{"id":"https://openalex.org/W4323566497","doi":"https://doi.org/10.3390/s23062897","title":"Dynamic Correlation Adjacency-Matrix-Based Graph Neural Networks for Traffic Flow Prediction","display_name":"Dynamic Correlation Adjacency-Matrix-Based Graph Neural Networks for Traffic Flow Prediction","publication_year":2023,"publication_date":"2023-03-07","ids":{"openalex":"https://openalex.org/W4323566497","doi":"https://doi.org/10.3390/s23062897","pmid":"https://pubmed.ncbi.nlm.nih.gov/36991611"},"language":"en","primary_location":{"id":"doi:10.3390/s23062897","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23062897","pdf_url":"https://www.mdpi.com/1424-8220/23/6/2897/pdf?version=1678192511","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/23/6/2897/pdf?version=1678192511","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100370030","display_name":"Junhua Gu","orcid":"https://orcid.org/0000-0001-8896-1735"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junhua Gu","raw_affiliation_strings":["School of Artificial Intelligence, Hebei University of Technology, Tianjin 300000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300000, China","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027573356","display_name":"Zhihao Jia","orcid":"https://orcid.org/0000-0003-1941-9269"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhihao Jia","raw_affiliation_strings":["School of Artificial Intelligence, Hebei University of Technology, Tianjin 300000, China"],"raw_orcid":"https://orcid.org/0000-0003-1941-9269","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300000, China","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051414197","display_name":"Taotao Cai","orcid":"https://orcid.org/0000-0002-3624-6120"},"institutions":[{"id":"https://openalex.org/I185523456","display_name":"University of Southern Queensland","ror":"https://ror.org/04sjbnx57","country_code":"AU","type":"education","lineage":["https://openalex.org/I185523456"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Taotao Cai","raw_affiliation_strings":["School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba 4350, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba 4350, Australia","institution_ids":["https://openalex.org/I185523456"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053270215","display_name":"Xiangyu Song","orcid":"https://orcid.org/0000-0002-5550-6354"},"institutions":[{"id":"https://openalex.org/I57093077","display_name":"Swinburne University of Technology","ror":"https://ror.org/031rekg67","country_code":"AU","type":"education","lineage":["https://openalex.org/I57093077"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xiangyu Song","raw_affiliation_strings":["School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Australia","institution_ids":["https://openalex.org/I57093077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086858608","display_name":"Adnan Mahmood","orcid":"https://orcid.org/0000-0003-3526-9037"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Adnan Mahmood","raw_affiliation_strings":["School of Computing, Macquarie University, Sydney 2109, Australia"],"raw_orcid":"https://orcid.org/0000-0003-3526-9037","affiliations":[{"raw_affiliation_string":"School of Computing, Macquarie University, Sydney 2109, Australia","institution_ids":["https://openalex.org/I99043593"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5027573356"],"corresponding_institution_ids":["https://openalex.org/I184843921"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2673},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2673},"fwci":3.0286,"has_fulltext":true,"cited_by_count":27,"citation_normalized_percentile":{"value":0.91160022,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"23","issue":"6","first_page":"2897","last_page":"2897"},"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":0.9937000274658203,"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":0.986299991607666,"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/adjacency-matrix","display_name":"Adjacency matrix","score":0.7388599514961243},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6792088150978088},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6636706590652466},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5864282250404358},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.5653022527694702},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5281187295913696},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4971759617328644},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4657723605632782},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.46512576937675476},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4192807972431183},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41625523567199707},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36539167165756226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3647720515727997},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2694321870803833},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.24357759952545166},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22239950299263},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19086068868637085}],"concepts":[{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.7388599514961243},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6792088150978088},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6636706590652466},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5864282250404358},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.5653022527694702},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5281187295913696},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4971759617328644},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