Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns – a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility ‘structural’ analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena. This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data’s impact on mobility and an introduction to the tools necessary to apply new techniques. The book covers in detail, mobility ‘structural’ analysis (and its dynamics), the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis related to complex processes and phenomena. The book bridges the gap between big data, data science, and Transportation Systems Analysis with a study of big data’s impact on mobility, and an introduction to the tools necessary to apply new techniques. – Guides readers through the paradigm-shifting opportunities and challenges of handling Big Data in transportation modeling and analytics- Covers current analytical innovations focused on capturing, predicting, visualizing, and controlling mobility patterns, while discussing future trends- Delivers an introduction to transportation-related information advances, providing a benchmark reference by world-leading experts in the field- Captures and manages mobility patterns, covering multiple purposes and alternative transport modes, in a multi-disciplinary approach- Companion website features videos showing the analyses performed, as well as test codes and data-sets, allowing readers to recreate the presented analyses and apply the highlighted techniques to their own data
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