This book discusses the role of mobile network data in urban informatics, particularly how mobile network data is utilized in the mobility context, where approaches, models, and systems are developed for understanding travel behavior. The objectives of this book are thus to evaluate the extent to which mobile network data reflects travel behavior and to develop guidelines on how to best use such data to understand and model travel behavior. To achieve these objectives, the book attempts to evaluate the strengths and weaknesses of this data source for urban informatics and its applicability to the development and implementation of travel behavior models through a series of the authors’ research studies.
Traditionally, survey-based information is used as an input for travel demand models that predict future travel behavior and transportation needs. A survey-based approach is however costly and time-consuming, and hence its information can be dated and limited to a particular region. Mobile network data thus emerges as a promising alternative data source that is massive in both cross-sectional and longitudinal perspectives, and one that provides both broader geographic coverage of travelers and longer-term travel behavior observation. The two most common types of travel demand model that have played an essential role in managing and planning for transportation systems are four-step models and activity-based models. The book’s chapters are structured on the basis of these travel demand models in order to provide researchers and practitioners with an understanding of urban informatics and the important role that mobile network data plays in advancing the state of the art from the perspectives of travel behavior research.
Author(s): Santi Phithakkitnukoon
Publisher: Springer
Year: 2022
Language: English
Pages: 245
City: Singapore
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
