乐胖代购免代理版

{"id":"https://openalex.org/W4391769663","doi":"https://doi.org/10.1109/itsc57777.2023.10421995","title":"Dynamic Self-Mutual Correlated Graph Convolutional Network for Traffic Prediction","display_name":"Dynamic Self-Mutual Correlated Graph Convolutional Network for Traffic Prediction","publication_year":2023,"publication_date":"2023-09-24","ids":{"openalex":"https://openalex.org/W4391769663","doi":"https://doi.org/10.1109/itsc57777.2023.10421995"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc57777.2023.10421995","pdf_url":null,"source":{"id":"https://openalex.org/S4363607737","display_name":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","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":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100584477","display_name":"Hao-yuan PANG","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyuan Pang","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,National Engineering Research Center of Mobile Network Technologies,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,National Engineering Research Center of Mobile Network Technologies,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025187681","display_name":"Qiang Wang","orcid":"https://orcid.org/0000-0002-9392-475X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Wang","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,National Engineering Research Center of Mobile Network Technologies,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,National Engineering Research Center of Mobile Network Technologies,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100604134","display_name":"Chen Xu","orcid":"https://orcid.org/0000-0002-5306-2155"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Xu","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,National Engineering Research Center of Mobile Network Technologies,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,National Engineering Research Center of Mobile Network Technologies,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"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":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":67},"biblio":{"volume":null,"issue":null,"first_page":"549","last_page":"555"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9725,"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":0.9725,"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/T10320","display_name":"Neural Networks and Applications","score":0.9396,"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"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7159223},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5122718},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.39669204},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37313873}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc57777.2023.10421995","pdf_url":null,"source":{"id":"https://openalex.org/S4363607737","display_name":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","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":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[{"funder":"https://openalex.org/F4320335843","funder_display_name":"Beijing Science and Technology Planning Project","award_id":"Z191100007519003"}],"datasets":[],"versions":[],"referenced_works_count":22,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W2069929199","https://openalex.org/W2130942839","https://openalex.org/W2157144902","https://openalex.org/W2345702419","https://openalex.org/W2528639018","https://openalex.org/W2903871660","https://openalex.org/W2904653841","https://openalex.org/W2907492528","https://openalex.org/W2912551914","https://openalex.org/W2950817888","https://openalex.org/W2963358464","https://openalex.org/W2964015378","https://openalex.org/W2996847713","https://openalex.org/W2997848713","https://openalex.org/W3038981236","https://openalex.org/W3103720336","https://openalex.org/W3158304688","https://openalex.org/W3174022889","https://openalex.org/W3206238760","https://openalex.org/W4200547309","https://openalex.org/W43001522"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2530322880","https://openalex.org/W2478288626","https://openalex.org/W2390279801","https://openalex.org/W2382290278","https://openalex.org/W2376932109","https://openalex.org/W2358668433","https://openalex.org/W2350741829","https://openalex.org/W2001405890","https://openalex.org/W1596801655"],"abstract_inverted_index":{"Precise":[0],"predicting":[1],"of":[2,13,34,52,87,148,160,197],"traffic":[3,35,93,161,172],"patterns":[4,94],"is":[5,181],"imperative":[6],"to":[7,47,57,77,90,98,133,192],"improve":[8],"the":[9,18,25,32,38,50,70,104,112,124,135,140,157,165,178],"functionality":[10],"and":[11,28,95,119,151,187,199],"efficiency":[12],"intelligent":[14],"transportation":[15],"systems.":[16],"Currently,":[17],"spatiotemporal":[19],"deep":[20],"learning":[21],"methods":[22,194],"are":[23],"among":[24],"most":[26],"successful":[27],"promising":[29],"approaches.":[30],"However,":[31],"task":[33],"prediction":[36],"encounters":[37],"following":[39],"challenges":[40],"that":[41,177],"must":[42],"be":[43],"addressed:":[44],"1)":[45],"How":[46,56],"dynamically":[48],"describe":[49],"inhomogeneity":[51],"different":[53],"periods.":[54],"2)":[55],"capture":[58],"global":[59,109,152],"dependencies":[60,153],"caused":[61,114],"by":[62,115],"hidden":[63,117],"factors.":[64,121],"In":[65],"this":[66],"paper,":[67],"we":[68],"propose":[69],"Dynamic":[71],"Self-Mutual":[72],"Correlated":[73],"Graph":[74],"Convolutional":[75],"Network(DSMCnet)":[76],"address":[78],"these":[79],"challenges.":[80],"DSMCnet":[81],"employs":[82],"a":[83,96],"sequence-to-sequence":[84],"architecture,":[85],"consisting":[86],"an":[88],"encoder":[89],"learn":[91],"historical":[92],"decoder":[97],"make":[99],"predictions.":[100],"This":[101],"framework":[102],"extracts":[103,139],"mutual":[105],"correlation":[106,142],"contained":[107],"in":[108,195],"data,":[110],"retaining":[111],"effect":[113],"both":[116],"factors":[118],"quantified":[120],"It":[122],"uses":[123],"dynamic":[125,149],"convolution":[126,150],"operator":[127],"based":[128],"on":[129,167],"node":[130],"state":[131],"distance":[132],"get":[134],"inhomogeneity.":[136],"Then":[137],"it":[138],"self":[141],"with":[143],"weighted":[144],"parameters.":[145],"The":[146],"cooperation":[147],"mechanisms":[154],"effectively":[155],"improves":[156],"expressive":[158],"ability":[159],"patterns.":[162],"We":[163],"evaluate":[164],"model":[166,180],"two":[168],"real-world":[169],"road":[170],"network":[171],"datasets.":[173],"Our":[174],"evaluation":[175],"suggests":[176],"proposed":[179],"approximately":[182],"7":[183],"%":[184,186,189],"-12":[185],"5%-11":[188],"improved":[190],"compared":[191],"baseline":[193],"terms":[196],"MAE":[198],"RMSE":[200],"metrics":[201],"respectively.":[202]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4391769663","counts_by_year":[],"updated_date":"2025-01-07T06:24:16.005759","created_date":"2024-02-14"}