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