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001453405 019__ $$a1352973537$$a1366166107
001453405 020__ $$a9789811967146$$q(electronic bk.)
001453405 020__ $$a9811967148$$q(electronic bk.)
001453405 020__ $$z981196713X
001453405 020__ $$z9789811967139
001453405 0247_ $$a10.1007/978-981-19-6714-6$$2doi
001453405 035__ $$aSP(OCoLC)1352623354
001453405 040__ $$aYDX$$beng$$cYDX$$dEBLCP$$dGW5XE$$dOCLCF$$dSFB$$dBRX
001453405 049__ $$aISEA
001453405 050_4 $$aHE336.A8
001453405 08204 $$a388.40285$$223/eng/20221219
001453405 1001_ $$aPhithakkitnukoon, Santi.
001453405 24510 $$aUrban informatics using mobile network data :$$btravel behavior research perspectives /$$cSanti Phithakkitnukoon.
001453405 260__ $$aSingapore :$$bSpringer,$$c2023.
001453405 300__ $$a1 online resource
001453405 336__ $$atext$$btxt$$2rdacontent
001453405 337__ $$acomputer$$bc$$2rdamedia
001453405 338__ $$aonline resource$$bcr$$2rdacarrier
001453405 5050_ $$aIntro -- Preface -- Acknowledgments -- Contents -- About the Author -- 1: The Overview of Mobile Network Data-Driven Urban Informatics -- 1.1 Urban Informatics -- 1.2 Traditional Methods in Travel Behavior Understanding -- 1.3 Mobile Network-Based Travel Behavior Data Sensing -- 1.4 Mobile Network Data-Based Travel Behavior Inference -- References -- 2: Inferring Passenger Travel Demand Using Mobile Phone CDR Data -- 2.1 Motivation and State of the Art -- 2.2 Case Study Area and Dataset -- 2.2.1 Case Study Area -- 2.2.2 Transit Profile of Case Study Area -- 2.2.2.1 Bus Service
001453405 5058_ $$a2.2.2.2 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
001453405 5058_ $$a3.2.2 Stay and Pass-by Area Identification -- 3.2.3 Significant Location Detection -- 3.2.4 Trip Detection -- 3.2.5 Trip Types -- 3.2.6 Trip Correction -- 3.2.7 Trip Expansion -- 3.2.8 Trip Distribution Modeling -- 3.2.8.1 Gravity Models -- 3.2.8.2 Log-Linear Models -- 3.3 Results and Discussion -- 3.3.1 Travel Distances -- 3.3.2 Trip Distribution Models -- 3.3.3 Log-Linear Model-Based Approaches -- 3.3.4 Trip Distance Distribution -- 3.4 Conclusion -- References -- 4: Inferring and Modeling Migration Flows Using Mobile Phone CDR Data -- 4.1 Motivation and State of the Art -- 4.2 Methodology
001453405 5058_ $$a4.2.1 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
001453405 5058_ $$a4.3.1 Log-Linear model -- 4.3.2 Gravity Model -- 4.3.3 Radiation Model -- 4.4 Conclusion -- References -- 5: Inferring Social Influence in Transport Mode Choice Using Mobile Phone CDR Data -- 5.1 Motivation and State of the Art -- 5.1.1 Social Influence on Travel Behavior -- 5.1.2 Mobile Sensing Approach in Behavior Analysis -- 5.2 Methodology -- 5.2.1 Subject Selection -- 5.2.2 Residence and Work Location Inference -- 5.2.3 Social Tie Strength Inference -- 5.2.4 Transport Mode Inference -- 5.3 Results -- 5.3.1 Commute Mode Choices of Social Ties -- 5.3.2 Social Distance
001453405 506__ $$aAccess limited to authorized users.
001453405 520__ $$aThis book discusses the role of mobile network data in urban informatics, particularly how mobile network data is utilized in the mobility context, where approaches, models, and systems are developed for understanding travel behavior. The objectives of this book are thus to evaluate the extent to which mobile network data reflects travel behavior and to develop guidelines on how to best use such data to understand and model travel behavior. To achieve these objectives, the book attempts to evaluate the strengths and weaknesses of this data source for urban informatics and its applicability to the development and implementation of travel behavior models through a series of the authors research studies. Traditionally, survey-based information is used as an input for travel demand models that predict future travel behavior and transportation needs. A survey-based approach is however costly and time-consuming, and hence its information can be dated and limited to a particular region. Mobile network data thus emerges as a promising alternative data source that is massive in both cross-sectional and longitudinal perspectives, and one that provides both broader geographic coverage of travelers and longer-term travel behavior observation. The two most common types of travel demand model that have played an essential role in managing and planning for transportation systems are four-step models and activity-based models. The books chapters are structured on the basis of these travel demand models in order to provide researchers and practitioners with an understanding of urban informatics and the important role that mobile network data plays in advancing the state of the art from the perspectives of travel behavior research.
001453405 588__ $$aDescription based on print version record.
001453405 650_0 $$aUrban transportation$$xData processing.
001453405 650_0 $$aMobile communication systems.
001453405 655_0 $$aElectronic books.
001453405 77608 $$iPrint version: $$z981196713X$$z9789811967139$$w(OCoLC)1342491332
001453405 852__ $$bebk
001453405 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-6714-6$$zOnline Access$$91397441.1
001453405 909CO $$ooai:library.usi.edu:1453405$$pGLOBAL_SET
001453405 980__ $$aBIB
001453405 980__ $$aEBOOK
001453405 982__ $$aEbook
001453405 983__ $$aOnline
001453405 994__ $$a92$$bISE