Intro
Preface
Acknowledgments
Contents
About the Author
1: The Overview of Mobile Network Data-Driven Urban Informatics
1.1 Urban Informatics
1.2 Traditional Methods in Travel Behavior Understanding
1.3 Mobile Network-Based Travel Behavior Data Sensing
1.4 Mobile Network Data-Based Travel Behavior Inference
References
2: Inferring Passenger Travel Demand Using Mobile Phone CDR Data
2.1 Motivation and State of the Art
2.2 Case Study Area and Dataset
2.2.1 Case Study Area
2.2.2 Transit Profile of Case Study Area
2.2.2.1 Bus Service

2.2.2.2 Taxi Service
2.2.3 Dataset
2.2.3.1 Mobile Network Data
2.2.3.2 Bus Data
2.3 Methodology and Results
2.4 Validation
2.5 Discussion of Potential Applications
2.5.1 Improving the Current Practice of Urban Paratransit Service
2.5.2 Providing Indicators for Potential High Order Public Transport Development
2.5.3 Cost-Effective Transport Planning Approach
2.6 Conclusion
References
3: Modeling Trip Distribution Using Mobile Phone CDR Data
3.1 Motivation and State of the Art
3.2 Methodology
3.2.1 Case Study Region and Dataset

3.2.2 Stay and Pass-by Area Identification
3.2.3 Significant Location Detection
3.2.4 Trip Detection
3.2.5 Trip Types
3.2.6 Trip Correction
3.2.7 Trip Expansion
3.2.8 Trip Distribution Modeling
3.2.8.1 Gravity Models
3.2.8.2 Log-Linear Models
3.3 Results and Discussion
3.3.1 Travel Distances
3.3.2 Trip Distribution Models
3.3.3 Log-Linear Model-Based Approaches
3.3.4 Trip Distance Distribution
3.4 Conclusion
References
4: Inferring and Modeling Migration Flows Using Mobile Phone CDR Data
4.1 Motivation and State of the Art
4.2 Methodology

4.2.1 Dataset
4.2.2 Subjects
4.2.3 Migration Flow Inference
4.2.4 Migration Flow Modelling
4.2.4.1 Expansion of Migration Trips
4.2.4.2 Migration Trip Distribution Modeling
4.2.4.2.1 Gravity Model
4.2.4.2.2 Log-Linear Model
4.2.4.2.3 Radiation Model
4.2.4.3 Generalized Cost
4.2.4.3.1 Travel Cost Measurements
4.2.4.3.1.1 Displacement
4.2.4.3.1.2 Road Network Distance
4.2.4.3.1.3 Monetary Cost
4.2.4.3.2 Reference Points
4.2.4.3.2.1 District Centroids
4.2.4.3.2.2 Farthest Cell Towers
4.2.4.3.2.3 Nearest Cell Towers
4.3 Results

4.3.1 Log-Linear model
4.3.2 Gravity Model
4.3.3 Radiation Model
4.4 Conclusion
References
5: Inferring Social Influence in Transport Mode Choice Using Mobile Phone CDR Data
5.1 Motivation and State of the Art
5.1.1 Social Influence on Travel Behavior
5.1.2 Mobile Sensing Approach in Behavior Analysis
5.2 Methodology
5.2.1 Subject Selection
5.2.2 Residence and Work Location Inference
5.2.3 Social Tie Strength Inference
5.2.4 Transport Mode Inference
5.3 Results
5.3.1 Commute Mode Choices of Social Ties
5.3.2 Social Distance