{"id":"https://openalex.org/W7131666913","doi":"https://doi.org/10.48550/arxiv.2602.21757","title":"Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction","display_name":"Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction","publication_year":2026,"publication_date":"2026-02-25","ids":{"openalex":"https://openalex.org/W7131666913","doi":"https://doi.org/10.48550/arxiv.2602.21757"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.21757","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126893333","display_name":"Xiannan Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Xiannan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126875288","display_name":"Quan Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Quan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126882769","display_name":"Chao Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Chao","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16345095,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"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":0.9925000071525574,"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":0.9925000071525574,"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":0.003000000026077032,"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":0.0010000000474974513,"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/exponential-smoothing","display_name":"Exponential smoothing","score":0.6524999737739563},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5902000069618225},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5360999703407288},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4799000024795532},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.4049000144004822},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.388700008392334},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.3880000114440918},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.37619999051094055}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7843000292778015},{"id":"https://openalex.org/C133710760","wikidata":"https://www.wikidata.org/wiki/Q775837","display_name":"Exponential smoothing","level":2,"score":0.6524999737739563},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5902000069618225},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5360999703407288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4934999942779541},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4799000024795532},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45500001311302185},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4049000144004822},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.388700008392334},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3880000114440918},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.37619999051094055},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34529998898506165},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.26750001311302185},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.21757","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.21757","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.21757","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.21757","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8111643195152283}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurately":[0],"predicting":[1],"short-term":[2],"traffic":[3,185],"demand":[4,140],"is":[5,78],"critical":[6],"for":[7,52,196],"intelligent":[8],"transportation":[9],"systems.":[10],"While":[11],"deep":[12],"learning":[13],"models":[14,151,187],"achieve":[15],"strong":[16],"performance":[17,168],"under":[18],"stationary":[19],"conditions,":[20],"their":[21],"accuracy":[22],"often":[23],"degrades":[24],"significantly":[25],"when":[26,162],"faced":[27],"with":[28,66,148,188],"distribution":[29,163],"shifts":[30,138,164],"caused":[31],"by":[32,111],"external":[33],"events":[34],"or":[35,54],"evolving":[36],"urban":[37,204],"dynamics.":[38,120],"Frequent":[39],"model":[40],"retraining":[41],"to":[42,44,90,117],"adapt":[43],"such":[45],"changes":[46],"incurs":[47],"prohibitive":[48],"computational":[49,174,190],"costs,":[50],"especially":[51],"large-scale":[53],"foundation":[55],"models.":[56],"To":[57],"address":[58],"this":[59],"challenge,":[60],"we":[61],"propose":[62],"FORESEE":[63,84,154,192],"(Forecasting":[64],"Online":[65],"Residual":[67],"Smoothing":[68],"and":[69,81,133,170,207],"Ensemble":[70],"Experts),":[71],"a":[72,112],"lightweight":[73],"online":[74,178],"adaptation":[75,183],"framework":[76],"that":[77,115,153],"accurate,":[79],"robust,":[80],"computationally":[82],"efficient.":[83],"operates":[85],"without":[86],"any":[87],"parameter":[88],"updates":[89],"the":[91,172,194],"base":[92],"model.":[93],"Instead,":[94],"it":[95],"corrects":[96],"today's":[97],"forecast":[98],"in":[99,139,202],"each":[100],"region":[101],"using":[102],"yesterday's":[103],"prediction":[104,157,200],"error,":[105],"stabilized":[106],"through":[107],"exponential":[108],"smoothing":[109,125],"guided":[110],"mixture-of-experts":[113],"mechanism":[114],"adapts":[116],"recent":[118],"error":[119,128],"Moreover,":[121],"an":[122],"adaptive":[123],"spatiotemporal":[124],"component":[126],"propagates":[127],"signals":[129],"across":[130],"neighboring":[131],"regions":[132],"time":[134],"slots,":[135],"capturing":[136],"coherent":[137],"patterns.":[141],"Extensive":[142],"experiments":[143],"on":[144],"seven":[145],"real-world":[146],"datasets":[147],"three":[149],"backbone":[150],"demonstrate":[152],"consistently":[155],"improves":[156],"accuracy,":[158],"maintains":[159],"robustness":[160],"even":[161],"are":[165,209],"minimal":[166],"(avoiding":[167],"degradation),":[169],"achieves":[171],"lowest":[173],"overhead":[175],"among":[176],"existing":[177],"methods.":[179],"By":[180],"enabling":[181],"real-time":[182],"of":[184],"forecasting":[186],"negligible":[189],"cost,":[191],"paves":[193],"way":[195],"deploying":[197],"reliable,":[198],"up-to-date":[199],"systems":[201],"dynamic":[203],"environments.":[205],"Code":[206],"data":[208],"available":[210],"at":[211],"https://github.com/xiannanhuang/FORESEE":[212]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-27T00:00:00"}
