000			03339cam a2200457 i 4500
001			on1368314926
003			OCoLC
005			20250612155527.0
006			m o d
007			cr cnu---unuuu
008			230212t20232023ne a ob 001 0 eng d
040			_aYDX _beng _erda _epn _cYDX _dOPELS _dYDX _dUKAHL _dUKMGB _dOCLCF _dINU _dTFW _dN$T _dOCLCO _dVRC
015			_aGBC2K2149 _2bnb
016	7		_a020802978 _2Uk
019			_a1372578724
020			_a0443184259 _qelectronic book
020			_a9780443184253 _q(electronic bk.)
020			_z9780443184246
020			_z0443184240
035			_a(OCoLC)1368314926 _z(OCoLC)1372578724
050		4	_aQA76.9.B45 _bH352 2023
082	0	4	_a005.7 _223/eng/20230314
245	0	0	_aHandbook of mobility data mining. _nVolume Two, _pMobility analytics and prediction / _cedited by Haoran Zhang.
246	3	0	_aMobility analytics and prediction
264		1	_aAmsterdam, Netherlands ; _aOxford, United Kingdom ; _aCambridge MA : _bElsevier, _c[2023]
264		4	_c�2023.
300			_a1 online resource : _billustrations (some color)
336			_atext _btxt _2rdacontent
336			_astill image _bsti _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
505	0		_aChapter one - Multi-data-based travel behavior analysis and prediction -- Chapter two - Mining individual significant places from historical trajectory data -- Chapter Three - Mobility pattern clustering with big human mobility data -- Chapter Four - Change detection of travel behavior: a case study of COVID-19 -- Chapter Five - User demographic characteristics inference based on big GPS trajectory data -- Chapter Six - Generative model for human mobility -- Chapter seven - Retrieval-based human trajectory generation -- Chapter eight - Grid-based origin-destination matrix prediction: a deep learning method with vector graph transformation similarity loss function -- Chapter Nine - MetaTraj: meta-learning for cross-scene cross-object trajectory prediction -- Chapter Ten - Social-DPF: socially acceptable distribution prediction of futures.
520			_a"Handbook of Mobility Data Mining, Volume Two: Mobility Analytics and Prediction introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book introduces how to design MDM platforms that adapt to the evolving mobility environment and new types of transportation and users."--Provided by Publisher.
504			_aIncludes bibliographical references and index.
588			_aDescription based on online resource; title from digital title page (viewed on March 23, 2023).
650		0	_aBig data.
650		0	_aMobile communication systems.
700	1		_aZhang, Haoran _c(College teacher), _eeditor.
776	0	8	_iPrint version: _z0443184240 _z9780443184246 _w(OCoLC)1321787571
856	4	0	_3ScienceDirect _uhttps://www.sciencedirect.com/science/book/9780443184246
999			_c216589 _d216589