| Description |
1 online resource (516 pages) |
| Contents |
Intro; Title page; Table of Contents; Copyright; Dedication; Contributors; About the Editors; Chapter 1: Big Data and Transport Analytics: An Introduction; Abstract; 1 Introduction; 2 Book Structure; Special Acknowledgments; Part I: Methodological; Chapter 2: Machine Learning Fundamentals; Abstract; 1 Introduction; 2 A Little Bit of History; 3 Deep Neural Networks and Optimization; 4 Bayesian Models; 5 Basics of Machine Learning Experiments; 6 Concluding Remarks; Chapter 3: Using Semantic Signatures for Social Sensing in Urban Environments; Abstract; 1 Introduction; 2 Spatial Signatures |
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3 Temporal Signatures4 Thematic Signatures; 5 Examples; 6 Summary; Chapter 4: Geographic Space as a Living Structure for Predicting Human Activities Using Big Data; Abstract; Acknowledgments; 1 Introduction; 2 Living Structure and the Topological Representation; 3 Data and Data Processing; 4 Prediction of Tweet Locations Through Living Structure; 5 Implications on the Topological Representation and Living Structure; 6 Conclusion; Chapter 5: Data Preparation; Abstract; 1 Introduction; 2 Tools and Techniques; 3 Probe Vehicle Traffic Data; 4 Context Data |
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Chapter 6: Data Science and Data VisualizationAbstract; 1 Introduction; 2 Structured Visualization; 3 Multidimensional Data Visualization Techniques; 4 Case Studies; 5 Conclusions; Chapter 7: Model-Based Machine Learning for Transportation; Abstract; 1 Introduction; 2 Case Study 1: Taxi Demand in New York City; 3 Case Study 2: Travel Mode Choices; 4 Case Study 3: Freeway Occupancy in San Francisco; 5 Case Study 4: Incident Duration Prediction; 6 Summary; Chapter 8: Textual Data in Transportation Research: Techniques and Opportunities; Abstract; 1 Introduction |
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2 Big Textual Data, Text Sources, and Text Mining3 Fundamental Concepts and Techniques in Literature; 4 Application Examples of Big Textual Data in Transportation; 5 Conclusions; Part II: Applications; Chapter 9: Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter; Abstract; 1 Introduction; 2 California Statewide Travel Demand Model; 3 Twitter Data; 4 Trip Extraction Methods; 5 Models for Matrix Conversion; 6 Summary and Conclusion; Chapter 10: Transit Data Analytics for Planning, Monitoring, Control, and Information; Abstract; Acknowledgments |
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1 Introduction2 Measuring System Performance From the Passenger's Point of View; 3 Decision Support With Predictive Analytics; 4 Optimal Design of Transit Demand Management Strategies; 5 Conclusion; Chapter 11: Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning Techniques; Abstract; 1 New Modeling Challenges and Data Opportunities; 2 Background; 3 Data-Driven Traffic Performance Modeling: Overall Framework; 4 Application to Mesoscopic Modeling; 5 Application to Microscopic Traffic Modeling; 6 Application to Weak Lane Discipline Modeling; 7 Network-Wide Application |
| Notes |
8 Conclusions |
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Print version record |
| Subject |
Urban transportation.
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Urban transportation -- Simulation methods
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Traffic engineering.
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urban transportation.
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traffic engineering.
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Traffic engineering
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Urban transportation
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Urban transportation -- Simulation methods
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| Form |
Electronic book
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| Author |
Dimitriou, Loukas
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Pereira, Francisco
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| ISBN |
9780128129715 |
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0128129719 |
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