{"id":"https://openalex.org/W2783305245","doi":"https://doi.org/10.1109/bigdata.2017.8258425","title":"Data analysis on train transportation data with nonnegative matrix factorization","display_name":"Data analysis on train transportation data with nonnegative matrix factorization","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2783305245","doi":"https://doi.org/10.1109/bigdata.2017.8258425","mag":"2783305245"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258425","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","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/A5102195183","display_name":"Kyoichi Ito","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Kyoichi Ito","raw_affiliation_strings":["The University of Tokyo, Institute of Industrial Science, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Institute of Industrial Science, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029829747","display_name":"Masaki Ito","orcid":"https://orcid.org/0000-0002-8032-7198"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masaki Ito","raw_affiliation_strings":["The University of Tokyo, Institute of Industrial Science, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Institute of Industrial Science, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072559254","display_name":"Kosuke Miyazaki","orcid":null},"institutions":[{"id":"https://openalex.org/I4210160866","display_name":"National Institute of Technology, Kagawa College","ror":"https://ror.org/04xfwhf17","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210120810","https://openalex.org/I4210160866"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kosuke Miyazaki","raw_affiliation_strings":["Kagawa College, National Institute of Technology, Kagawa, Japan"],"affiliations":[{"raw_affiliation_string":"Kagawa College, National Institute of Technology, Kagawa, Japan","institution_ids":["https://openalex.org/I4210160866"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108236928","display_name":"Keishi TANIMOTO","orcid":null},"institutions":[{"id":"https://openalex.org/I4588055","display_name":"Tottori University","ror":"https://ror.org/024yc3q36","country_code":"JP","type":"education","lineage":["https://openalex.org/I4588055"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Keishi Tanimoto","raw_affiliation_strings":["Department of Social Systems and Civil Engineering, Tottori University, Tottori, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Social Systems and Civil Engineering, Tottori University, Tottori, Japan","institution_ids":["https://openalex.org/I4588055"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050720322","display_name":"Kaoru Sezaki","orcid":"https://orcid.org/0000-0003-1194-4632"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kaoru Sezaki","raw_affiliation_strings":["The University of Tokyo, Centor for Spatial Information Science, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Centor for Spatial Information Science, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102195183"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.19933761,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":95},"biblio":{"volume":"401","issue":null,"first_page":"4080","last_page":"4085"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9923999905586243,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9812999963760376,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.636182963848114},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.6185475587844849},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.6173916459083557},{"id":"https://openalex.org/keywords/matrix-algebra","display_name":"Matrix algebra","score":0.4732261896133423},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.456257164478302},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.4370936155319214},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3097303807735443}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.636182963848114},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6185475587844849},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.6173916459083557},{"id":"https://openalex.org/C2988995629","wikidata":"https://www.wikidata.org/wiki/Q2915729","display_name":"Matrix algebra","level":3,"score":0.4732261896133423},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.456257164478302},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.4370936155319214},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3097303807735443},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8258425","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1246381107","https://openalex.org/W1902027874","https://openalex.org/W1988759034","https://openalex.org/W1991551840","https://openalex.org/W2080731889","https://openalex.org/W2092676845","https://openalex.org/W2101823987","https://openalex.org/W2135029798","https://openalex.org/W2301739719","https://openalex.org/W3143596294","https://openalex.org/W6697988454"],"related_works":["https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2972997031","https://openalex.org/W34555840","https://openalex.org/W2069570686"],"abstract_inverted_index":{"In":[0,78],"light":[1],"of":[2,26,35,43,75,94,114,116],"the":[3,15,33,41,46,61,85,90,95,112],"recognized":[4],"need":[5],"to":[6,11,38,54,70,107,133],"collect":[7],"and":[8,28,48,72,137,140],"analyze":[9],"data":[10,49,58,96],"maintain":[12],"urban":[13,36,76],"development,":[14],"\u201csmart":[16],"city\u201d":[17],"concept":[18],"has":[19,31],"gained":[20],"much":[21],"attention":[22],"recently.":[23],"The":[24,127],"development":[25],"sensing":[27],"information":[29,47],"techniques":[30],"facilitated":[32],"analysis":[34],"mobility":[37],"better":[39],"understand":[40],"characteristics":[42,139],"cities.":[44],"Of":[45],"that":[50],"can":[51],"be":[52],"used":[53],"characterize":[55],"cities,":[56],"transportation":[57,65],"are":[59],"among":[60],"most":[62],"useful":[63],"because":[64],"is":[66],"so":[67],"closely":[68],"related":[69],"human":[71],"other":[73],"aspects":[74],"mobility.":[77],"extracting":[79],"features":[80,131],"from":[81,89],"automatically":[82],"collected":[83],"data,":[84],"greatest":[86],"difficulty":[87],"comes":[88],"size":[91],"or":[92,105],"complexity":[93],"set,":[97],"as":[98],"these":[99],"often":[100],"have":[101],"too":[102],"many":[103],"attributes":[104],"indices":[106],"analyze.":[108],"This":[109],"paper":[110],"discusses":[111],"results":[113,128],"analyses":[115],"smart":[117],"card":[118],"ticketing":[119],"authentication":[120],"logs":[121],"using":[122],"nonnegative":[123],"matrix":[124],"factorization":[125],"(NMF).":[126],"present":[129],"extracted":[130],"applicable":[132],"assessing":[134],"various":[135],"user":[136],"station":[138],"dynamics.":[141]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}