{"id":"https://openalex.org/W2895799837","doi":"https://doi.org/10.1145/3269206.3271757","title":"Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization","display_name":"Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization","publication_year":2018,"publication_date":"2018-10-17","ids":{"openalex":"https://openalex.org/W2895799837","doi":"https://doi.org/10.1145/3269206.3271757","mag":"2895799837"},"language":"en","primary_location":{"id":"doi:10.1145/3269206.3271757","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3269206.3271757","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","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/A5040047825","display_name":"Yongshun Gong","orcid":"https://orcid.org/0000-0003-3948-4471"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Yongshun Gong","raw_affiliation_strings":["University of Technology, Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"University of Technology, Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100351559","display_name":"Zhibin Li","orcid":"https://orcid.org/0000-0002-4226-267X"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Zhibin Li","raw_affiliation_strings":["University of Technology, Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"University of Technology, Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100409994","display_name":"Jian Zhang","orcid":"https://orcid.org/0000-0002-7240-3541"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jian Zhang","raw_affiliation_strings":["University of Technology, Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"University of Technology, Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100431652","display_name":"Wei Liu","orcid":"https://orcid.org/0000-0001-6565-5815"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wei Liu","raw_affiliation_strings":["University of Technology, Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"University of Technology, Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100681023","display_name":"Yu Zheng","orcid":"https://orcid.org/0000-0002-5224-4344"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zheng","raw_affiliation_strings":["JD Finance, Beijing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD Finance, Beijing, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050390965","display_name":"Christina Kirsch","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Christina Kirsch","raw_affiliation_strings":["Sydney Trains-Operational Technology, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"Sydney Trains-Operational Technology, Sydney, Australia","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5040047825"],"corresponding_institution_ids":["https://openalex.org/I114017466"],"apc_list":null,"apc_paid":null,"fwci":5.8301,"has_fulltext":false,"cited_by_count":59,"citation_normalized_percentile":{"value":0.95990899,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1243","last_page":"1252"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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":1.0,"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":0.9998999834060669,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9994000196456909,"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":0.7927148938179016},{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.7349890470504761},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.6760631203651428},{"id":"https://openalex.org/keywords/flow-network","display_name":"Flow network","score":0.500680685043335},{"id":"https://openalex.org/keywords/crowds","display_name":"Crowds","score":0.4957038164138794},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4272492229938507},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4108216166496277},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36254021525382996},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33677589893341064},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.330288290977478},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.30422940850257874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7927148938179016},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.7349890470504761},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.6760631203651428},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.500680685043335},{"id":"https://openalex.org/C2777852691","wikidata":"https://www.wikidata.org/wiki/Q13430821","display_name":"Crowds","level":2,"score":0.4957038164138794},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4272492229938507},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4108216166496277},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36254021525382996},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33677589893341064},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.330288290977478},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.30422940850257874},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3269206.3271757","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3269206.3271757","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:opus.lib.uts.edu.au:10453/128531","is_oa":false,"landing_page_url":"http://hdl.handle.net/10453/128531","pdf_url":null,"source":{"id":"https://openalex.org/S4306401357","display_name":"UTS