{"id":"https://openalex.org/W2566236551","doi":"https://doi.org/10.1109/itsc.2016.7795689","title":"Forecasting dynamic public transport Origin-Destination matrices with long-Short term Memory recurrent neural networks","display_name":"Forecasting dynamic public transport Origin-Destination matrices with long-Short term Memory recurrent neural networks","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2566236551","doi":"https://doi.org/10.1109/itsc.2016.7795689","mag":"2566236551"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2016.7795689","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2016.7795689","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":"conference-paper","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/A5086337329","display_name":"Florian Toqu\u00e9","orcid":null},"institutions":[{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Florian Toque","raw_affiliation_strings":["Universit\u00e9 Paris-Est, IFSTTAR, COSYS-GRETTIA, Marne-Ia-Vallee, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 Paris-Est, IFSTTAR, COSYS-GRETTIA, Marne-Ia-Vallee, France","institution_ids":["https://openalex.org/I2800365227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087274722","display_name":"\u00c9tienne C\u00f4me","orcid":"https://orcid.org/0000-0002-0459-6388"},"institutions":[{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Etienne Come","raw_affiliation_strings":["Universit\u00e9 Paris-Est, IFSTTAR, COSYS-GRETTIA, Marne-Ia-Vallee, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 Paris-Est, IFSTTAR, COSYS-GRETTIA, Marne-Ia-Vallee, France","institution_ids":["https://openalex.org/I2800365227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029220520","display_name":"Mohamed Khalil El Mahrsi","orcid":"https://orcid.org/0000-0003-4769-0910"},"institutions":[{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Mohamed Khalil El Mahrsi","raw_affiliation_strings":["Universit\u00e9 Paris-Est, IFSTTAR, COSYS-GRETTIA, Marne-Ia-Vallee, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 Paris-Est, IFSTTAR, COSYS-GRETTIA, Marne-Ia-Vallee, France","institution_ids":["https://openalex.org/I2800365227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029516915","display_name":"Latifa Oukhellou","orcid":"https://orcid.org/0000-0002-5193-1732"},"institutions":[{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Latifa Oukhellou","raw_affiliation_strings":["Universit\u00e9 Paris-Est, IFSTTAR, COSYS-GRETTIA, Marne-Ia-Vallee, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 Paris-Est, IFSTTAR, COSYS-GRETTIA, Marne-Ia-Vallee, France","institution_ids":["https://openalex.org/I2800365227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2800365227"],"apc_list":null,"apc_paid":null,"fwci":11.2944,"has_fulltext":false,"cited_by_count":114,"citation_normalized_percentile":{"value":0.98736638,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1071","last_page":"1076"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9994999766349792,"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"}},{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9986000061035156,"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/recurrent-neural-network","display_name":"Recurrent neural network","score":0.7593278884887695},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7148529887199402},{"id":"https://openalex.org/keywords/smart-card","display_name":"Smart card","score":0.6801931858062744},{"id":"https://openalex.org/keywords/public-transport","display_name":"Public transport","score":0.6623405814170837},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6006523370742798},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5999398231506348},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.5413017868995667},{"id":"https://openalex.org/keywords/time-horizon","display_name":"Time horizon","score":0.4200695753097534},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4113697111606598},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3595648407936096},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.35113072395324707},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32906657457351685},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.17114967107772827},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13171857595443726},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.10325160622596741},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.09928420186042786},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08324724435806274}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.7593278884887695},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7148529887199402},{"id":"https://openalex.org/C110406131","wikidata":"https://www.wikidata.org/wiki/Q41349","display_name":"Smart