Mobility Patterns, Big Data and Transport Analytics

Name: Mobility Patterns, Big Data and Transport Analytics
Price: 125 USD
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ISBN: 9780443267895

Tools and Applications for Modeling

by Constantinos Antoniou, Loukas Dimitriou, Francisco Pereira

2nd Edition

Publisher: Elsevier

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Book Details

Print ISBN	9780443267895
eText ISBN	9780443267901
Publisher	Elsevier
Publishing Year	2026
Edition	2nd Edition
Language	English
Pages	892

Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling, 2nd Edition, edited by Constantinos Antoniou, Loukas Dimitriou, and Francisco Pereira, is a reference focused on capturing, predicting, visualizing, and controlling mobility patterns in transportation modeling. The volume addresses data-driven analytical methodologies used to examine human movement and transport performance.

The material examines big data handling in transportation modeling alongside model-based machine learning. Additional thematic coverage explores semantic signatures for social sensing in urban environments, data-driven traffic simulation models, and travel behavior patterns under emerging transportation technologies. Further sections address transit data analytics for transport planning and control as well as big data applications for evaluating road safety.

This second edition introduces updated content and new chapters covering active learning, transfer learning, and reinforcement learning for transport applications. The publication supports undergraduate and graduate students in transportation programs alongside transport researchers.

Chapter 1: Big data and transport analytics
Chapter 2: Machine Learning Fundamentals
Chapter 3: Using Semantic Signatures for Social Sensing in Urban Environments
Chapter 4: Geographic Space as a Living Structure for Predicting Human Activities Using Big Data
Chapter 5: Data Preparation
Chapter 6: Data Science and Data Visualization
Chapter 7: Model-Based Machine Learning for Transportation
Chapter 8: Capturing Travel Behavior Patterns on the Anticipating Transportation Technologies and Services
Chapter 9: Reinforcement Learning for Transport Applications
Chapter 10: Foundational principles of learner representations
Chapter 11: Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter
Chapter 12: Transit Data Analytics for Planning, Monitoring, Control, and Information
Chapter 13: A bridge between transit collective mobility patterns and fundamental economics
Chapter 14: Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning Techniques
Chapter 15: Big Data and Road Safety: A Comprehensive Review
Chapter 16: A Back-Engineering Approach to Explore Human Mobility Patterns Across Megacities Using Online Traffic Maps
Chapter 17: Pavement Patch Defects Detection and Classification Using Smartphones, Vibration Signals and Video Images
Chapter 18: Collaborative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and Perspectives
Chapter 19: Experiences with emerging data collection
Chapter 20: Machine Learning methods for processing time series count data in Transportation
Chapter 21: Analysing Travel Patterns on Data Collected by Bicycle Sharing Systems
Chapter 22: Optimal Pricing Schemes in the Maritime Market: Implementations by Deep RL
Chapter 23: Inequalities in mobility: Data-driven analysis of social equity issues in transport
Chapter 24: Conclusion