{"id":"https://openalex.org/W4416016017","doi":"https://doi.org/10.1145/3746252.3761212","title":"Extracting Global Temporal Patterns Within Short Look-Back Windows for Traffic Forecasting","display_name":"Extracting Global Temporal Patterns Within Short Look-Back Windows for Traffic Forecasting","publication_year":2025,"publication_date":"2025-11-07","ids":{"openalex":"https://openalex.org/W4416016017","doi":"https://doi.org/10.1145/3746252.3761212"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3761212","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3761212","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 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3746252.3761212","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Bo Sun","orcid":"https://orcid.org/0009-0006-6664-3238"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Sun","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China and Institute of Perceptual Intelligence, Pengcheng Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0006-6664-3238","affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China and Institute of Perceptual Intelligence, Pengcheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043667072","display_name":"Zhe Wu","orcid":"https://orcid.org/0000-0002-6982-2315"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Wu","raw_affiliation_strings":["Institute of Perceptual Intelligence, Pengcheng Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-6982-2315","affiliations":[{"raw_affiliation_string":"Institute of Perceptual Intelligence, Pengcheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120301192","display_name":"Zhiyuan Deng","orcid":"https://orcid.org/0009-0005-5494-3025"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyuan Deng","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China and Institute of Perceptual Intelligence, Pengcheng Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0005-5494-3025","affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China and Institute of Perceptual Intelligence, Pengcheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115601581","display_name":"Li Su","orcid":"https://orcid.org/0000-0001-5358-2786"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Su","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China and Institute of Perceptual Intelligence, Pengcheng Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-5358-2786","affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China and Institute of Perceptual Intelligence, Pengcheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101092469","display_name":"Qingfang Zheng","orcid":"https://orcid.org/0009-0006-7568-9318"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingfang Zheng","raw_affiliation_strings":["Institute of Perceptual Intelligence, Pengcheng Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0006-7568-9318","affiliations":[{"raw_affiliation_string":"Institute of Perceptual Intelligence, Pengcheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210136793"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2781","last_page":"2790"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9545000195503235,"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":0.9545000195503235,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.010200000368058681,"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":0.007199999876320362,"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/fuse","display_name":"Fuse (electrical)","score":0.5540000200271606},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.44749999046325684},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.43320000171661377},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.3741999864578247},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.35760000348091125},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.3310999870300293}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7253999710083008},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.5540000200271606},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.44749999046325684},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43320000171661377},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4189000129699707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3774999976158142},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.35760000348091125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3382999897003174},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.30730000138282776},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.296099990606308},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3761212","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3761212","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 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3746252.3761212","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3761212","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 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3840657713","display_name":null,"funder_award_id":"62441232","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W2004353783","https://openalex.org/W2624190409","https://openalex.org/W2747599906","https://openalex.org/W2807894308","https://openalex.org/W2903871660","https://openalex.org/W2950817888","https://openalex.org/W2969210779","https://openalex.org/W2997848713","https://openalex.org/W3080253043","https://openalex.org/W3136999308","https://openalex.org/W3188872815","https://openalex.org/W4283315029","https://openalex.org/W4306317966","https://openalex.org/W4382239616","https://openalex.org/W4385270240","https://openalex.org/W4385288150","https://openalex.org/W4393159807"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,56,87],"continuous":[2],"expansion":[3],"of":[4,89],"urban":[5,17],"areas,":[6],"accurate":[7],"and":[8,54,147,151],"effective":[9],"traffic":[10,18,21,158],"forecasting":[11,159],"has":[12],"become":[13],"essential":[14],"for":[15,116],"intelligent":[16],"management.":[19],"As":[20],"data":[22],"inherently":[23],"exhibits":[24],"temporal":[25,29,61,79,99,104],"dynamics,":[26],"modeling":[27],"its":[28],"patterns":[30,80],"is":[31,51,132,143,179],"critical":[32],"to":[33,58,134,145],"improve":[34],"prediction":[35],"performance.":[36],"However,":[37],"constrained":[38],"by":[39],"computational":[40],"complexity,":[41],"existing":[42],"methods":[43],"rely":[44],"primarily":[45],"on":[46,156],"short-term":[47],"historical":[48],"data,":[49],"which":[50],"typically":[52],"noisy":[53],"limits":[55],"ability":[57],"capture":[59],"global":[60,78,150],"patterns.":[62],"To":[63,85],"address":[64],"this":[65],"issue,":[66],"we":[67,107],"propose":[68],"a":[69,82,98,109,129,139],"novel":[70],"Dual-Stream":[71],"Transformer":[72],"model":[73,135],"(DSformer)":[74],"that":[75,101,112,166],"effectively":[76],"captures":[77],"through":[81],"time-index":[83,110],"model.":[84],"mitigate":[86],"impact":[88],"noise":[90],"in":[91],"short":[92],"look-back":[93],"windows,":[94],"DSformer":[95],"explicitly":[96],"learns":[97],"matrix":[100],"encodes":[102],"structured":[103],"dependencies.":[105],"Furthermore,":[106],"design":[108],"loss":[111],"encourages":[113],"similar":[114],"representations":[115],"adjacent":[117],"time":[118,125],"indices,":[119],"thereby":[120],"reducing":[121],"error":[122],"propagation":[123],"across":[124,161],"steps.":[126],"In":[127],"parallel,":[128],"historical-value":[130],"stream":[131],"employed":[133],"local":[136,152],"information.":[137,153],"Finally,":[138],"self-adaptive":[140],"learning":[141],"module":[142],"constructed":[144],"flexibly":[146],"accurately":[148],"fuse":[149],"Extensive":[154],"experiments":[155],"real-world":[157],"tasks":[160],"ten":[162],"diverse":[163],"scenarios":[164],"demonstrate":[165],"our":[167],"method":[168],"consistently":[169],"outperforms":[170],"state-of-the-art":[171],"baselines":[172],"while":[173],"maintaining":[174],"competitive":[175],"efficiency.":[176],"The":[177],"code":[178],"available":[180],"at":[181],"https://github.com/sky836/DSFormer.git.":[182]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-08T00:00:00"}
