{"id":"https://openalex.org/W7129062776","doi":"https://doi.org/10.1145/3773966.3779379","title":"A Two-Stage Anomaly-Aware Framework for Robust Traffic Forecasting with Memory-Guided GNNs","display_name":"A Two-Stage Anomaly-Aware Framework for Robust Traffic Forecasting with Memory-Guided GNNs","publication_year":2026,"publication_date":"2026-02-16","ids":{"openalex":"https://openalex.org/W7129062776","doi":"https://doi.org/10.1145/3773966.3779379"},"language":null,"primary_location":{"id":"doi:10.1145/3773966.3779379","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773966.3779379","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3773966.3779379","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008480744","display_name":"Pei-Xuan Li","orcid":"https://orcid.org/0000-0002-6014-4191"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Pei-Xuan Li","raw_affiliation_strings":["Department of Electrical Engineering, National Cheng Kung University, Tainan City, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Cheng Kung University, Tainan City, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088228765","display_name":"Cheng-Ru Chou","orcid":"https://orcid.org/0009-0001-6394-0986"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Cheng-Ru Chou","raw_affiliation_strings":["Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126126627","display_name":"Jhe-Wei Tsai","orcid":null},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jhe-Wei Tsai","raw_affiliation_strings":["Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007626411","display_name":"Hsun-Ping Hsieh","orcid":"https://orcid.org/0000-0001-6924-1337"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Hsun-Ping Hsieh","raw_affiliation_strings":["Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan and Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan and Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5008480744"],"corresponding_institution_ids":["https://openalex.org/I91807558"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.86441577,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1180","last_page":"1184"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9190000295639038,"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":0.9190000295639038,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.01720000058412552,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.005499999970197678,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.534500002861023},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5309000015258789},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45750001072883606},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4530999958515167},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.428600013256073},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3587000072002411},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.32190001010894775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.723800003528595},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.534500002861023},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5309000015258789},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45750001072883606},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4530999958515167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4320000112056732},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.428600013256073},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42320001125335693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3831000030040741},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3773966.3779379","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773966.3779379","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3773966.3779379","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773966.3779379","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.616068959236145,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2604847698","https://openalex.org/W2786827964","https://openalex.org/W2903871660","https://openalex.org/W2950361482","https://openalex.org/W2996847713","https://openalex.org/W2998436408","https://openalex.org/W3080253043","https://openalex.org/W4254182148","https://openalex.org/W4306317966","https://openalex.org/W4382239616","https://openalex.org/W4382449675","https://openalex.org/W4385562572","https://openalex.org/W4387846860","https://openalex.org/W4388343189"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"traffic":[1,42,75,135],"forecasting":[2,53],"plays":[3],"a":[4,50,93,97],"critical":[5],"role":[6],"in":[7,28],"modern":[8],"transportation":[9],"systems":[10],"by":[11,40],"enabling":[12],"effective":[13],"congestion":[14],"management":[15],"and":[16,69,77,96,109,127],"route":[17],"optimization.":[18],"Although":[19],"recent":[20],"deep":[21],"learning-based":[22],"models":[23,113,144],"have":[24],"shown":[25],"substantial":[26],"progress":[27],"modeling":[29],"complex":[30],"spatiotemporal":[31,70,114],"dependencies,":[32],"most":[33],"existing":[34],"methods":[35],"overlook":[36],"the":[37,85,104],"challenges":[38],"posed":[39],"anomalous":[41,79,146],"conditions.":[43,147],"To":[44],"address":[45],"this":[46],"gap,":[47],"we":[48],"propose":[49],"two-stage":[51],"anomaly-aware":[52],"correction":[54],"framework.":[55],"The":[56],"first":[57],"stage":[58],"employs":[59],"an":[60,88,117],"unsupervised":[61],"auto-regressive":[62],"anomaly":[63,89],"detector":[64],"that":[65,139],"combines":[66],"representation":[67,105],"learning":[68,100],"attention":[71],"to":[72,102],"capture":[73],"normal":[74,107],"patterns":[76,108],"filter":[78],"inputs":[80],"through":[81],"error":[82],"thresholding.":[83],"In":[84],"second":[86],"stage,":[87],"optimization":[90],"predictor":[91],"leverages":[92],"memory":[94],"module":[95],"sparsity":[98],"regularization":[99],"strategy":[101],"enhance":[103],"of":[106,122],"suppress":[110],"noise.":[111],"It":[112],"dependencies":[115],"using":[116],"information":[118],"propagation":[119],"layer":[120],"composed":[121],"sequential":[123],"small-kernel":[124],"temporal":[125],"convolutions":[126],"memory-guided":[128],"graph":[129],"convolutions.":[130],"Extensive":[131],"experiments":[132],"on":[133],"real-world":[134],"flow":[136],"datasets":[137],"demonstrate":[138],"our":[140],"framework":[141],"outperforms":[142],"state-of-the-art":[143],"under":[145]},"counts_by_year":[],"updated_date":"2026-02-23T20:09:44.859080","created_date":"2026-02-17T00:00:00"}