ePRESS (University of Technology Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114017466","host_organization_name":"University of Technology Sydney","host_organization_lineage":["https://openalex.org/I114017466"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7799999713897705,"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":32,"referenced_works":["https://openalex.org/W18306861","https://openalex.org/W146815106","https://openalex.org/W1746819321","https://openalex.org/W1968745326","https://openalex.org/W1973943669","https://openalex.org/W1979697018","https://openalex.org/W2002841906","https://openalex.org/W2006057946","https://openalex.org/W2007043321","https://openalex.org/W2009627675","https://openalex.org/W2033714695","https://openalex.org/W2044985623","https://openalex.org/W2054007841","https://openalex.org/W2085987121","https://openalex.org/W2088353498","https://openalex.org/W2112738128","https://openalex.org/W2115632271","https://openalex.org/W2129679514","https://openalex.org/W2131185675","https://openalex.org/W2162984166","https://openalex.org/W2169707717","https://openalex.org/W2276747974","https://openalex.org/W2469618837","https://openalex.org/W2528510132","https://openalex.org/W2604537950","https://openalex.org/W2737283524","https://openalex.org/W2913535645","https://openalex.org/W2963124587","https://openalex.org/W2963214893","https://openalex.org/W3143596294","https://openalex.org/W4211049957","https://openalex.org/W4244393449"],"related_works":["https://openalex.org/W2368138740","https://openalex.org/W2997512100","https://openalex.org/W2331043530","https://openalex.org/W1606975172","https://openalex.org/W2961085424","https://openalex.org/W2393933887","https://openalex.org/W2981647755","https://openalex.org/W4306674287","https://openalex.org/W4244121124","https://openalex.org/W2008613566"],"abstract_inverted_index":{"Crowd":[0,40],"Flow":[1,41],"Prediction":[2],"(CFP)":[3],"is":[4],"one":[5,166],"major":[6,32],"challenge":[7],"in":[8,57,110,130],"the":[9,14,31,47,99,125,135,150,154,169,186],"intelligent":[10],"transportation":[11],"systems":[12],"of":[13,127,144,153,188],"Sydney":[15],"Trains":[16],"Network.":[17],"However,":[18],"most":[19],"advanced":[20],"CFP":[21],"methods":[22],"only":[23],"focus":[24],"on":[25,177],"entrance":[26],"and":[27,70,121,133],"exit":[28],"flows":[29],"at":[30],"stations":[33,156],"or":[34],"a":[35,61,142,178],"few":[36],"subway":[37],"lines,":[38],"neglecting":[39],"Distribution":[42],"(CFD)":[43],"forecasting":[44],"problem":[45],"across":[46],"entire":[48],"city":[49],"network.":[50],"CFD":[51,145],"prediction":[52],"plays":[53],"an":[54,87],"irreplaceable":[55],"role":[56],"metro":[58],"management":[59],"as":[60],"tool":[62],"that":[63,91],"can":[64],"help":[65],"authorities":[66],"plan":[67],"route":[68],"schedules":[69],"avoid":[71],"congestion.":[72],"In":[73],"this":[74],"paper,":[75],"we":[76],"propose":[77],"three":[78],"online":[79],"non-negative":[80],"matrix":[81],"factorization":[82],"(ONMF)":[83],"models.":[84,191],"ONMF-AO":[85,120],"incorporates":[86],"Average":[88],"Optimization":[89],"strategy":[90],"adapts":[92],"to":[93,103,123,138,161,168],"stable":[94],"passenger":[95],"flows.":[96],"ONMF-MR":[97,122],"captures":[98],"Most":[100],"Recent":[101],"trends":[102],"achieve":[104],"better":[105],"performance":[106],"when":[107],"sudden":[108],"changes":[109],"crowd":[111],"flow":[112],"occur.":[113],"The":[114],"Hybrid":[115],"model,":[116],"ONMF-H,":[117],"integrates":[118],"both":[119,147],"exploit":[124],"strengths":[126],"each":[128],"model":[129],"different":[131],"scenarios":[132],"enhance":[134],"models'":[136],"applicability":[137],"real-world":[139,180],"situations.":[140],"Given":[141],"series":[143],"snapshots,":[146],"models":[148],"learn":[149],"latent":[151],"attributes":[152],"train":[155],"and,":[157],"therefore,":[158],"are":[159],"able":[160],"capture":[162],"transition":[163],"patterns":[164],"from":[165],"timestamp":[167],"next":[170],"by":[171],"combining":[172],"historic":[173],"guidance.":[174],"Intensive":[175],"experiments":[176],"large-scale,":[179],"dataset":[181],"containing":[182],"transactional":[183],"data":[184],"demonstrate":[185],"superiority":[187],"our":[189],"ONMF":[190]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":5}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}