{"id":"https://openalex.org/W4252855139","doi":"https://doi.org/10.1109/cnsm.2016.7818454","title":"An improved Markov method for prediction of user mobility","display_name":"An improved Markov method for prediction of user mobility","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W4252855139","doi":"https://doi.org/10.1109/cnsm.2016.7818454"},"language":"en","primary_location":{"id":"doi:10.1109/cnsm.2016.7818454","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnsm.2016.7818454","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 12th International Conference on Network and Service Management (CNSM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006612493","display_name":"Yihang Cheng","orcid":"https://orcid.org/0000-0003-2396-0306"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yihang Cheng","raw_affiliation_strings":["Beijing Laboratory of Advanced Information Networks of BUPT, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Networks of BUPT, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090482974","display_name":"Yuanyuan Qiao","orcid":"https://orcid.org/0000-0002-3573-9847"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Qiao","raw_affiliation_strings":["Beijing Key Laboratory of Network System Architecture and Convergence of BUPT, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Network System Architecture and Convergence of BUPT, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032734064","display_name":"Jie Yang","orcid":"https://orcid.org/0000-0002-0294-1037"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Yang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5006612493"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.4912,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.94836641,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"394","last_page":"399"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11896","display_name":"Opportunistic and Delay-Tolerant Networks","score":0.9787999987602234,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10298","display_name":"Urban Transport and Accessibility","score":0.9627000093460083,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.840087890625},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.7003512978553772},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.63509202003479},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.6184557676315308},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5885981917381287},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5032438635826111},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.47379323840141296},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4538312554359436},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4243845045566559},{"id":"https://openalex.org/keywords/mobile-telephony","display_name":"Mobile telephony","score":0.41729283332824707},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3514619469642639},{"id":"https://openalex.org/keywords/mobile-radio","display_name":"Mobile radio","score":0.22029367089271545},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.15295416116714478}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.840087890625},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7003512978553772},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.63509202003479},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.6184557676315308},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5885981917381287},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5032438635826111},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.47379323840141296},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4538312554359436},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4243845045566559},{"id":"https://openalex.org/C95491727","wikidata":"https://www.wikidata.org/wiki/Q992968","display_name":"Mobile telephony","level":3,"score":0.41729283332824707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3514619469642639},{"id":"https://openalex.org/C2781307350","wikidata":"https://www.wikidata.org/wiki/Q6887221","display_name":"Mobile radio","level":2,"score":0.22029367089271545},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.15295416116714478},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnsm.2016.7818454","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnsm.2016.7818454","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 12th International Conference on Network and Service Management (CNSM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W66477288","https://openalex.org/W1524104700","https://openalex.org/W1651166699","https://openalex.org/W1827223372","https://openalex.org/W1878544709","https://openalex.org/W1964461063","https://openalex.org/W1982300822","https://openalex.org/W1987228002","https://openalex.org/W1997203390","https://openalex.org/W2012580531","https://openalex.org/W2013551812","https://openalex.org/W2024877464","https://openalex.org/W2027660225","https://openalex.org/W2043330589","https://openalex.org/W2051176066","https://openalex.org/W2053571848","https://openalex.org/W2056284729","https://openalex.org/W2090978188","https://openalex.org/W2147363999","https://openalex.org/W2171267091","https://openalex.org/W2339435260","https://openalex.org/W3099023905","https://openalex.org/W6631370270","https://openalex.org/W6638501158","https://openalex.org/W6703762999","https://openalex.org/W7039844959"],"related_works":["https://openalex.org/W1510894296","https://openalex.org/W2134386692","https://openalex.org/W2194396582","https://openalex.org/W2082284720","https://openalex.org/W2116722627","https://openalex.org/W2537260108","https://openalex.org/W2158700654","https://openalex.org/W2379938888","https://openalex.org/W2566202039","https://openalex.org/W4233405330"],"abstract_inverted_index":{"The":[0],"developments":[1],"of":[2,10,22,30,37,55,80,90,96,128,142,147],"Information":[3],"and":[4,8,20,72,131,144,163,189],"Communication":[5],"Technology":[6],"(ICT)":[7],"Internet":[9],"Things":[11],"(IoT)":[12],"are":[13,78],"being":[14],"used":[15],"to":[16,58,137,192],"enhance":[17],"quality,":[18],"performance":[19,111,175],"interactivity":[21],"urban":[23],"services.":[24],"Benefited":[25],"from":[26,47],"the":[27,82,104,139,184,194],"widespread":[28],"adoption":[29],"mobile":[31,38,49,91],"devices,":[32],"we":[33,93,151,181],"can":[34],"collect":[35],"amount":[36],"data":[39,50,89],"for":[40,53,99,199],"user":[41],"mobility":[42],"analysis.":[43],"Mining":[44],"hidden":[45],"information":[46],"users'":[48],"is":[51,123],"important":[52],"builders":[54],"smart":[56],"city":[57],"provide":[59],"better":[60,110,174],"location-based":[61],"service.":[62],"This":[63],"paper":[64],"focuses":[65],"on":[66,157],"two":[67],"classical":[68],"domain-independent":[69],"prediction":[70,121,126,140,149,187,197],"models":[71],"one":[73],"improved":[74,170],"Markov":[75,106,143],"model":[76],"that":[77,103],"capable":[79],"estimating":[81],"next":[83],"location.":[84],"By":[85],"using":[86],"27-day-long":[87],"traffic":[88],"network,":[92],"extract":[94],"trajectories":[95],"4914":[97],"individuals":[98],"experiments.":[100],"We":[101],"find":[102],"original":[105],"algorithm":[107,198],"has":[108,172],"a":[109,153,173,200],"in":[112,176],"resource":[113],"consumption":[114],"than":[115,125],"LZ":[116],"family":[117],"algorithms,":[118,150],"but":[119],"its":[120],"accuracy":[122,127,141,188],"lower":[124],"LeZi":[129,133],"Update":[130],"Active":[132],"algorithm.":[134],"In":[135,179],"order":[136],"improve":[138],"overcome":[145],"drawbacks":[146],"traditional":[148],"present":[152],"new":[154],"method":[155,171],"based":[156],"Markov,":[158],"which":[159],"considers":[160],"both":[161],"temporal":[162],"spatial":[164],"factors.":[165],"Extensive":[166],"experiments":[167],"demonstrate":[168],"our":[169],"location":[177],"prediction.":[178],"addition,":[180],"further":[182],"study":[183],"relationship":[185],"between":[186],"trajectory's":[190],"regularity,":[191],"identify":[193],"most":[195],"suitable":[196],"trajectory.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}