{"id":"https://openalex.org/W4313116560","doi":"https://doi.org/10.1109/icpr56361.2022.9956477","title":"Multi-Hierarchical Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting","display_name":"Multi-Hierarchical Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4313116560","doi":"https://doi.org/10.1109/icpr56361.2022.9956477"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956477","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956477","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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/A5100699764","display_name":"Zilong Li","orcid":"https://orcid.org/0000-0002-8380-2396"},"institutions":[{"id":"https://openalex.org/I1300757298","display_name":"Heilongjiang University of Science and Technology","ror":"https://ror.org/030xwyx96","country_code":"CN","type":"education","lineage":["https://openalex.org/I1300757298"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zilong Li","raw_affiliation_strings":["Heilongjiang University,Department of Computer Science and Technology,Harbin,China,150080"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heilongjiang University,Department of Computer Science and Technology,Harbin,China,150080","institution_ids":["https://openalex.org/I1300757298"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101523305","display_name":"Qianqian Ren","orcid":"https://orcid.org/0000-0003-1171-7018"},"institutions":[{"id":"https://openalex.org/I1300757298","display_name":"Heilongjiang University of Science and Technology","ror":"https://ror.org/030xwyx96","country_code":"CN","type":"education","lineage":["https://openalex.org/I1300757298"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qianqian Ren","raw_affiliation_strings":["Heilongjiang University,Department of Computer Science and Technology,Harbin,China,150080"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heilongjiang University,Department of Computer Science and Technology,Harbin,China,150080","institution_ids":["https://openalex.org/I1300757298"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100336427","display_name":"Long Chen","orcid":"https://orcid.org/0000-0003-0184-5446"},"institutions":[{"id":"https://openalex.org/I1300757298","display_name":"Heilongjiang University of Science and Technology","ror":"https://ror.org/030xwyx96","country_code":"CN","type":"education","lineage":["https://openalex.org/I1300757298"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Long Chen","raw_affiliation_strings":["Heilongjiang University,Department of Computer Science and Technology,Harbin,China,150080"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heilongjiang University,Department of Computer Science and Technology,Harbin,China,150080","institution_ids":["https://openalex.org/I1300757298"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058779185","display_name":"Xiaohong Sui","orcid":"https://orcid.org/0000-0002-2376-448X"},"institutions":[{"id":"https://openalex.org/I41317344","display_name":"Jiangxi Science and Technology Normal University","ror":"https://ror.org/04r1zkp10","country_code":"CN","type":"education","lineage":["https://openalex.org/I41317344"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohong Sui","raw_affiliation_strings":["Guangxi Science & Technology Normal University,Laibin,China,546199"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangxi Science & Technology Normal University,Laibin,China,546199","institution_ids":["https://openalex.org/I41317344"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100665542","display_name":"Jinbao Li","orcid":"https://orcid.org/0009-0000-7031-4636"},"institutions":[{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinbao Li","raw_affiliation_strings":["Qilu University of Technology,Shandong Artificial Intelligence Institute,Jinan,China,250013"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qilu University of Technology,Shandong Artificial Intelligence Institute,Jinan,China,250013","institution_ids":["https://openalex.org/I152269853"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100699764"],"corresponding_institution_ids":["https://openalex.org/I1300757298"],"apc_list":null,"apc_paid":null,"fwci":0.6696,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.6601626,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"22","issue":null,"first_page":"4913","last_page":"4919"},"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9939000010490417,"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/T10524","display_name":"Traffic control and management","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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":0.7547746896743774},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.6896414756774902},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.601845383644104},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5353901982307434},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5341370105743408},{"id":"https://openalex.org/keywords/spatial-correlation","display_name":"Spatial