{"id":"https://openalex.org/W2783913168","doi":"https://doi.org/10.1109/iske.2017.8258756","title":"Dynamic Bayesian networks with Gaussian mixture models for short-term passenger flow forecasting","display_name":"Dynamic Bayesian networks with Gaussian mixture models for short-term passenger flow forecasting","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2783913168","doi":"https://doi.org/10.1109/iske.2017.8258756","mag":"2783913168"},"language":"en","primary_location":{"id":"doi:10.1109/iske.2017.8258756","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iske.2017.8258756","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","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/A5076239050","display_name":"J\u00e9r\u00e9my Roos","orcid":"https://orcid.org/0000-0003-2848-8514"},"institutions":[{"id":"https://openalex.org/I100532134","display_name":"Universit\u00e9 Claude Bernard Lyon 1","ror":"https://ror.org/029brtt94","country_code":"FR","type":"education","lineage":["https://openalex.org/I100532134","https://openalex.org/I203339264"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Jeremy Roos","raw_affiliation_strings":["ERIC EA 3083, RATP - Universit\u00e9 de Lyon, Paris, France"],"affiliations":[{"raw_affiliation_string":"ERIC EA 3083, RATP - Universit\u00e9 de Lyon, Paris, France","institution_ids":["https://openalex.org/I100532134"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088568974","display_name":"St\u00e9phane Bonnevay","orcid":null},"institutions":[{"id":"https://openalex.org/I100532134","display_name":"Universit\u00e9 Claude Bernard Lyon 1","ror":"https://ror.org/029brtt94","country_code":"FR","type":"education","lineage":["https://openalex.org/I100532134","https://openalex.org/I203339264"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Stephane Bonnevay","raw_affiliation_strings":["ERIC EA 3083, Universit\u00e9 de Lyon, Villeurbanne, France"],"affiliations":[{"raw_affiliation_string":"ERIC EA 3083, Universit\u00e9 de Lyon, Villeurbanne, France","institution_ids":["https://openalex.org/I100532134"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061837009","display_name":"G\u00e9rald Gavin","orcid":null},"institutions":[{"id":"https://openalex.org/I100532134","display_name":"Universit\u00e9 Claude Bernard Lyon 1","ror":"https://ror.org/029brtt94","country_code":"FR","type":"education","lineage":["https://openalex.org/I100532134","https://openalex.org/I203339264"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Gerald Gavin","raw_affiliation_strings":["ERIC EA 3083, Universit\u00e9 de Lyon, Villeurbanne, France"],"affiliations":[{"raw_affiliation_string":"ERIC EA 3083, Universit\u00e9 de Lyon, Villeurbanne, France","institution_ids":["https://openalex.org/I100532134"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5076239050"],"corresponding_institution_ids":["https://openalex.org/I100532134"],"apc_list":null,"apc_paid":null,"fwci":1.6538,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.84575919,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9991000294685364,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9991000294685364,"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/T11106","display_name":"Data Management and Algorithms","score":0.9922999739646912,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.991100013256073,"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":0.6665302515029907},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.594851016998291},{"id":"https://openalex.org/keywords/dynamic-bayesian-network","display_name":"Dynamic Bayesian network","score":0.5923870801925659},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5819535851478577},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5397383570671082},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5345404148101807},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.5235211849212646},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.480728417634964},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.41800352931022644},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.4169427156448364},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.40447232127189636},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.363838255405426},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32782673835754395},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2978118658065796},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18960142135620117},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.14829528331756592},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0973818302154541}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6665302515029907},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.594851016998291},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.5923870801925659},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5819535851478577},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5397