Urban Informatics Using Mobile Network Data: Travel Behavior Research Perspectives

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
		5.3.3 Physical Distance
		5.3.4 Ego-Network Effect
	5.4 Conclusion
	References
6: Inferring Route Choice Using Mobile Phone CDR Data
	6.1 Motivation and State of the Art
	6.2 Methodology
		6.2.1 Dataset
		6.2.2 Residence and Work Location Inference
		6.2.3 Route Choices
		6.2.4 Route Choice Inference Methods
			6.2.4.1 Interpolation-Based Method
			6.2.4.2 Shortest Distance-Based Method
			6.2.4.3 Voronoi Cell-Based Method
			6.2.4.4 Visited Voronoi Cell-Based Method
			6.2.4.5 Noise Filtering
	6.3 Results
		6.3.1 Interpolation-Based Methods
		6.3.2 Shortest Distance-Based Methods
		6.3.3 Voronoi Cell-Based Methods
		6.3.4 Visited Voronoi Cell-Based Methods
		6.3.5 Result Summary
	6.4 Conclusion
	References
7: Analysis of Weather Effects on People´s Daily Activity Patterns Using Mobile Phone GPS Data
	7.1 Motivation and State of the Art
	7.2 Methodology
		7.2.1 Datasets
		7.2.2 Analysis
	7.3 Results
		7.3.1 Weather Effects on Mobility and Stop Duration
		7.3.2 Weather Effects on Activities at Different Times of the Day
		7.3.3 Weather Effects on Activities in Different Areas
	7.4 Conclusion
	References
8: Analysis of Tourist Behavior Using Mobile Phone GPS Data
	8.1 Motivation and State of the Art
	8.2 Methodology
		8.2.1 Dataset
		8.2.2 Residence and Workplace Location Detection
		8.2.3 Touristic Trip Inference
	8.3 Analysis of Tourist Behavior
		8.3.1 Amount of Touristic Trips
		8.3.2 Time Spent at Destination
		8.3.3 Mode of Transportation
		8.3.4 Relationship Between Personal Mobility and Travel Behavior
	8.4 Analysis of Similarity in Travel Behavior
	8.5 Application
	8.6 Conclusion
	References
9: An Outlook for Future Mobile Network Data-Driven Urban Informatics
	9.1 Mobile Network Data Characteristics
	9.2 Data Collection
	9.3 Data Uncertainty and Privacy
	9.4 Travel Behavior Pattern Mining
		9.4.1 Group Movement Pattern Mining
		9.4.2 Trajectory Clustering
			9.4.2.1 Trajectory Data Preparation
			9.4.2.2 Distance Measurements
			9.4.2.3 Clustering Models
				9.4.2.3.1 Densely Clustering
				9.4.2.3.2 Hierarchical Clustering
				9.4.2.3.3 Spectral Clustering
		9.4.3 Sequential Pattern Mining
	9.5 Conclusion
	References