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

3.4 Fundamental Data Types and Structures: Data Frames and List3.5 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 Introduction5.2 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 VisualizationAbstract; 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

Exercise ProblemsReferences; 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

Title page; Table of Contents; Copyright; Dedication; About the Editors; About the Contributors; Preface; Acknowledgments; 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