000			03154nam a2200385Ia 4500
000			04197nam a22004215i 4500
001			978-981-19-6714-6
003			DE-He213
005			20240319120751.0
007			cr nn 008mamaa
008			221129s2023 si | s |||| 0|eng d
020			_a9789811967146 _9978-981-19-6714-6
082			_a5.7
100			_aPhithakkitnukoon, Santi. _928140
245			_aUrban Informatics Using Mobile Network Data _cby Santi Phithakkitnukoon. _h[electronic resource] :
250			_a1st ed. 2023.
260			_aSingapore _bSpringer Nature Singapore _c2023
300			_aXIII, 241 p. 1 illus. _bonline resource.
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.
650			_aArtificial intelligence _928141
650			_aComputer Application in Social and Behavioral Sciences. _928142
650			_aData Analysis and Big Data. _928143
650			_aData Mining and Knowledge Discovery. _928144
650			_aData mining. _928145
650			_aData Science. _928146
650			_aMethodology of Data Collection and Processing. _928147
650			_aQuantitative research. _928148
650			_aSampling (Statistics). _928149
650			_aSocial sciences _928150
650			_aTraffic engineering. _928151
650			_aTransportation engineering. _928152
650			_aTransportation Technology and Traffic Engineering. _928153
856			_uhttps://doi.org/10.1007/978-981-19-6714-6
942			_cEBK _2ddc
999			_c14932 _d14932