card","level":2,"score":0.6801931858062744},{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.6623405814170837},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6006523370742798},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5999398231506348},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.5413017868995667},{"id":"https://openalex.org/C28761237","wikidata":"https://www.wikidata.org/wiki/Q7805321","display_name":"Time horizon","level":2,"score":0.4200695753097534},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4113697111606598},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3595648407936096},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.35113072395324707},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32906657457351685},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.17114967107772827},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13171857595443726},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.10325160622596741},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.09928420186042786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08324724435806274},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/itsc.2016.7795689","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2016.7795689","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"},{"id":"pmh:oai:HAL:hal-01357641v1","is_oa":false,"landing_page_url":"https://hal.science/hal-01357641","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE 19th International Conference on Intelligent Transportation Systems","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1543780507","https://openalex.org/W1582774210","https://openalex.org/W1942925989","https://openalex.org/W1978481188","https://openalex.org/W1987728022","https://openalex.org/W2004131532","https://openalex.org/W2006781891","https://openalex.org/W2007043321","https://openalex.org/W2007373465","https://openalex.org/W2035971144","https://openalex.org/W2036380561","https://openalex.org/W2044985623","https://openalex.org/W2054007841","https://openalex.org/W2064675550","https://openalex.org/W2065846791","https://openalex.org/W2070751343","https://openalex.org/W2075671353","https://openalex.org/W2076663670","https://openalex.org/W2082533141","https://openalex.org/W2085987121","https://openalex.org/W2090431844","https://openalex.org/W2107878631","https://openalex.org/W2132177526","https://openalex.org/W2160933868","https://openalex.org/W2164379995","https://openalex.org/W2269446275","https://openalex.org/W2301739719","https://openalex.org/W2964121744","https://openalex.org/W3014049250","https://openalex.org/W4301266338","https://openalex.org/W6631190155","https://openalex.org/W6659565444","https://openalex.org/W6667299615","https://openalex.org/W6697988454"],"related_works":["https://openalex.org/W2179452086","https://openalex.org/W2944614267","https://openalex.org/W2912153778","https://openalex.org/W4288108708","https://openalex.org/W4387163678","https://openalex.org/W2973430807","https://openalex.org/W4385280324","https://openalex.org/W2890685186","https://openalex.org/W2984436043","https://openalex.org/W4390245176"],"abstract_inverted_index":{"A":[0,87],"considerable":[1],"number":[2],"of":[3,18,60,113,131,147,156],"studies":[4,20],"have":[5],"been":[6],"undertaken":[7],"on":[8,101,164],"using":[9,79],"smart":[10,104],"card":[11,105],"data":[12,152],"to":[13,22,55,69],"analyse":[14],"urban":[15],"mobility.":[16],"Most":[17],"these":[19],"aim":[21],"identify":[23],"recurrent":[24,84],"passenger":[25,33],"habits,":[26],"reveal":[27],"mobility":[28,37],"patterns,":[29],"reconstruct":[30],"and":[31,47,58],"predict":[32],"flows,":[34],"etc.":[35],"Forecasting":[36],"demand":[38],"is":[39,51,99],"a":[40,76,102,126],"central":[41],"problem":[42],"for":[43],"public":[44,110],"transport":[45,111,158],"authorities":[46],"operators":[48],"alike.":[49],"It":[50],"the":[52,109,138,145,165],"first":[53],"step":[54],"efficient":[56],"allocation":[57],"optimisation":[59],"available":[61],"resources.":[62],"This":[63],"paper":[64],"explores":[65],"an":[66],"innovative":[67],"approach":[68],"forecasting":[70],"dynamic":[71],"Origin-Destination":[72],"(OD)":[73],"matrices":[74,155],"in":[75,161],"subway":[77],"network":[78,112],"long":[80],"Short-term":[81],"Memory":[82],"(LSTM)":[83],"neural":[85],"networks.":[86],"comparison":[88],"with":[89,137,144],"traditional":[90],"approaches,":[91],"such":[92],"as":[93],"calendar":[94],"methodology":[95],"or":[96],"Vector":[97],"Autoregression":[98],"conducted":[100],"real":[103],"dataset":[106],"issued":[107],"from":[108],"Rennes":[114],"M\u00e9tropole,":[115],"France.":[116],"The":[117],"obtained":[118],"results":[119],"show":[120],"that":[121],"reliable":[122],"short-term":[123],"prediction":[124,166],"(over":[125],"15":[127],"minutes":[128],"time":[129],"horizon)":[130],"OD":[132,154],"pairs":[133],"can":[134],"be":[135],"achieved":[136],"proposed":[139],"approach.":[140],"We":[141],"also":[142],"experiment":[143],"effect":[146],"taking":[148],"into":[149],"account":[150],"additional":[151],"about":[153],"nearby":[157],"systems":[159],"(buses":[160],"this":[162],"case)":[163],"accuracy.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}