4657723605632782},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.46512576937675476},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4192807972431183},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41625523567199707},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36539167165756226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3647720515727997},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2694321870803833},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.24357759952545166},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22239950299263},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19086068868637085},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/s23062897","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23062897","pdf_url":"https://www.mdpi.com/1424-8220/23/6/2897/pdf?version=1678192511","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:36991611","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36991611","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10055944","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10055944","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10055944/pdf/sensors-23-02897.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"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":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:6377f88991014f8eace2154f8ce937d5","is_oa":true,"landing_page_url":"https://doaj.org/article/6377f88991014f8eace2154f8ce937d5","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 23, Iss 6, p 2897 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/23/6/2897/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s23062897","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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":"Sensors; Volume 23; Issue 6; Pages: 2897","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s23062897","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23062897","pdf_url":"https://www.mdpi.com/1424-8220/23/6/2897/pdf?version=1678192511","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4323566497.pdf"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W2064675550","https://openalex.org/W2101491865","https://openalex.org/W2756203131","https://openalex.org/W2768008502","https://openalex.org/W2903871660","https://openalex.org/W2921532413","https://openalex.org/W2965341826","https://openalex.org/W2989567700","https://openalex.org/W2990330017","https://openalex.org/W2996847713","https://openalex.org/W2998436408","https://openalex.org/W3001437801","https://openalex.org/W3006610470","https://openalex.org/W3015918353","https://openalex.org/W3021626758","https://openalex.org/W3022478765","https://openalex.org/W3026164661","https://openalex.org/W3031632615","https://openalex.org/W3033337169","https://openalex.org/W3034005731","https://openalex.org/W3038981236","https://openalex.org/W3045202147","https://openalex.org/W3080253043","https://openalex.org/W3080284469","https://openalex.org/W3080344546","https://openalex.org/W3081002782","https://openalex.org/W3091641260","https://openalex.org/W3094231942","https://openalex.org/W3096604274","https://openalex.org/W3098944285","https://openalex.org/W3102272367","https://openalex.org/W3103720336","https://openalex.org/W3166508292","https://openalex.org/W3170140111","https://openalex.org/W3174022889","https://openalex.org/W4200634602","https://openalex.org/W4225284516"],"related_works":["https://openalex.org/W4213150077","https://openalex.org/W2369410163","https://openalex.org/W2982430984","https://openalex.org/W2078477160","https://openalex.org/W2059018062","https://openalex.org/W2604585036","https://openalex.org/W1991172810","https://openalex.org/W125803343","https://openalex.org/W1989103179","https://openalex.org/W2117632582"],"abstract_inverted_index":{"Modeling":[0],"complex":[1],"spatial":[2,119],"and":[3,153],"temporal":[4,123,129],"dependencies":[5],"in":[6,24,94],"multivariate":[7,26],"time":[8,27],"series":[9],"data":[10,105],"is":[11,115,125],"crucial":[12],"for":[13,117,127],"traffic":[14],"forecasting.":[15],"Graph":[16,90],"convolutional":[17],"networks":[18],"have":[19],"proved":[20],"to":[21,39,53,68,77,136],"be":[22],"effective":[23],"predicting":[25],"series.":[28],"Although":[29],"a":[30,84,107],"predefined":[31],"graph":[32,70,113],"structure":[33,71],"can":[34,99],"help":[35],"the":[36,46,50,69,138],"model":[37,51,86,98],"converge":[38,63],"good":[40],"results":[41],"quickly,":[42],"it":[43],"also":[44],"limits":[45],"further":[47],"improvement":[48],"of":[49,72,140],"due":[52,67],"its":[54],"stationary":[55],"state.":[56],"In":[57],"addition,":[58],"current":[59],"methods":[60,149],"may":[61],"not":[62],"on":[64],"some":[65],"datasets":[66,74],"these":[73],"being":[75],"difficult":[76],"learn.":[78],"Motivated":[79],"by":[80],"this,":[81],"we":[82,132],"propose":[83],"novel":[85],"named":[87],"Dynamic":[88],"Correlation":[89],"Convolutional":[91],"Network":[92],"(DCGCN)":[93],"this":[95],"paper.":[96],"The":[97],"construct":[100],"adjacency":[101],"matrices":[102],"from":[103],"input":[104],"using":[106,150],"correlation":[108,112],"coefficient;":[109],"thus,":[110],"dynamic":[111],"convolution":[114,124],"used":[116,126],"capturing":[118],"dependencies.":[120,130],"Meanwhile,":[121],"gated":[122],"modeling":[128],"Finally,":[131],"performed":[133],"extensive":[134],"experiments":[135],"evaluate":[137],"performance":[139],"our":[141],"proposed":[142],"method":[143],"against":[144],"ten":[145],"existing":[146],"well-recognized":[147],"baseline":[148],"two":[151],"original":[152],"four":[154],"public":[155],"datasets.":[156]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":4}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}