on leave from Clemson as a Program Officer in the Computer Network Systems Division of the National Science Foundation during 2015, working on research programs in Big Data, EXploiting Parallelism and Scalability, and Computer Systems Research. Apon established the High Performance Computing Center at the University of Arkansas and directed the center from 2005 to 2011. She has more than 100 scholarly publications in areas of cluster computing, performance analysis of high performance computing systems, and scalable data analytics. She is a Senior Member of the Association for Computing Machinery and a Senior Member of the Institute of Electrical and Electronics Engineers. Apon holds a Ph.D. in Computer Science from Vanderbilt University.
Affiliations and expertise
Professor, School of Computing, Clemson University, USAKD
Kakan Dey
Kakan Dey is Assistant Professor and Director of the Connected and Automated Transportation Systems (CATS) Lab at the West Virginia University. His primary research area is intelligent transportation systems, which include connected and automated vehicle technology, data science, cyber-physical systems, and smart cities.
Affiliations and expertise
Associate Professor, Michigan State University, USARead Data Analytics for Intelligent Transportation Systems on ScienceDirect