{"id":"https://openalex.org/W4402351825","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651254","title":"Heterogeneous Augmentation Based Spatio-Temporal Graph Convolutional Network for Traffic Forecasting","display_name":"Heterogeneous Augmentation Based Spatio-Temporal Graph Convolutional Network for Traffic Forecasting","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351825","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651254"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651254","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651254","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5084769547","display_name":"Hongyan Mao","orcid":"https://orcid.org/0000-0001-6749-1607"},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyan Mao","raw_affiliation_strings":["East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,ShangHai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,ShangHai,China","institution_ids":["https://openalex.org/I4210139618","https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077655412","display_name":"Zhenyu Sun","orcid":"https://orcid.org/0000-0001-5788-9339"},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"ZhenYu Sun","raw_affiliation_strings":["East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,ShangHai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,ShangHai,China","institution_ids":["https://openalex.org/I4210139618","https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108152776","display_name":"Ning Qin","orcid":"https://orcid.org/0009-0007-0981-6451"},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Qin","raw_affiliation_strings":["East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,ShangHai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,ShangHai,China","institution_ids":["https://openalex.org/I4210139618","https://openalex.org/I66867065"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4672,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63412684,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"28","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":0.9998999834060669,"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":0.9998999834060669,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9945999979972839,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.993399977684021,"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":0.7690371870994568},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5525517463684082},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42750734090805054},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.375559538602829},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3450638949871063},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23744279146194458}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7690371870994568},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5525517463684082},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42750734090805054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.375559538602829},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3450638949871063},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23744279146194458}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651254","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651254","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1522301498","https://openalex.org/W2132711183","https://openalex.org/W2528639018","https://openalex.org/W2560674852","https://openalex.org/W2751760169","https://openalex.org/W2756203131","https://openalex.org/W2788134583","https://openalex.org/W2903871660","https://openalex.org/W2962790412","https://openalex.org/W2963358464","https://openalex.org/W2965341826","https://openalex.org/W2996847713","https://openalex.org/W3038981236","https://openalex.org/W3080253043","https://openalex.org/W3095746859","https://openalex.org/W3103720336","https://openalex.org/W3121498550","https://openalex.org/W3126367810","https://openalex.org/W3138340468","https://openalex.org/W3154503084","https://openalex.org/W4230512065","https://openalex.org/W4306317428","https://openalex.org/W4312346007","https://openalex.org/W4382240004","https://openalex.org/W6628877408","https://openalex.org/W6631190155","https://openalex.org/W6637242042","https://openalex.org/W6746015598","https://openalex.org/W6780221082"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4293226380","https://openalex.org/W4396696052","https://openalex.org/W2382290278"],"abstract_inverted_index":{"Accurate":[0],"traffic":[1,25,56,96,119],"forecasting":[2,97],"is":[3,69],"of":[4,15,24,33,55,62,143,172],"great":[5],"importance":[6],"for":[7],"Intelligent":[8],"Transportation":[9],"Systems":[10],"(ITS)":[11],"in":[12,41,49],"the":[13,21,51,60,72,88,108,124,136,164,168],"context":[14],"smart":[16],"cities,":[17],"which":[18],"can":[19],"address":[20],"challenging":[22],"task":[23],"prediction":[26,84],"at":[27],"different":[28],"periods.":[29],"Although":[30],"current":[31],"research":[32],"STGNN":[34],"(Spatio-Temporal":[35],"Graph":[36,103],"Neural":[37],"Networks)":[38],"has":[39],"advanced":[40],"modeling":[42,50],"spatio-temporal":[43,53,77,125,140],"correlations,":[44,135],"they":[45],"still":[46],"face":[47],"challenges":[48],"dynamic":[52],"correlation":[54],"data":[57],"due":[58],"to":[59,71,82,122,128],"deficiency":[61],"static":[63],"pre-defined":[64],"graphs.":[65],"Additionally,":[66],"insufficient":[67],"attention":[68],"given":[70],"heterogeneous":[73],"graph":[74,116,152],"structure":[75],"within":[76],"dependencies,":[78],"despite":[79],"its":[80],"potential":[81],"enhance":[83],"accuracy.":[85],"Inspired":[86],"by":[87],"above":[89],"two":[90],"insights,":[91],"we":[92],"propose":[93],"a":[94,111,139,144,150],"novel":[95],"framework:":[98],"Heterogeneous":[99],"Augmentation":[100],"Based":[101],"Spatio-Temporal":[102],"Convolutional":[104],"Network":[105],"(HASTGCN).":[106],"Specifically,":[107],"model":[109],"employs":[110],"graph-augmentation":[112],"module":[113],"based":[114],"on":[115,159],"topology":[117],"and":[118,132,149,163,170],"flow":[120],"patterns":[121],"learn":[123],"heterogeneity.":[126],"Further,":[127],"simultaneously":[129],"capture":[130],"short-term":[131],"long-term":[133],"temporal":[134,146],"framework":[137],"incorporates":[138],"block":[141],"consisting":[142],"multi-scale":[145],"convolution":[147,153],"network":[148],"spectral":[151],"network.":[154],"Adequate":[155],"experiments":[156],"were":[157],"conducted":[158],"four":[160],"real-world":[161],"datasets,":[162],"experimental":[165],"results":[166],"demonstrate":[167],"effectiveness":[169],"superiority":[171],"our":[173],"method":[174],"over":[175],"other":[176],"baseline":[177],"methods.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}