Download or read book Mobility Patterns, Big Data and Transport Analytics written by Constantinos Antoniou and published by Elsevier. This book was released on 2025-06-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling, Second Edition 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. It features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. The fields covered by this book are evolving rapidly and this new edition updates the existing material and provides new chapters that reflect recent developments in the field (such as the emergence of active, transfer and reinforcement learning). 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. It 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 - Features a companion website providing 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