Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Contributors - p. xi 6. List of Figures - p. xiii 7. List of Tables - p. xix 8. Preface - p. xxi 9. Acronyms - p. xxv 10. Chapter 1: Overview of Data-Driven Solutions - p. xxv 11. Chapter 2: Data-Driven Energy Efficient Driving Control in Connected Vehicle Environment - p. 10 12. Chapter 3: Machine Learning and Computer Vision-Enabled Traffic Sensing Data Analysis and Quality Enhancement - p. 49 13. Chapter 4: Data-Driven Approaches for Estimating Travel Time Reliability - p. 79 14. Chapter 5: Urban Travel Behavior Study Based on Data Fusion Model - p. 110 15. Chapter 6: Urban Travel Mobility Exploring With Large-Scale Trajectory Data - p. 135 16. Chapter 7: Public Transportation Big Data Mining and Analysis - p. 174 17. Chapter 8: Simulation-Based Optimization for Network Modeling With Heterogeneous Data - p. 200 18. Chapter 9: Network Modelling and Resilience Analysis of Air Transportation: A Data-Driven, Open-Source Approach - p. 225 19. Chapter 10: Health Assessment of Electric Multiple Units - p. 245 20. Index - p. 265
Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Contributors - p. xi 6. List of Figures - p. xiii 7. List of Tables - p. xix 8. Preface - p. xxi 9. Acronyms - p. xxv 10. Chapter 1: Overview of Data-Driven Solutions - p. xxv 11. Chapter 2: Data-Driven Energy Efficient Driving Control in Connected Vehicle Environment - p. 10 12. Chapter 3: Machine Learning and Computer Vision-Enabled Traffic Sensing Data Analysis and Quality Enhancement - p. 49 13. Chapter 4: Data-Driven Approaches for Estimating Travel Time Reliability - p. 79 14. Chapter 5: Urban Travel Behavior Study Based on Data Fusion Model - p. 110 15. Chapter 6: Urban Travel Mobility Exploring With Large-Scale Trajectory Data - p. 135 16. Chapter 7: Public Transportation Big Data Mining and Analysis - p. 174 17. Chapter 8: Simulation-Based Optimization for Network Modeling With Heterogeneous Data - p. 200 18. Chapter 9: Network Modelling and Resilience Analysis of Air Transportation: A Data-Driven, Open-Source Approach - p. 225 19. Chapter 10: Health Assessment of Electric Multiple Units - p. 245 20. Index - p. 265
