{"id":"https://openalex.org/W4384787459","doi":"https://doi.org/10.1109/tits.2023.3289193","title":"A Customized Data Fusion Tensor Approach for Interval-Wise Missing Network Volume Imputation","display_name":"A Customized Data Fusion Tensor Approach for Interval-Wise Missing Network Volume Imputation","publication_year":2023,"publication_date":"2023-07-18","ids":{"openalex":"https://openalex.org/W4384787459","doi":"https://doi.org/10.1109/tits.2023.3289193"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2023.3289193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2023.3289193","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://eprints.whiterose.ac.uk/207110/1/IEEE%20TITS-Interval-wise%20missing%20volume%20imputation_V8.1.2.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047364335","display_name":"Jiping Xing","orcid":"https://orcid.org/0000-0002-7477-4194"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiping Xing","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-7477-4194","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054726192","display_name":"Ronghui Liu","orcid":"https://orcid.org/0000-0003-0627-3184"},"institutions":[{"id":"https://openalex.org/I130828816","display_name":"University of Leeds","ror":"https://ror.org/024mrxd33","country_code":"GB","type":"education","lineage":["https://openalex.org/I130828816"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ronghui Liu","raw_affiliation_strings":["Institute for Transport Studies, University of Leeds, Leeds, U.K"],"raw_orcid":"https://orcid.org/0000-0003-0627-3184","affiliations":[{"raw_affiliation_string":"Institute for Transport Studies, University of Leeds, Leeds, U.K","institution_ids":["https://openalex.org/I130828816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013421156","display_name":"Anish Khadka","orcid":"https://orcid.org/0000-0003-4432-7198"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Khadka Anish","raw_affiliation_strings":["Department of Civil and Environmental Engineering, The University of Auckland, Auckland, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, The University of Auckland, Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100320722","display_name":"Zhiyuan Liu","orcid":"https://orcid.org/0000-0002-6331-0810"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyuan Liu","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":2.0129,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.84764155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"24","issue":"11","first_page":"12107","last_page":"12122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9991000294685364,"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":0.9991000294685364,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.983299970626831,"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/missing-data","display_name":"Missing data","score":0.8130652904510498},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.7570333480834961},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5938119292259216},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5823214650154114},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5529206991195679},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3613869845867157},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33757132291793823},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2576640546321869}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8130652904510498},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7570333480834961},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5938119292259216},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5823214650154114},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5529206991195679},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3613869845867157},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33757132291793823},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2576640546321869}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tits.2023.3289193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2023.3289193","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:200475","is_oa":false,"landing_page_url":"https://eprints.whiterose.ac.uk/200475/1/IEEE%20TITS-Interval-wise%20missing%20volume%20imputation_Revised%20Manuscript%20-%20WRR.pdf","pdf_url":"https://eprints.whiterose.ac.uk/200475/1/IEEE%20TITS-Interval-wise%20missing%20volume%20imputation_Revised%20Manuscript%20-%20WRR.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:207110","is_oa":true,"landing_page_url":"https://eprints.whiterose.ac.uk/207110/1/IEEE%20TITS-Interval-wise%20missing%20volume%20imputation_V8.1.2.pdf","pdf_url":"https://eprints.whiterose.ac.uk/207110/1/IEEE%20TITS-Interval-wise%20missing%20volume%20imputation_V8.1.2.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":{"id":"pmh:oai:eprints.whiterose.ac.uk:207110","is_oa":true,"landing_page_url":"https://eprints.whiterose.ac.uk/207110/1/IEEE%20TITS-Interval-wise%20missing%20volume%20imputation_V8.1.2.pdf","pdf_url":"https://eprints.whiterose.ac.uk/207110/1/IEEE%20TITS-Interval-wise%20missing%20volume%20imputation_V8.1.2.