Data Analytics for Intelligent Transportation Systems
- 2nd Edition - November 2, 2024
- Imprint: Elsevier
- Editors: Mashrur Chowdhury, Kakan Dey, Amy Apon
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 8 7 8 - 2
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 8 7 9 - 9
Data Analytics for Intelligent Transportation Systems, Second Edition provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems (ITS), in… Read more
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Request a sales quoteData Analytics for Intelligent Transportation Systems, Second Edition provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems (ITS), including 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. Other sections provide extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies.
All fundamentals/concepts presented in this book are explained in the context of ITS. Users will learn everything from the basics of different ITS data types and characteristics to how to evaluate alternative data analytics for different ITS applications. In addition, they will discover 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.
- Utilizes real ITS examples to facilitate a quicker grasp of materials presented
- Contains contributors from both leading academic and commercial domains
- Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications
- Includes exercise problems in each chapter to help readers apply and master the learned fundamentals, concepts, and techniques
- New to the second edition: Two new chapters on Quantum Computing in Data Analytics and Society and Environment in ITS Data Analytics
Graduate courses on transportation engineering/intelligent transportation systems; professionals such as those in organizations like the Institute of Transportation Engineers (ITE), American Society of Civil Engineers (ASCE), Institute of Electrical and Electronics Engineers (IEEE), and Intelligent Transportation Society of America (ITS America) Graduate students in data science programs, researchers in transportation engineering, automotive engineering, and computer science domains
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of contributors
- Preface
- 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 intelligent transportation system
- 1.3 Intelligent transportation system architecture: the framework of intelligent transportation system applications
- 1.4 Overview of intelligent transportation system applications
- 1.5 Intelligent transportation systems past, present, and future
- 1.6 Overview of book: data analytics for intelligent transportation system 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 Recent advances and trends in data analytics
- 2.7 Conclusion
- 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 to the R programming environment for data analytics
- 3.2 Research data exchange
- 3.3 Fundamental data types and structures: data frames and list
- 3.4 Importing data from external files
- 3.5 Ingesting online social media data
- 3.6 Data mining and machine learning techniques and libraries in Python
- 3.7 Big data processing: Hadoop MapReduce
- 3.8 Summary
- Exercises
- References
- Chapter 4. Lifecycle and data pipelines: the centrality of data
- Abstract
- 4.1 Introduction
- 4.2 Use cases and data variability
- 4.3 Data and its life cycle
- 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 connected transport 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 Quantum infrastructure
- 5.7 Conclusion
- Exercises
- 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 and conclusions
- 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 Case study: designing a multivariate visual analytics tool
- 7.11 Chapter summary and conclusions
- Exercises
- 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
- Exercises
- Appendix A
- References
- Chapter 9. Data analytics for safety applications
- Abstract
- 9.1 Introduction
- 9.2 Overview of safety research
- 9.3 Connected vehicles and traffic safety
- 9.4 Statistical methods
- 9.5 Categorical data modeling
- 9.6 Artificial intelligence
- 9.7 Safety data
- 9.8 Crash data
- 9.9 Traffic data
- 9.10 Roadway data
- 9.11 Weather data
- 9.12 Vehicle and driver data
- 9.13 Naturalistic driving study
- 9.14 Connected and autonomous vehicle data
- 9.15 Other data
- 9.16 Issues and future directions
- 9.17 Future directions
- 9.18 Chapter summary and conclusions
- 9.19 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 Data visualization
- 10.5 Summary and conclusions
- Exercise problems
- Solution to exercise problems
- References
- Chapter 11. Data analytics for prediction of real-time traffic flow parameters
- Abstract
- 11.1 Traffic flow parameters and its importance
- 11.2 Traffic flow data
- 11.3 Noise reduction model
- 11.4 Prediction models
- 11.5 Real-time application efficacy
- 11.6 Conclusion
- Questions
- References
- Chapter 12. Social media data in transportation
- Abstract
- 12.1 Introduction to social media
- 12.2 Social media data characteristics
- 12.3 Social media data analysis
- 12.4 Application of social media data in transportation
- 12.5 Future research issues/challenges for data analytics-enabled social media data
- 12.6 Summary
- 12.7 Conclusions
- Exercise problems
- References
- Chapter 13. Artificial intelligence in transportation data analytics
- Abstract
- 13.1 Artificial intelligence in transportation data analytics
- 13.2 Questions and solutions
- Appendix
- References
- Chapter 14. Blockchains and intelligent transportation system applications
- Abstract
- 14.1 Introduction
- 14.2 Background
- 14.3 Related work
- 14.4 System architecture
- 14.5 Oblivious algorithms
- 14.6 System implementation
- 14.7 Experimental evaluation
- 14.8 Conclusions and future work
- Exercises
- Appendix A Oblivious execution guarantees
- Appendix B Experiments with other aggregation techniques
- References
- Chapter 15. Detection and mitigation of spoofing attacks in-based autonomous ground vehicle navigation systems
- Abstract
- 15.1 Introduction
- 15.2 Global navigation satellite system-based positioning
- 15.3 Vulnerabilities of global navigation satellite system-based navigation
- 15.4 Spoofing attack modeling
- 15.5 Attack detection
- 15.6 Attack mitigation
- 15.7 Conclusions
- Questions and exercise problems
- References
- Index
- Edition: 2
- Published: November 2, 2024
- Imprint: Elsevier
- No. of pages: 472
- Language: English
- Paperback ISBN: 9780443138782
- eBook ISBN: 9780443138799
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.KD
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, USAAA
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, USARead Data Analytics for Intelligent Transportation Systems on ScienceDirect