{"id":"https://openalex.org/W2510229614","doi":"https://doi.org/10.1109/itsc.2016.7795730","title":"Smart card in public transportation: Designing a analysis system at the human scale","display_name":"Smart card in public transportation: Designing a analysis system at the human scale","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2510229614","doi":"https://doi.org/10.1109/itsc.2016.7795730","mag":"2510229614"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2016.7795730","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2016.7795730","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)","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/A5046268396","display_name":"Emeric Tonnelier","orcid":"https://orcid.org/0009-0007-4353-7536"},"institutions":[{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Emeric Tonnelier","raw_affiliation_strings":["Sorbonne Universite Campus Pierre et Marie Curie, Paris, \u00c3\u017dle-de-France, FR"],"affiliations":[{"raw_affiliation_string":"Sorbonne Universite Campus Pierre et Marie Curie, Paris, \u00c3\u017dle-de-France, FR","institution_ids":["https://openalex.org/I39804081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005493916","display_name":"Nicolas Baskiotis","orcid":"https://orcid.org/0000-0001-5015-0961"},"institutions":[{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Nicolas Baskiotis","raw_affiliation_strings":["Sorbonne Universite Campus Pierre et Marie Curie, Paris, \u00c3\u017dle-de-France, FR"],"affiliations":[{"raw_affiliation_string":"Sorbonne Universite Campus Pierre et Marie Curie, Paris, \u00c3\u017dle-de-France, FR","institution_ids":["https://openalex.org/I39804081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044389669","display_name":"Vincent Guigue","orcid":"https://orcid.org/0000-0002-1450-5566"},"institutions":[{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Vincent Guigue","raw_affiliation_strings":["Sorbonne Universite Campus Pierre et Marie Curie, Paris, \u00c3\u017dle-de-France, FR"],"affiliations":[{"raw_affiliation_string":"Sorbonne Universite Campus Pierre et Marie Curie, Paris, \u00c3\u017dle-de-France, FR","institution_ids":["https://openalex.org/I39804081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113667126","display_name":"Patrick Gallinari","orcid":"https://orcid.org/0000-0003-3360-8269"},"institutions":[{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Patrick Gallinari","raw_affiliation_strings":["Sorbonne Universite Campus Pierre et Marie Curie, Paris, \u00c3\u017dle-de-France, FR"],"affiliations":[{"raw_affiliation_string":"Sorbonne Universite Campus Pierre et Marie Curie, Paris, \u00c3\u017dle-de-France, FR","institution_ids":["https://openalex.org/I39804081"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5046268396"],"corresponding_institution_ids":["https://openalex.org/I39804081"],"apc_list":null,"apc_paid":null,"fwci":1.6357,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.89876578,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":"36","issue":null,"first_page":"1336","last_page":"1341"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9962999820709229,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7818751335144043},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.7377903461456299},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6309773921966553},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5254082679748535},{"id":"https://openalex.org/keywords/public-transport","display_name":"Public transport","score":0.5101259350776672},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5075899362564087},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.48669058084487915},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.46000391244888306},{"id":"https://openalex.org/keywords/smart-card","display_name":"Smart card","score":0.42944595217704773},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3776397407054901},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3552718758583069},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1277565062046051},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.12274965643882751}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7818751335144043},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.7377903461456299},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6309773921966553},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5254082679748535},{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.5101259350776672},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5075899362564087},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.48669058084487915},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.46000391244888306},{"id":"https://openalex.org/C110406131","wikidata":"https://www.wikidata.org/wiki/Q41349","display_name":"Smart card","level":2,"score":0.42944595217704773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3776397407054901},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3552718758583069},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1277565062046051},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.12274965643882751},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2016.7795730","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2016.7795730","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W206743705","https://openalex.org/W1246381107","https://openalex.org/W1956951577","https://openalex.org/W1963757997","https://openalex.org/W1964461063","https://openalex.org/W1983638675","https://openalex.org/W1987228002","https://openalex.org/W2000603369","https://openalex.org/W2007043321","https://openalex.org/W2007582728","https://openalex.org/W2028893095","https://openalex.org/W2038921590","https://openalex.org/W2044985623","https://openalex.org/W2054007841","https://openalex.org/W2054505936","https://openalex.org/W2082729958","https://openalex.org/W2116216716","https://openalex.org/W2129343844","https://openalex.org/W2135046866","https://openalex.org/W2151693816","https://openalex.org/W2165783061","https://openalex.org/W2301739719","https://openalex.org/W2400505421","https://openalex.org/W2799061466","https://openalex.org/W3014049250","https://openalex.org/W3143596294","https://openalex.org/W4302161581"],"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/W2179452086","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2972997031","https://openalex.org/W34555840"],"abstract_inverted_index":{"In":[0,79],"the":[1,16,26,35,52,60,126,135,138,151,173,175],"20th":[2],"century,":[3],"most":[4],"mobility":[5,136],"studies":[6],"were":[7],"based":[8,85,117],"on":[9,86,134,150,166],"costly":[10],"surveys":[11],"with":[12],"few":[13],"samples;":[14],"nowadays,":[15],"data":[17,39,72,109],"from":[18,69,104,160],"static":[19],"and":[20,46,76,99,122,148],"mobile":[21],"sensors":[22,38,71],"allow":[23],"to":[24,96,131,144],"track":[25],"habits":[27],"of":[28,32,37,54,106,125,137],"a":[29,74,82,111,120,167],"massive":[30],"number":[31],"citizens.":[33],"However,":[34],"counterpart":[36],"is":[40,62,73,92,142],"that":[41],"they":[42],"generally":[43],"provide":[44],"noisy":[45],"partial":[47],"signals":[48],"lacking":[49],"semantic":[50,67,132],"information:":[51],"purpose":[53],"each":[55],"human":[56,152],"activity":[57],"captured":[58],"by":[59,172],"sensor":[61],"unknown.":[63],"Extracting":[64],"this":[65,80],"latent":[66],"information":[68,133],"raw":[70],"challenging":[75],"crucial":[77],"task.":[78],"paper,":[81],"novel":[83],"algorithm":[84,118],"non":[87],"negative":[88],"matrix":[89],"factorization":[90],"(NMF)":[91],"proposed":[93,115],"in":[94,110],"order":[95],"extract":[97],"precise":[98],"meaningful":[100],"user":[101],"temporal":[102],"profiles":[103,127,156],"logs":[105],"smart":[107],"card":[108],"transportation":[112],"system.":[113],"The":[114,140],"NMF":[116],"allows":[119],"natural":[121],"informative":[123],"clustering":[124],"which":[128],"can":[129],"lead":[130],"users.":[139],"approach":[141],"compared":[143],"4":[145],"others":[146],"algorithms":[147],"focuses":[149],"scale,":[153],"indeed,":[154],"individual":[155],"differ":[157],"quite":[158],"substantially":[159],"group":[161],"profiles.":[162],"Experiments":[163],"are":[164],"conducted":[165],"3":[168],"months":[169],"dataset":[170],"supplied":[171],"STIF,":[174],"Parisian":[176],"public":[177],"transport":[178],"authority.":[179]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
