{"id":"https://openalex.org/W4391768618","doi":"https://doi.org/10.1109/itsc57777.2023.10422273","title":"Spatial-Temporal Graph Discriminant AutoEncoder for Traffic Congestion Forecasting","display_name":"Spatial-Temporal Graph Discriminant AutoEncoder for Traffic Congestion Forecasting","publication_year":2023,"publication_date":"2023-09-24","ids":{"openalex":"https://openalex.org/W4391768618","doi":"https://doi.org/10.1109/itsc57777.2023.10422273"},"language":"en","primary_location":{"id":"doi:10.1109/itsc57777.2023.10422273","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/itsc57777.2023.10422273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)","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/A5081146701","display_name":"Jiaheng Peng","orcid":"https://orcid.org/0000-0002-4182-8838"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiaheng Peng","raw_affiliation_strings":["Zhejiang University,State Key Lab of Industrial Control Technology,Hangzhou,China","State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University,State Key Lab of Industrial Control Technology,Hangzhou,China","institution_ids":["https://openalex.org/I76130692","https://openalex.org/I4391767838"]},{"raw_affiliation_string":"State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I4391767838"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015716546","display_name":"Tong Guan","orcid":"https://orcid.org/0000-0001-7653-3741"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tong Guan","raw_affiliation_strings":["Zhejiang University,State Key Lab of Industrial Control Technology,Hangzhou,China","State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University,State Key Lab of Industrial Control Technology,Hangzhou,China","institution_ids":["https://openalex.org/I76130692","https://openalex.org/I4391767838"]},{"raw_affiliation_string":"State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I4391767838"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101519378","display_name":"Jun Liang","orcid":"https://orcid.org/0000-0003-1115-0824"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Liang","raw_affiliation_strings":["Zhejiang University,State Key Lab of Industrial Control Technology,Hangzhou,China","State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University,State Key Lab of Industrial Control Technology,Hangzhou,China","institution_ids":["https://openalex.org/I76130692","https://openalex.org/I4391767838"]},{"raw_affiliation_string":"State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I4391767838"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5081146701"],"corresponding_institution_ids":["https://openalex.org/I4391767838","https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22923788,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"23","last_page":"28"},"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":0.9979000091552734,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9958000183105469,"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/autoencoder","display_name":"Autoencoder","score":0.9126795530319214},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7270244359970093},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6506205201148987},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5665846467018127},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5624480247497559},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.5352158546447754},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5035545229911804},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4993288516998291},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47506874799728394},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4291608929634094},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3982560634613037},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2963162660598755},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.152342289686203},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1302790343761444},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.11915174126625061}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9126795530319214},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7270244359970093},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6506205201148987},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5665846467018127},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5624480247497559},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.5352158546447754},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5035545229911804},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4993288516998291},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47506874799728394},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4291608929634094},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3982560634613037},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2963162660598755},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.152342289686203},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1302790343761444},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.11915174126625061}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc57777.2023.10422273","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/itsc57777.2023.10422273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1546200464","https://openalex.org/W1554540371","https://openalex.org/W2025768430","https://openalex.org/W2036785686","https://openalex.org/W2103305545","https://openalex.org/W2157331557","https://openalex.org/W2218318129","https://openalex.org/W2460563573","https://openalex.org/W2624190409","https://openalex.org/W2808017405","https://openalex.org/W2890474333","https://openalex.org/W2894115892","https://openalex.org/W2900529838","https://openalex.org/W2903871660","https://openalex.org/W2919054368","https://openalex.org/W2945177784","https://openalex.org/W2963358464","https://openalex.org/W2972534151","https://openalex.org/W2997574889","https://openalex.org/W3033940652","https://openalex.org/W3107996479","https://openalex.org/W3126787811","https://openalex.org/W3143258789","https://openalex.org/W3177858041","https://openalex.org/W3209643259","https://openalex.org/W6602989467","https://openalex.org/W6675525543","https://openalex.org/W6681096077","https://openalex.org/W6746015598","https://openalex.org/W6780221082","https://openalex.org/W6786345023"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4386815338","https://openalex.org/W2145836866","https://openalex.org/W2803255133"],"abstract_inverted_index":{"Traffic":[0],"congestion":[1,24,59,102],"is":[2,25],"a":[3,48,82],"growing":[4],"issue":[5],"in":[6,122,139],"modern":[7],"cities,":[8],"with":[9],"significant":[10],"negative":[11],"impacts":[12],"on":[13,107,125],"the":[14,16,51,88,126],"environment,":[15],"economy,":[17],"and":[18,31,66,73,93,147,150],"people's":[19],"daily":[20],"lives.":[21],"Accurately":[22],"predicting":[23],"crucial":[26],"for":[27,57,101,135],"effective":[28],"road":[29,152],"control":[30],"route":[32],"planning,":[33],"making":[34],"it":[35],"an":[36,118],"essential":[37],"component":[38],"of":[39,120],"intelligent":[40],"transportation":[41],"systems.":[42],"In":[43],"this":[44],"paper,":[45],"we":[46],"propose":[47],"novel":[49],"algorithm,":[50],"Spatial-Temporal":[52],"Graph":[53],"Discriminant":[54],"Autoencoder":[55],"(STGDAE),":[56],"improving":[58,136],"prediction.":[60,103],"STGDAE":[61,113],"combines":[62],"graph":[63],"convolution":[64],"layers":[65],"recurrent":[67],"neural":[68],"networks":[69],"to":[70,86,96],"extract":[71],"spatial":[72],"temporal":[74],"features":[75],"from":[76],"traffic":[77,137],"data":[78],"efficiently.":[79],"We":[80],"introduce":[81],"distance":[83],"loss":[84],"term":[85],"improve":[87],"autoencoder's":[89],"feature":[90],"extraction":[91],"effectiveness":[92],"utilize":[94],"labels":[95],"retain":[97],"more":[98],"useful":[99],"information":[100],"Our":[104],"extensive":[105],"experiments":[106],"two":[108],"real-world":[109,140],"datasets":[110],"demonstrate":[111],"that":[112],"outperforms":[114],"state-of-the-art":[115],"methods,":[116],"achieving":[117],"improvement":[119],"0.1":[121],"F1":[123],"score":[124],"PeMSD8":[127],"dataset.":[128],"The":[129],"proposed":[130],"algorithm":[131],"has":[132],"promising":[133],"potential":[134],"management":[138],"scenarios,":[141],"such":[142],"as":[143],"reducing":[144],"travel":[145],"times":[146],"fuel":[148],"consumption":[149],"enhancing":[151],"safety.":[153]},"counts_by_year":[],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}