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Author:   Santi Phithakkitnukoon
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Verlag, Singapore
Edition:   1st ed. 2023
Weight:   0.397kg
ISBN:  

9789811967160

ISBN 10:   9811967164
Pages:   241
Publication Date:   01 December 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

Chapter 1 The Overview of Mobile Network Data-Driven Urban Informatics.- Chapter 2 Inferring Passenger Travel Demand Using Mobile Phone CDR Data.- Chapter 3 Modeling Trip Distribution Using Mobile Phone CDR Data.- Chapter 4 Inferring and Modeling Migration Flows Using Mobile Phone CDR Data.- Chapter 5 Inferring Social Influence in Transport Mode Choice Using Mobile Phone CDR Data.- Chapter 6 Inferring Route Choice Using Mobile Phone CDR Data.- Chapter 7 Analysis of Weather Effects on People’s Daily Activity Patterns Using Mobile Phone GPS Data.- Chapter 8 Analysis of Tourist Behavior Using Mobile Phone GPS Data.- Chapter 9 An Outlook for Future Mobile Network Data-Driven Urban Informatics.

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Author Information

Santi Phithakkitnukoon is originally from Chiang Mai, Thailand. Santi is currently an Associate Professor with the Department of Computer Engineering, Faculty of Engineering, Chiang Mai University. He received B.S. and M.S. degrees in electrical engineering from the Southern Methodist University, USA, in 2003 and 2005, respectively, and a Ph.D. in computer science and engineering from the University of North Texas, USA. Before joining Chiang Mai University, he was a Lecturer in Computing with The Open University, U.K., a Research Associate with the Culture Lab (now known as Open Lab), Newcastle University, U.K., and a Postdoctoral Fellow with the SENSEable City Lab, Massachusetts Institute of Technology, USA. His research interest is in urban informatics, particularly in analyzing large-scale digital footprints such as mobile phone CDRs, GPS traces, social media data, and opportunistic sensing data sources to better understand human behavior and urban dynamics.

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