{"id":"https://openalex.org/W7138637845","doi":"https://doi.org/10.1609/aaai.v40i18.38599","title":"HyperD: Hybrid Periodicity Decoupling Framework for Traffic Forecasting","display_name":"HyperD: Hybrid Periodicity Decoupling Framework for Traffic Forecasting","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138637845","doi":"https://doi.org/10.1609/aaai.v40i18.38599"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i18.38599","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i18.38599","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i18.38599","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102116643","display_name":"Minlan Shao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Minlan Shao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129751628","display_name":"Zijian Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zijian Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129697101","display_name":"Yili Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yili Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129693601","display_name":"Yiwei Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yiwei Dai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100949643","display_name":"Xu Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu Shen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129730964","display_name":"Xin Eric Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":100.8045,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.99977619,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"40","issue":"18","first_page":"15689","last_page":"15697"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.984000027179718,"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":0.984000027179718,"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":0.003100000089034438,"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/T10524","display_name":"Traffic control and management","score":0.002099999925121665,"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/residual","display_name":"Residual","score":0.8474000096321106},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.704800009727478},{"id":"https://openalex.org/keywords/decoupling","display_name":"Decoupling (probability)","score":0.5440000295639038},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5056999921798706},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.4909999966621399},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.482699990272522},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.39879998564720154}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8474000096321106},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.704800009727478},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6826000213623047},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.5440000295639038},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5056999921798706},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.482699990272522},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40959998965263367},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3098999857902527},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.30410000681877136},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C204673680","wikidata":"https://www.wikidata.org/wiki/Q1628107","display_name":"Airfield traffic pattern","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C2993660032","wikidata":"https://www.wikidata.org/wiki/Q746984","display_name":"Traffic speed","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2840000092983246},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.257999986410141}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i18.38599","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i18.38599","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i18.38599","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i18.38599","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8118606805801392,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"traffic":[1,24,45,95,184],"forecasting":[2,25],"plays":[3],"a":[4,90,160],"vital":[5],"role":[6],"in":[7,146],"intelligent":[8],"transportation":[9],"systems,":[10],"enabling":[11],"applications":[12],"such":[13],"as":[14],"congestion":[15],"control,":[16],"route":[17],"planning,":[18],"and":[19,44,50,60,99,117,124,172,199],"urban":[20],"mobility":[21],"optimization.":[22],"However,":[23],"remains":[26],"challenging":[27],"due":[28],"to":[29,204],"two":[30,155],"key":[31],"factors:":[32],"(1)":[33],"complex":[34],"spatial":[35],"dependencies":[36],"arising":[37],"from":[38],"dynamic":[39],"interactions":[40],"between":[41,153],"road":[42],"segments":[43],"sensors":[46],"across":[47],"the":[48,52,108,138,154,169,176],"network,":[49],"(2)":[51],"coexistence":[53],"of":[54],"multi-scale":[55],"periodic":[56,62,98,103,122,170],"patterns":[57,63,119],"(e.g.,":[58,75],"daily":[59,116],"weekly":[61,118],"driven":[64],"by":[65,72,107,137],"human":[66],"routines)":[67],"with":[68,168,175],"irregular":[69],"fluctuations":[70],"caused":[71],"unpredictable":[73],"events":[74],"accidents,":[76],"weather,":[77],"or":[78],"construction).":[79],"To":[80,149],"tackle":[81],"these":[82],"challenges,":[83],"we":[84,157],"propose":[85],"HyperD":[86,188],"(Hybrid":[87],"Periodic":[88,110],"Decoupling),":[89],"novel":[91],"framework":[92],"that":[93,187],"decouples":[94],"data":[96],"into":[97],"residual":[100,128,177],"components.":[101],"The":[102,127],"component":[104],"is":[105,135],"handled":[106],"Hybrid":[109],"Representation":[111,141],"Module,":[112,142],"which":[113,130,164],"extracts":[114],"fine-grained":[115],"using":[120],"learnable":[121],"embeddings":[123],"spatial-temporal":[125],"attention.":[126],"component,":[129],"captures":[131],"non-periodic,":[132],"high-frequency":[133,173],"fluctuations,":[134],"modeled":[136],"Frequency-Aware":[139],"Residual":[140],"leveraging":[143],"complex-valued":[144],"MLP":[145],"frequency":[147],"domain.":[148],"enforce":[150],"semantic":[151],"separation":[152],"components,":[156],"further":[158],"introduce":[159],"Dual-View":[161],"Alignment":[162],"Loss,":[163],"aligns":[165],"low-frequency":[166],"information":[167,174],"branch":[171],"branch.":[178],"Extensive":[179],"experiments":[180],"on":[181],"four":[182],"real-world":[183],"datasets":[185],"demonstrate":[186],"achieves":[189],"state-of-the-art":[190],"prediction":[191],"accuracy,":[192],"while":[193],"offering":[194],"superior":[195],"robustness":[196],"under":[197],"disturbances":[198],"improved":[200],"computational":[201],"efficiency":[202],"compared":[203],"existing":[205],"methods.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-03-18T00:00:00"}