383570671082},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5345404148101807},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5235211849212646},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.480728417634964},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.41800352931022644},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.4169427156448364},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.40447232127189636},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.363838255405426},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32782673835754395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2978118658065796},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18960142135620117},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.14829528331756592},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0973818302154541},{"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/iske.2017.8258756","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iske.2017.8258756","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-01807067v1","is_oa":false,"landing_page_url":"https://hal.science/hal-01807067","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":"International Conference on Intelligent Systems and Knowledge Engineering, Nov 2017, Nanjing - Jiangsu, China","raw_type":"Conference papers"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W43001522","https://openalex.org/W53197328","https://openalex.org/W626441390","https://openalex.org/W1486632395","https://openalex.org/W1517993545","https://openalex.org/W1580148563","https://openalex.org/W1696331174","https://openalex.org/W1875626450","https://openalex.org/W1896341954","https://openalex.org/W1968589915","https://openalex.org/W1977332637","https://openalex.org/W1985687306","https://openalex.org/W1988420953","https://openalex.org/W2009797633","https://openalex.org/W2012051283","https://openalex.org/W2020294948","https://openalex.org/W2021153764","https://openalex.org/W2025653905","https://openalex.org/W2039417141","https://openalex.org/W2049633694","https://openalex.org/W2073121023","https://openalex.org/W2075356300","https://openalex.org/W2079662306","https://openalex.org/W2080507495","https://openalex.org/W2082533141","https://openalex.org/W2085987121","https://openalex.org/W2090192376","https://openalex.org/W2094350745","https://openalex.org/W2098613108","https://openalex.org/W2109703216","https://openalex.org/W2110575115","https://openalex.org/W2114759290","https://openalex.org/W2115092992","https://openalex.org/W2145039203","https://openalex.org/W2154834035","https://openalex.org/W2159080219","https://openalex.org/W2163150789","https://openalex.org/W2168175751","https://openalex.org/W2170112109","https://openalex.org/W2399175846","https://openalex.org/W2750425604","https://openalex.org/W2758413552","https://openalex.org/W6619978402","https://openalex.org/W6628849183","https://openalex.org/W6637345734","https://openalex.org/W6639558219","https://openalex.org/W6676245295","https://openalex.org/W6677345854","https://openalex.org/W6683076482","https://openalex.org/W6684066714"],"related_works":["https://openalex.org/W2473373438","https://openalex.org/W3183113072","https://openalex.org/W2126934800","https://openalex.org/W2187019487","https://openalex.org/W2368486525","https://openalex.org/W4290803287","https://openalex.org/W1715419791","https://openalex.org/W2077224612","https://openalex.org/W643788828","https://openalex.org/W2153481672"],"abstract_inverted_index":{"A":[0],"dynamic":[1],"Bayesian":[2],"network":[3],"approach":[4,53],"is":[5,15,105],"proposed":[6],"for":[7,95],"short-term":[8],"passenger":[9,110],"flow":[10],"forecasting.":[11],"The":[12,103],"graphical":[13],"structure":[14,76],"based":[16],"on":[17],"the":[18,22,32,40,52,63,67,70,75,78,83,97,108,119],"causal":[19],"relationships":[20,65],"between":[21,66],"flows":[23,111],"and":[24,28,77,117],"their":[25],"spatiotemporal":[26],"neighbourhood,":[27],"takes":[29],"into":[30],"account":[31],"transport":[33],"service.":[34],"In":[35,47,69],"previous":[36],"work,":[37],"we":[38,50,90],"described":[39],"local":[41],"conditional":[42],"distributions":[43],"as":[44],"linear":[45],"Gaussians.":[46],"this":[48],"paper,":[49],"extend":[51],"to":[54,60,88,107],"Gaussian":[55],"mixture":[56],"models":[57],"in":[58],"order":[59],"better":[61],"catch":[62],"nonlinear":[64],"variables.":[68],"presence":[71],"of":[72,100,112],"incomplete":[73],"data,":[74],"parameters":[79],"are":[80],"learned":[81],"by":[82],"structural":[84],"expectation-maximization":[85],"(EM)":[86],"algorithm,":[87],"which":[89],"add":[91],"a":[92],"new":[93],"step":[94],"determining":[96],"optimal":[98],"number":[99],"mixing":[101],"components.":[102],"model":[104],"applied":[106],"on-board":[109],"Paris":[113],"metro":[114],"line":[115],"2":[116],"outperforms":[118],"other":[120],"testing":[121],"methods.":[122]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}