Mobility Patterns, Big Data and Transport Analytics

Tools and Applications for Modeling

Constantinos Antoniou, Loukas Dimitriou, Francisco Pereira

• Publisher: Elsevier
• Year: 2026
• Edition: 2
• eBook ISBN:
  9780443267901
• Topic: Business
• Subtopic: Operations
• Index: Business

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. The book features prominent international experts who provide overviews on new analytical frameworks, applications, and concepts in mobility analysis and transportation systems. Fields covered are evolving rapidly, and this new edition updates 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, 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. - 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 with videos showing analyses performed, as well as test codes and data-sets, thus allowing readers to recreate and apply highlighted techniques to their own data

Table of contents

• Title of Book
• Chapter 1 Big data and transport analytics: an introduction
• 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 Reinforcement learning: a snapshot bibliometric analysis of the current state-of-the-art
• Chapter 9 Foundational principles for representation learning: applications in autonomous mobility
• Chapter 10 Large language models for urban transportation
• Chapter 11 Capturing travel behavior patterns on the anticipating transportation technologies and services
• Chapter 12 Statewide comparison of origin-destination matrices between California travel model and Twitter
• Chapter 13 User-centric transit data analytics for inference, prediction, and optimization
• Chapter 14 Transit systems and collective mobility patterns: a microeconomic approach using simultaneous equations modeling
• Chapter 15 Data-driven traffic prediction and simulation models: mobility patterns using machine learning techniques
• Chapter 16 Big Data and road safety: a comprehensive review
• Chapter 17 Mobility patterns across multiple cities based on online traffic maps
• Chapter 18 Pavement patch defects detection and classification using smartphones, vibration signals, and video images
• Chapter 19 Enabling smart mobility: collaborative positioning for urban intelligent transportation systems and personal mobility
• Chapter 20 Emerging methods to collect big data on travel experiences
• Chapter 21 Machine learning methods for processing time series count data in transportation
• Chapter 22 Micromobility spatial and temporal patterns within the urban landscape: considerations on demand modeling, the effects of mobility safety and transport disruptions
• Chapter 23 Optimal static and dynamic tariff-setting strategies in systems of port terminals using reinforcement learning methods
• Chapter 24 Inequalities in mobility: data-driven analysis of social equity issues in transport
• Index