Urban Informatics Using Mobile Network Data

Travel Behavior Research Perspectives
1. Auflage 2023
ISBN: 978-981-19-6713-9
Verlag: Springer Nature Singapore

Buch, Englisch, 241 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 553 g

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 aparticular 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.

Zielgruppe

Research

Autoren/Hrsg.

Phithakkitnukoon, Santi

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Verkehrstechnologie: Allgemeines
• Technische Wissenschaften Bauingenieurwesen Verkehrsingenieurwesen, Verkehrsplanung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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.

Über Autor(innen)

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.