{"id":"https://openalex.org/W4410127621","doi":"https://doi.org/10.1155/ijdm/7220522","title":"2D Spatiotemporal Hypergraph Convolution Network for Dynamic OD Traffic Flow Prediction","display_name":"2D Spatiotemporal Hypergraph Convolution Network for Dynamic OD Traffic Flow Prediction","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4410127621","doi":"https://doi.org/10.1155/ijdm/7220522"},"language":"en","primary_location":{"id":"doi:10.1155/ijdm/7220522","is_oa":true,"landing_page_url":"https://doi.org/10.1155/ijdm/7220522","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/ijdm/7220522","source":{"id":"https://openalex.org/S175833906","display_name":"International Journal of Digital Multimedia Broadcasting","issn_l":"1687-7578","issn":["1687-7578","1687-7586"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Digital Multimedia Broadcasting","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/ijdm/7220522","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Cheng Fang","orcid":"https://orcid.org/0009-0009-0359-2498"},"institutions":[{"id":"https://openalex.org/I161222507","display_name":"Open University of China","ror":"https://ror.org/02maxav80","country_code":"CN","type":"education","lineage":["https://openalex.org/I161222507"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Fang","raw_affiliation_strings":["Open Education College ,  Changsha Open University ,  Changsha ,  Hunan Province ,  China"],"raw_orcid":"https://orcid.org/0009-0009-0359-2498","affiliations":[{"raw_affiliation_string":"Open Education College ,  Changsha Open University ,  Changsha ,  Hunan Province ,  China","institution_ids":["https://openalex.org/I161222507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042784382","display_name":"Li Wang","orcid":"https://orcid.org/0000-0002-2464-5765"},"institutions":[{"id":"https://openalex.org/I161222507","display_name":"Open University of China","ror":"https://ror.org/02maxav80","country_code":"CN","type":"education","lineage":["https://openalex.org/I161222507"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Li Wang","raw_affiliation_strings":["School of Computer Science ,  The Open University of China ,  Beijing ,  China ,  gist.ac.kr"],"raw_orcid":"https://orcid.org/0000-0002-2464-5765","affiliations":[{"raw_affiliation_string":"School of Computer Science ,  The Open University of China ,  Beijing ,  China ,  gist.ac.kr","institution_ids":["https://openalex.org/I161222507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5042784382"],"corresponding_institution_ids":["https://openalex.org/I161222507"],"apc_list":{"value":900,"currency":"USD","value_usd":900},"apc_paid":{"value":900,"currency":"USD","value_usd":900},"fwci":0.8617,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.71920293,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"2025","issue":"1","first_page":null,"last_page":null},"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/T10524","display_name":"Traffic control and management","score":0.9966999888420105,"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"}},{"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8242061138153076},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6578055024147034},{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.6406551599502563},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.47699645161628723},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.458008736371994},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3060533106327057},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1804770529270172},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11386704444885254},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.09222522377967834},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.0853661298751831},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.0809163749217987}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8242061138153076},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6578055024147034},{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.6406551599502563},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.47699645161628723},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.458008736371994},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3060533106327057},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1804770529270172},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11386704444885254},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.09222522377967834},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0853661298751831},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0809163749217987}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1155/ijdm/7220522","is_oa":true,"landing_page_url":"https://doi.org/10.1155/ijdm/7220522","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/ijdm/7220522","source":{"id":"https://openalex.org/S175833906","display_name":"International