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020			_a9789811967146
024	7		_a10.1007/978-981-19-6714-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aHE336 .A8 _b2023 EB
100	1		_aPhithakkitnukoon, Santi _eautor _0(orcid)0000-0002-5716-9363 _1https://orcid.org/0000-0002-5716-9363 _4http://id.loc.gov/vocabulary/relators/aut _9689419
245	1	0	_aUrban Informatics Using Mobile Network Data : _bTravel Behavior Research Perspectives _cby Santi Phithakkitnukoon
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aChapter 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.
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 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.
988			_aSpringer_Computer_2023
650		7	_2embne _9144847 _aIngeniería de tráfico _xProceso de datos
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-6714-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eb _zSI