{"id":"https://openalex.org/W4306316915","doi":"https://doi.org/10.1145/3511808.3557480","title":"Traffic Speed Imputation with Spatio-Temporal Attentions and Cycle-Perceptual Training","display_name":"Traffic Speed Imputation with Spatio-Temporal Attentions and Cycle-Perceptual Training","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306316915","doi":"https://doi.org/10.1145/3511808.3557480"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557480","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557480","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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/A5002044458","display_name":"Qianxiong Xu","orcid":"https://orcid.org/0000-0001-9175-6783"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Qianxiong Xu","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006117974","display_name":"Sijie Ruan","orcid":"https://orcid.org/0000-0002-4520-7174"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sijie Ruan","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027315111","display_name":"Cheng Long","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Cheng Long","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044417807","display_name":"Liang Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Yu","raw_affiliation_strings":["Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114125419","display_name":"Chen Zhang","orcid":"https://orcid.org/0009-0008-1062-4550"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chen Zhang","raw_affiliation_strings":["Hong Kong Polytechnic University, Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Hong Kong Polytechnic University, Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I14243506"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5002044458"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":3.3551,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.94146341,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2280","last_page":"2289"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9951000213623047,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9639999866485596,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7789499759674072},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.7732900977134705},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5168562531471252},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.515033483505249},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4573991894721985},{"id":"https://openalex.org/keywords/traffic-speed","display_name":"Traffic speed","score":0.4358009099960327},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4257434904575348},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.4214523136615753},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41430792212486267}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7789499759674072},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7732900977134705},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5168562531471252},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.515033483505249},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4573991894721985},{"id":"https://openalex.org/C2993660032","wikidata":"https://www.wikidata.org/wiki/Q746984","display_name":"Traffic speed","level":2,"score":0.4358009099960327},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4257434904575348},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4214523136615753},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41430792212486267},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3511808.3557480","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557480","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2429386427","display_name":null,"funder_award_id":"Tier 2 Award MOE-T2EP20221-0013 and Tier 1 Award (RG77/21)","funder_id":"https://openalex.org/F4320320751","funder_display_name":"Ministry of Education - Singapore"},{"id":"https://openalex.org/G7270978258","display_name":null,"funder_award_id":"2019YFB2102100","funder_id":"https://openalex.org/F4320321540","funder_display_name":"Ministry of Science and Technology of the People's Republic of China"},{"id":"https://openalex.org/G7876055779","display_name":null,"funder_award_id":"AN-GC-2020-006","funder_id":"https://openalex.org/F4320320766","funder_display_name":"Nanyang Technological University"}],"funders":[{"id":"https://openalex.org/F4320320751","display_name":"Ministry of Education - Singapore","ror":"https://ror.org/01kcva023"},{"id":"https://openalex.org/F4320320766","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302"},{"id":"https://openalex.org/F4320321540","display_name":"Ministry of Science and Technology of the People's Republic of China","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1866403196","https://openalex.org/W1999110238","https://openalex.org/W2002928429","https://openalex.org/W2098722118","https://openalex.org/W2153458569","https://openalex.org/W2203947310","https://openalex.org/W2331128040","https://openalex.org/W2438471924","https://openalex.org/W2496357561","https://openalex.org/W2552480641","https://openalex.org/W2795142517","https://openalex.org/W2833324965","https://openalex.org/W2950019211","https://openalex.org/W3029687524","https://openalex.org/W3031359560"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549","https://openalex.org/W3179858851","https://openalex.org/W3123177881"],"abstract_inverted_index":{"The":[0,148],"phenomena":[1],"of":[2,10,24,33,129],"data":[3,16,25,71],"missing":[4],"are":[5,18,114],"common":[6],"in":[7],"the":[8,31,48,58,64,70,92,127,134],"field":[9],"traffic,":[11],"yet":[12],"existing":[13],"solutions":[14],"for":[15,47,83,110],"imputation":[17,50,79],"not":[19],"sufficient":[20],"due":[21],"to":[22,105,125],"challenges":[23],"sparsity,":[26],"complex":[27,54],"traffic":[28,55],"situations":[29],"and":[30,60,122,139,143],"lack":[32],"complete":[34],"ground":[35],"truths.":[36],"In":[37,74],"this":[38],"paper,":[39],"we":[40],"propose":[41],"a":[42],"novel":[43],"solution":[44],"called":[45],"STCPA":[46,52,76],"speed":[49],"problem.":[51],"captures":[53],"correlations":[56],"among":[57],"spatial":[59],"temporal":[61],"dimensions":[62],"via":[63],"attention":[65],"mechanism,":[66],"which":[67,90,102],"helps":[68,103],"mitigate":[69],"sparsity":[72],"issue.":[73],"addition,":[75],"adopts":[77],"an":[78,97],"cycle":[80],"consistency":[81],"constraint":[82],"providing":[84],"reliable":[85],"supervisions":[86],"on":[87,116,141],"unobserved":[88],"entries,":[89],"improves":[91],"training.":[93],"Furthermore,":[94],"it":[95,132],"incorporates":[96],"extra":[98],"Road-aware":[99],"Perceptual":[100],"Loss,":[101],"encourage":[104],"preserve":[106],"more":[107],"meaningful":[108],"semantics":[109],"imputation.":[111],"Extensive":[112],"experiments":[113],"conducted":[115],"two":[117],"real-world":[118],"datasets,":[119,146],"namely,":[120],"Chengdu":[121,142],"New":[123,144],"York,":[124],"demonstrate":[126],"effectiveness":[128],"STCPA,":[130],"e.g.,":[131],"outperforms":[133],"best":[135],"baseline":[136],"by":[137],"7.64%":[138],"5.00%":[140],"York":[145],"respectively.":[147],"code":[149],"is":[150],"available":[151],"at":[152],"https://github.com/Sam1224/STCPA.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}