Journal of Digital Multimedia Broadcasting","issn_l":"1687-7578","issn":["1687-7578","1687-7586"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Digital Multimedia Broadcasting","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:06f7353f1048410fb1a6b79cfd48f3bf","is_oa":true,"landing_page_url":"https://doaj.org/article/06f7353f1048410fb1a6b79cfd48f3bf","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":"International Journal of Digital Multimedia Broadcasting, Vol 2025 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/ijdm/7220522","is_oa":true,"landing_page_url":"https://doi.org/10.1155/ijdm/7220522","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/ijdm/7220522","source":{"id":"https://openalex.org/S175833906","display_name":"International Journal of Digital Multimedia Broadcasting","issn_l":"1687-7578","issn":["1687-7578","1687-7586"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Digital Multimedia Broadcasting","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410127621.pdf","grobid_xml":"https://content.openalex.org/works/W4410127621.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1986235898","https://openalex.org/W2023703387","https://openalex.org/W2075004209","https://openalex.org/W2105381119","https://openalex.org/W2152233025","https://openalex.org/W2170057991","https://openalex.org/W2528639018","https://openalex.org/W2604847698","https://openalex.org/W2756203131","https://openalex.org/W2768372159","https://openalex.org/W2808162662","https://openalex.org/W2946160394","https://openalex.org/W2949732208","https://openalex.org/W2965341826","https://openalex.org/W3029978369","https://openalex.org/W3031609778","https://openalex.org/W3103720336","https://openalex.org/W3146824751","https://openalex.org/W3201110633","https://openalex.org/W4210267805","https://openalex.org/W4211213044","https://openalex.org/W4214498971","https://openalex.org/W4214598038","https://openalex.org/W4240805545","https://openalex.org/W4244364296","https://openalex.org/W4282926996","https://openalex.org/W4285275791","https://openalex.org/W4296743024","https://openalex.org/W4367050941","https://openalex.org/W4376863142","https://openalex.org/W4387820018","https://openalex.org/W4390664481"],"related_works":["https://openalex.org/W4376608589","https://openalex.org/W1537073411","https://openalex.org/W1948107826","https://openalex.org/W3138003926","https://openalex.org/W1630514295","https://openalex.org/W4300037846","https://openalex.org/W4288275998","https://openalex.org/W2963081352","https://openalex.org/W4376608938","https://openalex.org/W2180469977"],"abstract_inverted_index":{"Predicting":[0],"origin\u2010destination":[1],"(OD)":[2],"flow":[3,82,109],"presents":[4],"a":[5,53,74,89,96,155],"significant":[6],"challenge":[7],"in":[8,43,107,118],"intelligent":[9],"transportation":[10],"due":[11],"to":[12,38,103,129,165],"the":[13,40,56,127,142,160,163,168],"intricate":[14,169],"dynamic":[15,120],"correlations":[16,106],"between":[17,83],"starting":[18],"points":[19],"and":[20],"destinations.":[21],"Although":[22],"existing":[23],"OD":[24,67,84,108,150,172],"prediction":[25],"methodologies":[26],"leveraging":[27],"graph":[28],"neural":[29,92],"networks":[30],"have":[31],"demonstrated":[32],"commendable":[33],"performance,":[34,158],"they":[35],"often":[36],"struggle":[37],"address":[39,47],"complexities":[41],"inherent":[42],"two\u2010sided":[44],"correlations.":[45],"To":[46],"this":[48,50],"gap,":[49],"paper":[51],"introduces":[52],"novel":[54],"approach,":[55],"2D":[57,97],"spatiotemporal":[58],"hypergraph":[59,98],"convolution":[60,91],"network":[61,93,100],"(2D\u2010HGCN),":[62],"designed":[63],"specifically":[64],"for":[65,148],"forecasting":[66],"traffic":[68,81,173],"flow.":[69,174],"Our":[70,152],"proposed":[71],"model":[72,128,147],"employs":[73],"two\u2010stage":[75],"architecture.":[76],"Initially,":[77],"temporal":[78],"characteristics":[79],"of":[80,114,144,162,171],"pairs":[85],"are":[86],"captured":[87],"using":[88],"1D":[90],"(1D\u2010CNNs).":[94],"Subsequently,":[95],"convolutional":[99],"is":[101],"introduced":[102],"uncover":[104],"spatial":[105,133],"patterns.":[110],"The":[111],"unique":[112],"aspect":[113],"our":[115,145],"2D\u2010HGCN":[116,164],"lies":[117],"its":[119],"hypergraph,":[121],"which":[122],"evolves":[123],"over":[124],"time,":[125],"enabling":[126],"adaptively":[130],"learn":[131],"changing":[132],"dependencies.":[134],"Experimental":[135],"evaluation":[136],"conducted":[137],"on":[138],"real\u2010world":[139],"datasets":[140],"highlights":[141],"efficiency":[143],"suggested":[146],"predicting":[149],"flows.":[151],"results":[153],"demonstrate":[154],"promising":[156],"predictive":[157],"showcasing":[159],"ability":[161],"effectively":[166],"capture":[167],"dynamics":[170]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}