{"id":"https://openalex.org/W2989870471","doi":"https://doi.org/10.1109/tkde.2019.2954868","title":"Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting","display_name":"Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting","publication_year":2019,"publication_date":"2019-11-28","ids":{"openalex":"https://openalex.org/W2989870471","doi":"https://doi.org/10.1109/tkde.2019.2954868","mag":"2989870471"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2019.2954868","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tkde.2019.2954868","pdf_url":"https://ieeexplore.ieee.org/ielx7/69/9427778/08917560.pdf","source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ieeexplore.ieee.org/ielx7/69/9427778/08917560.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059672283","display_name":"Abdelkader Baggag","orcid":"https://orcid.org/0000-0001-8742-5519"},"institutions":[{"id":"https://openalex.org/I4210144839","display_name":"Hamad bin Khalifa University","ror":"https://ror.org/03eyq4y97","country_code":"QA","type":"education","lineage":["https://openalex.org/I4210144839"]}],"countries":["QA"],"is_corresponding":true,"raw_author_name":"Abdelkader Baggag","raw_affiliation_strings":["Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar"],"raw_orcid":"https://orcid.org/0000-0001-8742-5519","affiliations":[{"raw_affiliation_string":"Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar","institution_ids":["https://openalex.org/I4210144839"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013259491","display_name":"Sofiane Abbar","orcid":"https://orcid.org/0000-0002-2819-8691"},"institutions":[{"id":"https://openalex.org/I4210144839","display_name":"Hamad bin Khalifa University","ror":"https://ror.org/03eyq4y97","country_code":"QA","type":"education","lineage":["https://openalex.org/I4210144839"]}],"countries":["QA"],"is_corresponding":false,"raw_author_name":"Sofiane Abbar","raw_affiliation_strings":["Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar"],"raw_orcid":"https://orcid.org/0000-0002-2819-8691","affiliations":[{"raw_affiliation_string":"Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar","institution_ids":["https://openalex.org/I4210144839"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101437672","display_name":"Ankit Sharma","orcid":"https://orcid.org/0000-0002-1853-2035"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ankit Sharma","raw_affiliation_strings":["Department of Computer Science, University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":"https://orcid.org/0000-0002-1853-2035","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083588635","display_name":"Tahar Zanouda","orcid":"https://orcid.org/0009-0005-3646-600X"},"institutions":[{"id":"https://openalex.org/I1306339040","display_name":"Ericsson (Sweden)","ror":"https://ror.org/05a7rhx54","country_code":"SE","type":"company","lineage":["https://openalex.org/I1306339040"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Tahar Zanouda","raw_affiliation_strings":["Ericsson, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ericsson, Sweden","institution_ids":["https://openalex.org/I1306339040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032024943","display_name":"Abdulaziz Al-Homaid","orcid":"https://orcid.org/0000-0003-2699-4368"},"institutions":[{"id":"https://openalex.org/I4210144839","display_name":"Hamad bin Khalifa University","ror":"https://ror.org/03eyq4y97","country_code":"QA","type":"education","lineage":["https://openalex.org/I4210144839"]}],"countries":["QA"],"is_corresponding":false,"raw_author_name":"Abdulaziz Al-Homaid","raw_affiliation_strings":["Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar"],"raw_orcid":"https://orcid.org/0000-0003-2699-4368","affiliations":[{"raw_affiliation_string":"Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar","institution_ids":["https://openalex.org/I4210144839"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045390773","display_name":"Abhiraj Mohan","orcid":null},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhiraj Mohan","raw_affiliation_strings":["Department of Computer Science, University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002187701","display_name":"Jaideep Srivastava","orcid":"https://orcid.org/0000-0001-9385-7545"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jaideep Srivastava","raw_affiliation_strings":["Department of Computer Science, University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":"https://orcid.org/0000-0001-9385-7545","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5059672283"],"corresponding_institution_ids":["https://openalex.org/I4210144839"],"apc_list":null,"apc_paid":{"value":1392,"currency":"EUR","value_usd":1501},"fwci":1.4643,"has_fulltext":true,"cited_by_count":46,"citation_normalized_percentile":{"value":0.82305736,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"33","issue":"6","first_page":"2573","last_page":"2587"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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/T12303","display_name":"Tensor decomposition and applications","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"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":0.9840999841690063,"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/computer-science","display_name":"Computer