{"id":"https://openalex.org/W7131104813","doi":"https://doi.org/10.1016/j.cie.2026.111924","title":"Multi-Graph Inductive Representation Learning for Large-Scale Urban Rail Demand Prediction under Disruptions","display_name":"Multi-Graph Inductive Representation Learning for Large-Scale Urban Rail Demand Prediction under Disruptions","publication_year":2026,"publication_date":"2026-02-23","ids":{"openalex":"https://openalex.org/W7131104813","doi":"https://doi.org/10.1016/j.cie.2026.111924"},"language":"en","primary_location":{"id":"doi:10.1016/j.cie.2026.111924","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.cie.2026.111924","pdf_url":null,"source":{"id":"https://openalex.org/S196821226","display_name":"Computers & Industrial Engineering","issn_l":"0360-8352","issn":["0360-8352","1879-0550"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers &amp; Industrial Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.cie.2026.111924","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011094724","display_name":"Dang Viet Anh Nguyen","orcid":"https://orcid.org/0009-0000-6208-0876"},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"Dang Viet Anh Nguyen","raw_affiliation_strings":["Department of Technology, Management, and Economics, Technical University of Denmark (DTU), 2800 Kongens Lyngby, Denmark"],"raw_orcid":"https://orcid.org/0009-0000-6208-0876","affiliations":[{"raw_affiliation_string":"Department of Technology, Management, and Economics, Technical University of Denmark (DTU), 2800 Kongens Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107608956","display_name":"Johan Victor Flensburg","orcid":null},"institutions":[{"id":"https://openalex.org/I2802839772","display_name":"Banedanmark (Denmark)","ror":"https://ror.org/013bx1v37","country_code":"DK","type":"company","lineage":["https://openalex.org/I2802839772"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"J. Victor Flensburg","raw_affiliation_strings":["Traffic Division, Banedanmark, 4100 Ringsted, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Traffic Division, Banedanmark, 4100 Ringsted, Denmark","institution_ids":["https://openalex.org/I2802839772"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057630890","display_name":"Fabrizio Cerreto","orcid":"https://orcid.org/0000-0001-8392-5743"},"institutions":[{"id":"https://openalex.org/I4210107154","display_name":"The Capital Region Pharmacy","ror":"https://ror.org/01jh2g196","country_code":"DK","type":"healthcare","lineage":["https://openalex.org/I2802567020","https://openalex.org/I4210107154","https://openalex.org/I4210145385"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Fabrizio Cerreto","raw_affiliation_strings":["Metroselskabet og Hovedstadens Letbane, 2300 K\u00f8benhavn S, Denmark"],"raw_orcid":"https://orcid.org/0000-0001-8392-5743","affiliations":[{"raw_affiliation_string":"Metroselskabet og Hovedstadens Letbane, 2300 K\u00f8benhavn S, Denmark","institution_ids":["https://openalex.org/I4210107154"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126608532","display_name":"Bianca Pascariu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210154111","display_name":"Universit\u00e9 Gustave Eiffel","ror":"https://ror.org/03x42jk29","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210154111"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Bianca Pascariu","raw_affiliation_strings":["COSYS-ESTAS, Gustave Eiffel University, F-59650, Villeneuve dAscq, France"],"raw_orcid":"https://orcid.org/0009-0009-3911-0446","affiliations":[{"raw_affiliation_string":"COSYS-ESTAS, Gustave Eiffel University, F-59650, Villeneuve dAscq, France","institution_ids":["https://openalex.org/I4210154111"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126608295","display_name":"Paola Pellegrini","orcid":null},"institutions":[{"id":"https://openalex.org/I4210154111","display_name":"Universit\u00e9 Gustave Eiffel","ror":"https://ror.org/03x42jk29","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210154111"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Paola Pellegrini","raw_affiliation_strings":["COSYS-ESTAS, Gustave Eiffel University, F-59650, Villeneuve dAscq, France"],"raw_orcid":"https://orcid.org/0000-0002-6087-651X","affiliations":[{"raw_affiliation_string":"COSYS-ESTAS, Gustave Eiffel University, F-59650, Villeneuve dAscq, France","institution_ids":["https://openalex.org/I4210154111"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090562367","display_name":"Carlos Lima Azevedo","orcid":"https://orcid.org/0000-0003-3902-6569"},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Carlos Lima Azevedo","raw_affiliation_strings":["Department of Technology, Management, and Economics, Technical University of Denmark (DTU), 2800 Kongens Lyngby, Denmark"],"raw_orcid":"https://orcid.org/0000-0003-3902-6569","affiliations":[{"raw_affiliation_string":"Department of Technology, Management, and Economics, Technical University of Denmark (DTU), 2800 Kongens Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078981714","display_name":"Filipe Rodrigues","orcid":"https://orcid.org/0000-0001-6979-6498"},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Filipe Rodrigues","raw_affiliation_strings":["Department of Technology, Management, and Economics, Technical University of Denmark (DTU), 2800 Kongens Lyngby, Denmark"],"raw_orcid":"https://orcid.org/0000-0001-6979-6498","affiliations":[{"raw_affiliation_string":"Department of Technology, Management, and Economics, Technical University of Denmark (DTU), 2800 Kongens Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5011094724"],"corresponding_institution_ids":["https://openalex.org/I96673099"],"apc_list":{"value":3310,"currency":"USD","value_usd":3310},"apc_paid":{"value":3310,"currency":"USD","value_usd":3310},"fwci":9.0775,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.95827523,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"215","issue":null,"first_page":"111924","last_page":"111924"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.5892000198364258,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.5892000198364258,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.11749999970197678,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.052299998700618744,"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/robustness","display_name":"Robustness (evolution)","score":0.7677000164985657},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5733000040054321},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5659000277519226},{"id":"https://openalex.org/keywords/urban-rail-transit","display_name":"Urban rail transit","score":0.4575999975204468},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4564000070095062},{"id":"https://openalex.org/keywords/demand-patterns","display_name":"Demand patterns","score":0.38609999418258667},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.37950000166893005}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7677000164985657},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5936999917030334},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5733000040054321},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5659000277519226},{"id":"https://openalex.org/C2780434240","wikidata":"https://www.wikidata.org/wiki/Q3491904","display_name":"Urban rail transit","level":2,"score":0.4575999975204468},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4564000070095062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4253999888896942},{"id":"https://openalex.org/C32597650","wikidata":"https://www.wikidata.org/wiki/Q5255044","display_name":"Demand patterns","level":3,"score":0.38609999418258667},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.37950000166893005},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3763999938964844},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.32019999623298645},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2515000104904175},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.cie.2026.111924","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.cie.2026.111924","pdf_url":null,"source":{"id":"https://openalex.org/S196821226","display_name":"Computers & Industrial Engineering","issn_l":"0360-8352","issn":["0360-8352","1879-0550"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers &amp; Industrial Engineering","raw_type":"journal-article"},{"id":"pmh:oai:HAL:emse-05538560v1","is_oa":true,"landing_page_url":"https://hal-emse.ccsd.cnrs.fr/emse-05538560","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Computers & Industrial Engineering, 2026, 215, pp.111924. &#x27E8;10.1016/j.cie.2026.111924&#x27E9;","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1016/j.cie.2026.111924","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.cie.2026.111924","pdf_url":null,"source":{"id":"https://openalex.org/S196821226","display_name":"Computers & Industrial Engineering","issn_l":"0360-8352","issn":["0360-8352","1879-0550"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers &amp; Industrial Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.42674610018730164,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G4807924743","display_name":null,"funder_award_id":"N 875022","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"},{"id":"https://openalex.org/G7426779385","display_name":null,"funder_award_id":"875022","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320332999","display_name":"Horizon 2020 Framework Programme","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2901504064","https://openalex.org/W2949676527","https://openalex.org/W2973219073","https://openalex.org/W3109254449","https://openalex.org/W3119081436","https://openalex.org/W3119249947","https://openalex.org/W3119688269","https://openalex.org/W3199302499","https://openalex.org/W3210458411","https://openalex.org/W4316660991","https://openalex.org/W4316810825","https://openalex.org/W4391652455","https://openalex.org/W4396529138","https://openalex.org/W4398223791","https://openalex.org/W4401387486","https://openalex.org/W4406559612","https://openalex.org/W4408460923","https://openalex.org/W4411092923"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,77,117,149],"expansion":[2],"of":[3,81,116],"cities":[4],"over":[5],"time,":[6],"Urban":[7],"Rail":[8],"Transit":[9],"(URT)":[10],"networks":[11,51],"have":[12],"also":[13],"grown":[14],"significantly.":[15],"Accurate":[16],"demand":[17,39],"prediction":[18,40],"plays":[19],"a":[20,63,137],"crucial":[21],"role":[22],"in":[23,65,140],"supporting":[24],"planning,":[25],"scheduling,":[26],"fleet":[27],"management,":[28],"and":[29,72,94,130,154,164],"other":[30],"operational":[31,53,88,157],"decisions.":[32],"This":[33],"study":[34],"proposes":[35],"an":[36],"Origin-Destination":[37],"(OD)":[38],"model":[41,57,100,109],"called":[42],"Multi-Graph":[43],"Inductive":[44],"Representation":[45],"Learning":[46],"(mGraphSAGE)":[47],"for":[48,162],"large-scale":[49,163],"URT":[50,119,166],"under":[52,105,156],"uncertainties.":[54],"The":[55,108,145],"proposed":[56],"represents":[58],"each":[59],"OD":[60],"pair":[61],"as":[62,91,99],"node":[64],"multiple":[66],"graphs":[67],"that":[68,124],"capture":[69],"distinct":[70],"spatial":[71,78,152],"temporal":[73],"correlations,":[74],"thereby":[75],"enhancing":[76],"learning":[79,132],"capability":[80],"graph-based":[82,129],"methods":[83],"while":[84],"maintaining":[85],"scalability.":[86],"Moreover,":[87],"uncertainties":[89],"such":[90],"train":[92],"delays":[93],"cancellations":[95],"are":[96],"explicitly":[97],"incorporated":[98],"inputs":[101],"to":[102,136],"improve":[103],"robustness":[104,155],"real-world":[106],"disruptions.":[107],"is":[110],"validated":[111],"on":[112],"three":[113],"network":[114,143],"scales":[115],"Copenhagen":[118],"system.":[120],"Experimental":[121],"results":[122],"show":[123],"mGraphSAGE":[125],"outperforms":[126],"both":[127],"conventional":[128],"machine":[131],"baselines,":[133],"achieving":[134],"up":[135],"5%":[138],"reduction":[139],"RMSE":[141],"across":[142],"scales.":[144],"consistent":[146],"improvement":[147],"demonstrates":[148],"model\u2019s":[150],"enhanced":[151],"representation":[153],"uncertainties,":[158],"confirming":[159],"its":[160],"suitability":[161],"disrupted":[165],"environments.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-15T08:34:33.830935","created_date":"2026-02-24T00:00:00"}
