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Data Analytics for Intelligent Transportation Systems
This informative guide demonstrates how data analytics can improve transportation-related management decisions, mobility, efficiency, and environmental impacts
Mashrur Chowdhury (Edited by), Amy Apon (Edited by), Kakan Dey (Edited by)
9780128097151, Elsevier Science
Paperback, published 4 April 2017
344 pages
23.4 x 19 x 2.2 cm, 0.63 kg
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.
1. Characteristics of Intelligent Transportation Systems and Its Relationship With Data Analytics 2. Data Analytics: Fundamentals 3. Data Science Tools and Techniques to Support Data Analytics in Transportation Applications 4. The Centrality of Data: Data Lifecycle and Data Pipelines 5. Data Infrastructure for Intelligent Transportation Systems 6. Security and Data Privacy of Modern Automobiles 7. Interactive Data Visualization 8. Data Analytics in Systems Engineering for Intelligent Transportation Systems 9. Data Analytics for Safety Applications 10. Data Analytics for Intermodal Freight Transportation Applications 11. Social Media Data in Transportation 12. Machine Learning in Transportation Data Analytics
Subject Areas: Personal finance [VSB], Investment & securities [KFFM], Finance [KFF], International finance [KCLF]