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8100000023841858}],"awards":[{"id":"https://openalex.org/G2151261136","display_name":null,"funder_award_id":"RSSB/494204565/aVSTP","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3309558537","display_name":null,"funder_award_id":"RSSB/494204565/aVSTP","funder_id":"https://openalex.org/F4320335087","funder_display_name":"Innovate UK"},{"id":"https://openalex.org/G3716380425","display_name":null,"funder_award_id":"52131203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3819342789","display_name":null,"funder_award_id":"71922007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G443959179","display_name":null,"funder_award_id":"71890970","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6986922007","display_name":null,"funder_award_id":"71890972","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335087","display_name":"Innovate UK","ror":"https://ror.org/05ar5fy68"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4384787459.pdf","grobid_xml":"https://content.openalex.org/works/W4384787459.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W1798398164","https://openalex.org/W1814521481","https://openalex.org/W1963826206","https://openalex.org/W1971757341","https://openalex.org/W1977146314","https://openalex.org/W1988420953","https://openalex.org/W2000215628","https://openalex.org/W2024165284","https://openalex.org/W2025603201","https://openalex.org/W2039417141","https://openalex.org/W2045487859","https://openalex.org/W2071729267","https://openalex.org/W2091449379","https://openalex.org/W2106411961","https://openalex.org/W2131241448","https://openalex.org/W2163150789","https://openalex.org/W2179076887","https://openalex.org/W2326228792","https://openalex.org/W2514294276","https://openalex.org/W2597533437","https://openalex.org/W2605264395","https://openalex.org/W2789819752","https://openalex.org/W2791202102","https://openalex.org/W2792660917","https://openalex.org/W2888918158","https://openalex.org/W2894714913","https://openalex.org/W2902048196","https://openalex.org/W2963928805","https://openalex.org/W2975127965","https://openalex.org/W2987097922","https://openalex.org/W3023367528","https://openalex.org/W3137769352","https://openalex.org/W3148396924","https://openalex.org/W3166291381","https://openalex.org/W3188900617","https://openalex.org/W3196788510","https://openalex.org/W3202635391","https://openalex.org/W4207015651","https://openalex.org/W4213415874","https://openalex.org/W4281257454","https://openalex.org/W4285160778","https://openalex.org/W4286610775","https://openalex.org/W4289761856","https://openalex.org/W4308005045","https://openalex.org/W4308709475","https://openalex.org/W4311165413","https://openalex.org/W4363676665","https://openalex.org/W6676179485","https://openalex.org/W6678911119"],"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/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549","https://openalex.org/W3123177881"],"abstract_inverted_index":{"Traffic":[0],"missing":[1,22,39,51,57,95,235],"data":[2,54,73,86,91,108,130,136,156,167,176,227],"imputation":[3,18,97,223],"is":[4,122],"a":[5,43,66,150,171,230],"fundamental":[6],"demand":[7],"and":[8,52,59,87,114,217,237],"crucial":[9],"application":[10],"for":[11,48,92,155],"real-world":[12,118],"intelligent":[13],"transportation":[14],"systems.":[15],"The":[16],"wide":[17,111],"methods":[19],"in":[20,46,109,165,175,193,200],"different":[21,166],"patterns":[23],"have":[24],"demonstrated":[25],"the":[26,72,93,103,110,162,191,194,197,222,241],"superiority":[27],"of":[28,106,196,243],"tensor":[29,68,77,153,206],"learning":[30],"by":[31],"effectively":[32],"characterizing":[33],"complex":[34,56],"spatiotemporal":[35,112],"correlations.":[36],"However,":[37],"interval-wise":[38,94],"volume":[40,96],"scenarios":[41,239],"remain":[42],"challenging":[44],"topic,":[45],"particular":[47],"long-term":[49],"continuous":[50],"high-dimensional":[53],"with":[55,117,140,161,233],"mechanisms":[58],"patterns.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64,147,169,183],"propose":[65],"customized":[67],"decomposition":[69,154],"framework,":[70],"named":[71],"fusion":[74],"CANDECOMP/PARAFAC":[75],"(DFCP)":[76],"decomposition,":[78],"to":[79,159,189],"combine":[80],"vehicle":[81,125],"license":[82,126],"plate":[83,127],"recognition":[84,128],"(LPR)":[85,129],"cellphone":[88],"location":[89],"(CL)":[90],"on":[98],"urban":[99],"networks.":[100],"Benefiting":[101],"from":[102,179],"unique":[104],"advantages":[105],"CL":[107,216],"coverage":[113],"correlates":[115],"highly":[116],"traffic":[119],"states,":[120],"it":[121],"fused":[123],"into":[124],"imputation.":[131,177],"They":[132],"are":[133],"regarded":[134],"as":[135],"types":[137],"dimension,":[138],"combined":[139],"other":[141],"dimensions":[142],"(different":[143],"segments,":[144],"time,":[145],"days),":[146],"innovatively":[148],"design":[149],"4-way":[151],"low-n-rank":[152],"reconstruction.":[157],"Furthermore,":[158],"deal":[160],"diverse":[163],"disturbances":[164],"dimensions,":[168],"derive":[170],"regularization":[172,181],"penalty":[173],"coefficient":[174],"Different":[178],"existing":[180],"schemes,":[182],"further":[184],"introduce":[185],"Bayesian":[186],"optimization":[187],"(BO)":[188],"enhance":[190],"performance":[192],"non-convexity":[195],"objective":[198],"function":[199],"our":[201,212],"regularized":[202],"hyperparametric":[203],"solutions":[204],"during":[205],"decomposition.":[207],"Numerical":[208],"experiments":[209],"highlight":[210],"that":[211],"proposed":[213],"method,":[214],"combining":[215],"LPR":[218,226],"data,":[219],"significantly":[220],"outperforms":[221],"method":[224],"using":[225],"only.":[228],"And":[229],"sensitivity":[231],"analysis":[232],"varying":[234],"length":[236],"rate":[238],"demonstrates":[240],"robustness":[242],"model":[244],"performance.":[245]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}