Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling
Editat de Constantinos Antoniou, Loukas Dimitriou, Francisco Pereiraen Limba Engleză Paperback – 23 ian 2026
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
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (2) | 592.76 lei 5-7 săpt. | +103.38 lei 4-10 zile |
| ELSEVIER SCIENCE – 27 noi 2018 | 592.76 lei 5-7 săpt. | +103.38 lei 4-10 zile |
| ELSEVIER SCIENCE – 23 ian 2026 | 595.63 lei Precomandă |
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Specificații
ISBN-13: 9780443267895
ISBN-10: 0443267898
Pagini: 632
Dimensiuni: 152 x 229 mm
Greutate: 0.45 kg
Ediția:2
Editura: ELSEVIER SCIENCE
ISBN-10: 0443267898
Pagini: 632
Dimensiuni: 152 x 229 mm
Greutate: 0.45 kg
Ediția:2
Editura: ELSEVIER SCIENCE
Cuprins
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
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