{"id":"https://openalex.org/W4408696582","doi":"https://doi.org/10.1109/itsc58415.2024.10920177","title":"Two-Dimensional Traffic Status Imputation: A Spatial and Temporal Joint Learning Using Multi-Scale Information","display_name":"Two-Dimensional Traffic Status Imputation: A Spatial and Temporal Joint Learning Using Multi-Scale Information","publication_year":2024,"publication_date":"2024-09-24","ids":{"openalex":"https://openalex.org/W4408696582","doi":"https://doi.org/10.1109/itsc58415.2024.10920177"},"language":"en","primary_location":{"id":"doi:10.1109/itsc58415.2024.10920177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc58415.2024.10920177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},"type":"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/A5111679330","display_name":"Ruojian Li","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruojian Li","raw_affiliation_strings":["Zhejiang University,Hangzhou,China,310058"],"affiliations":[{"raw_affiliation_string":"Zhejiang University,Hangzhou,China,310058","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078350335","display_name":"Wentong Guo","orcid":"https://orcid.org/0000-0002-2488-3261"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentong Guo","raw_affiliation_strings":["Zhejiang University,Hangzhou,China,310058"],"affiliations":[{"raw_affiliation_string":"Zhejiang University,Hangzhou,China,310058","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058276518","display_name":"Hongsheng Qi","orcid":"https://orcid.org/0000-0002-2934-7601"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongsheng Qi","raw_affiliation_strings":["Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University,Hangzhou,China,310058"],"affiliations":[{"raw_affiliation_string":"Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University,Hangzhou,China,310058","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5111679330"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30412241,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"980","last_page":"986"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9987000226974487,"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":0.9987000226974487,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9781000018119812,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9452000260353088,"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":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7113907337188721},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5784046649932861},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.5383118391036987},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5062652826309204},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46054786443710327},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3872523903846741},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37639230489730835},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.13077214360237122},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12026563286781311},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.09691250324249268},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09338966012001038}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7113907337188721},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5784046649932861},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5383118391036987},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5062652826309204},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46054786443710327},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3872523903846741},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37639230489730835},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.13077214360237122},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12026563286781311},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.09691250324249268},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09338966012001038},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc58415.2024.10920177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc58415.2024.10920177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1638382529","display_name":null,"funder_award_id":"LGF22E080007","funder_id":"https://openalex.org/F4320338468","funder_display_name":"Basic Public Welfare Research Program of Zhejiang Province"},{"id":"https://openalex.org/G574419961","display_name":null,"funder_award_id":"52272314,52131202","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7966165018","display_name":null,"funder_award_id":"2021YFE0194400","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320338468","display_name":"Basic Public Welfare Research Program of Zhejiang Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W172222775","https://openalex.org/W1841820628","https://openalex.org/W2015568638","https://openalex.org/W2480680997","https://openalex.org/W2801457640","https://openalex.org/W2890672150","https://openalex.org/W2896642734","https://openalex.org/W2963360736","https://openalex.org/W2964189376","https://openalex.org/W2978273467","https://openalex.org/W3004008526","https://openalex.org/W3035229572","https://openalex.org/W3037106793","https://openalex.org/W3165312697","https://openalex.org/W3167202680","https://openalex.org/W3174697924","https://openalex.org/W3176061695","https://openalex.org/W4237650824","https://openalex.org/W4280581140","https://openalex.org/W4289516034","https://openalex.org/W4290098428","https://openalex.org/W4309623216","https://openalex.org/W4319335604","https://openalex.org/W4321073685","https://openalex.org/W4385248694","https://openalex.org/W4385618374","https://openalex.org/W6739901393","https://openalex.org/W6763701032","https://openalex.org/W6795140394","https://openalex.org/W6845625448"],"related_works":["https://openalex.org/W4211215373","https://openalex.org/W3217094455","https://openalex.org/W3119637569","https://openalex.org/W2989589450","https://openalex.org/W2405773734","https://openalex.org/W3123325766","https://openalex.org/W2791189374","https://openalex.org/W4403319084","https://openalex.org/W2374234271","https://openalex.org/W3208043357"],"abstract_inverted_index":{"Missing":[0],"values":[1],"imputation":[2,26,49],"in":[3,27,58,147,154],"traffic":[4,28,46,59,140],"time":[5,29,47,60,72,130,158],"series":[6,30,48,73,159],"is":[7,86,163],"a":[8,19,44],"significant":[9],"research":[10],"endeavor":[11],"within":[12],"intelligent":[13],"transportation":[14],"systems.":[15],"This":[16,99],"study":[17,149],"introduces":[18],"multi-task":[20],"training":[21],"approach":[22],"for":[23,109],"missing":[24],"data":[25],"and":[31,54,96],"observed":[32],"reconstruction.":[33],"The":[34,115,161],"paper":[35,63,100,134],"presents":[36],"the":[37,52,79,83,102,105,111,120,124,133,144,155],"Masked":[38],"Imputation":[39],"Task":[40],"(MIT)":[41],"Block":[42],"as":[43],"novel":[45],"approach.":[50],"Given":[51],"substantial":[53],"intricate":[55],"seasonality":[56],"inherent":[57],"series,":[61],"this":[62,148],"employs":[64],"Fast":[65],"Fourier":[66],"Variation":[67,95],"to":[68,88,129],"transform":[69],"one-dimensional":[70],"multi-scale":[71],"into":[74],"two-dimensional":[75],"data,":[76],"thereby":[77],"representing":[78],"sequence":[80,90],"information.":[81],"Additionally,":[82],"MLP":[84],"structure":[85],"utilized":[87],"capture":[89],"features":[91],"across":[92],"both":[93],"Intraperiod":[94],"Interperiod":[97],"Variation.":[98],"proposes":[101],"utilization":[103],"of":[104,123,157],"Diagonally-Masked":[106],"Self-Attention":[107],"mechanism":[108,126],"achieving":[110],"observation":[112],"reconstruction":[113,121],"task.":[114],"diagonal":[116],"mask":[117],"substantially":[118],"improves":[119],"capability":[122],"self-attention":[125],"when":[127],"applied":[128],"series.":[131],"Subsequently,":[132],"conducts":[135],"experiments":[136],"using":[137],"an":[138],"open-source":[139],"dataset,":[141],"demonstrating":[142],"that":[143],"proposed":[145],"model":[146],"outperforms":[150],"state-of-the-art":[151],"models":[152],"significantly":[153],"domain":[156],"analysis.":[160],"code":[162],"publicly":[164],"available":[165],"at":[166],"https://github.com/RuojianLi/MAITTS.":[167]},"counts_by_year":[],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}