correlation","score":0.4969632923603058},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4199255406856537},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3847258985042572},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.322613924741745},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23077449202537537},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10736361145973206}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7547746896743774},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6896414756774902},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.601845383644104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5353901982307434},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5341370105743408},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.4969632923603058},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4199255406856537},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3847258985042572},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.322613924741745},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23077449202537537},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10736361145973206},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956477","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956477","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7099999785423279,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320310113","display_name":"Heilongjiang University","ror":"https://ror.org/04zyhq975"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W43001522","https://openalex.org/W1522301498","https://openalex.org/W1665214252","https://openalex.org/W1973943669","https://openalex.org/W2040297119","https://openalex.org/W2075407851","https://openalex.org/W2083238230","https://openalex.org/W2101491865","https://openalex.org/W2108196201","https://openalex.org/W2125817951","https://openalex.org/W2163605009","https://openalex.org/W2755146079","https://openalex.org/W2782791108","https://openalex.org/W2901504064","https://openalex.org/W2903871660","https://openalex.org/W2940640769","https://openalex.org/W2962756421","https://openalex.org/W2962790412","https://openalex.org/W2963840672","https://openalex.org/W2964321699","https://openalex.org/W2965341826","https://openalex.org/W2968911474","https://openalex.org/W2996451395","https://openalex.org/W2996847713","https://openalex.org/W2997848713","https://openalex.org/W3027664001","https://openalex.org/W3102259344","https://openalex.org/W3103720336","https://openalex.org/W6637242042","https://openalex.org/W6684191040","https://openalex.org/W6720006811","https://openalex.org/W6747337883"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W147410782","https://openalex.org/W4287804464","https://openalex.org/W3022252430","https://openalex.org/W3103989898","https://openalex.org/W2152352598","https://openalex.org/W3015684221","https://openalex.org/W4287816705"],"abstract_inverted_index":{"Traffic":[0],"forecasting":[1,58],"is":[2,18,88,95],"essential":[3],"for":[4,135],"transportation":[5],"services":[6],"such":[7],"as":[8],"traffic":[9,16,25,39,56,138,144],"control":[10],"and":[11,34,79,92,103,114,128],"route":[12],"planning.":[13],"However,":[14],"accurate":[15],"prediction":[17],"challenging":[19],"due":[20],"to":[21,54,75,97,117],"complex":[22],"characteristics":[23,122],"of":[24,101,121],"data.":[26],"Existing":[27],"solutions":[28],"may":[29],"not":[30],"adequately":[31],"capture":[32],"dynamic":[33,77],"nonlinear":[35,80],"spatial-temporal":[36,70],"correlations":[37],"in":[38,73],"network.":[40],"In":[41],"this":[42],"paper,":[43],"we":[44],"propose":[45],"a":[46,69,84],"novel":[47],"Multi-Hierarchical":[48],"Spatial-Temporal":[49],"Graph":[50],"Convolutional":[51],"Networks":[52],"(MH-GCN)":[53],"solve":[55],"flow":[57],"problem.":[59],"It":[60],"adopts":[61],"an":[62],"attention-based":[63],"encoder-decoder":[64],"structure.":[65],"Firstly,":[66],"MH-GCN":[67,150],"uses":[68],"attention":[71,86],"mechanism":[72],"encoder":[74,91],"model":[76,98],"spatial":[78],"temporal":[81],"correlations.":[82],"Then,":[83],"transformer":[85],"layer":[87],"positioned":[89],"between":[90],"decoder,":[93],"which":[94],"used":[96,134],"the":[99,107,124,130,148,154],"correlation":[100],"historical":[102],"future":[104,137],"time.":[105],"Finally,":[106],"decoder":[108],"utilizes":[109],"Convolution":[110],"Group,":[111,113],"Pooling":[112],"Dilation":[115],"Group":[116],"extract":[118],"different":[119],"hierarchical":[120],"from":[123],"already":[125],"modeled":[126],"features,":[127],"then":[129],"fused":[131],"results":[132],"are":[133],"predicting":[136],"conditions.":[139],"Experiments":[140],"on":[141],"two":[142],"real":[143],"datasets":[145],"demonstrate":[146],"that":[147],"proposed":[149],"obtains":[151],"improvements":[152],"over":[153],"state-of-the-art":[155],"baselines.":[156]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-14T08:36:36.166977","created_date":"2025-10-10T00:00:00"}