science","score":0.8006550073623657},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5462120175361633},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5374759435653687},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.5122780203819275},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4809098243713379},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.4803841710090637},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45401138067245483},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.443204790353775},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4407593309879303},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37540990114212036},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3521580696105957},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1725124716758728}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8006550073623657},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5462120175361633},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5374759435653687},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.5122780203819275},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4809098243713379},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4803841710090637},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45401138067245483},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.443204790353775},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4407593309879303},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37540990114212036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3521580696105957},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1725124716758728},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tkde.2019.2954868","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tkde.2019.2954868","pdf_url":"https://ieeexplore.ieee.org/ielx7/69/9427778/08917560.pdf","source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"},{"id":"pmh:oai:figshare.com:article/24006414","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Learning_Spatiotemporal_Latent_Factors_of_Traffic_via_Regularized_Tensor_Factorization_Imputing_Missing_Values_and_Forecasting/24006414","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1109/tkde.2019.2954868","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tkde.2019.2954868","pdf_url":"https://ieeexplore.ieee.org/ielx7/69/9427778/08917560.pdf","source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2989870471.pdf","grobid_xml":"https://content.openalex.org/works/W2989870471.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1615057313","https://openalex.org/W1798398164","https://openalex.org/W1864134408","https://openalex.org/W1963757997","https://openalex.org/W1967444754","https://openalex.org/W1978346123","https://openalex.org/W1982469530","https://openalex.org/W2003924793","https://openalex.org/W2004891103","https://openalex.org/W2008345648","https://openalex.org/W2024165284","https://openalex.org/W2027047167","https://openalex.org/W2049500727","https://openalex.org/W2057685268","https://openalex.org/W2096532744","https://openalex.org/W2111346360","https://openalex.org/W2112738128","https://openalex.org/W2116111642","https://openalex.org/W2141280932","https://openalex.org/W2144475703","https://openalex.org/W2153458569","https://openalex.org/W2159788726","https://openalex.org/W2162171343","https://openalex.org/W2162521467","https://openalex.org/W2165178985","https://openalex.org/W2276747974","https://openalex.org/W2350384743","https://openalex.org/W2520495422","https://openalex.org/W2552480641","https://openalex.org/W3122868618","https://openalex.org/W6684306577","https://openalex.org/W6729542563"],"related_works":["https://openalex.org/W3014300295","https://openalex.org/W2797845355","https://openalex.org/W2954233016","https://openalex.org/W2377146919","https://openalex.org/W3207437239","https://openalex.org/W4361732478","https://openalex.org/W2026561823","https://openalex.org/W4284688182","https://openalex.org/W2497048638","https://openalex.org/W4323519561"],"abstract_inverted_index":{"Intelligent":[0],"transportation":[1],"systems":[2],"are":[3,133,138,253],"a":[4,119,127,154,200,212,249,270],"key":[5],"component":[6],"in":[7,35,104,118,130,153,171,180,256],"smart":[8,45],"cities,":[9],"and":[10,13,33,41,66,83,110,136,158,174,210,286,301,309,311],"the":[11,16,24,60,67,73,87,96,105,144,159,164,168,172,177,181,204,218,223,227,236,240,243,262,266,281,291,298,303],"estimation":[12],"prediction":[14],"of":[15,26,63,70,98,107,112,156,206,226,235,242,265,283],"spatiotemporal":[17,49],"traffic":[18,27,53,88,113,278,304,318],"state":[19,264],"is":[20,54,124,161,294,307],"critical":[21],"to":[22,37,52,59,80,162,175,191,216,260,316],"capture":[23],"dynamics":[25],"congestion,":[28],"i.e.,":[29],"its":[30],"generation,":[31],"propagation":[32],"mitigation,":[34],"order":[36],"increase":[38],"operational":[39],"efficiency":[40],"improve":[42],"livability":[43],"within":[44],"cities.":[46],"And":[47],"while":[48,221],"data":[50,74,100,189,279,300],"related":[51,79],"becoming":[55],"common":[56],"place":[57],"due":[58,190],"wide":[61],"availability":[62],"cheap":[64],"sensors":[65,149,185],"rapid":[68],"deployment":[69],"IoT":[71],"platforms,":[72],"still":[75],"suffer":[76],"some":[77],"challenges":[78],"sparsity,":[81],"incompleteness,":[82],"noise":[84],"which":[85,131,184,231],"makes":[86],"analytics":[89],"difficult.":[90],"In":[91],"this":[92],"article,":[93],"we":[94],"investigate":[95],"problem":[97],"missing":[99,178,219,299],"or":[101],"noisy":[102],"information":[103],"context":[106],"real-time":[108],"monitoring":[109],"forecasting":[111],"congestion":[114],"for":[115,150,167,183,203,296],"road":[116,122,139,207,237,267],"networks":[117],"city.":[120],"The":[121,229,245],"network":[123,208,268],"represented":[125],"as":[126],"directed":[128],"graph":[129],"nodes":[132],"junctions":[134],"(intersections)":[135],"edges":[137,170],"segments.":[140],"We":[141,198],"assume":[142],"that":[143,290],"city":[145],"has":[146],"deployed":[147],"high-fidelity":[148],"speed":[151,165],"reading":[152],"subset":[155],"edges;":[157],"objective":[160],"infer":[163],"readings":[166],"remaining":[169],"network;":[173],"estimate":[176,217],"values":[179],"segments":[182],"have":[186],"stopped":[187],"generating":[188],"technical":[192],"problems":[193],"(e.g.,":[194],"battery,":[195],"network,":[196,238],"etc.).":[197],"propose":[199],"tensor":[201],"representation":[202],"series":[205],"snapshots,":[209],"develop":[211],"regularized":[213],"factorization":[214],"method":[215],"values,":[220],"learning":[222],"latent":[224],"factors":[225],"network.":[228],"regularizer,":[230],"incorporates":[232],"spatial":[233],"properties":[234],"improves":[239],"quality":[241],"results.":[244],"learned":[246],"factors,":[247],"with":[248,269,276],"graph-based":[250],"temporal":[251],"dependency,":[252],"then":[254],"used":[255],"an":[257],"autoregressive":[258],"algorithm":[259],"predict":[261],"future":[263],"large":[271],"horizon.":[272],"Extensive":[273],"numerical":[274],"experiments":[275],"real":[277],"from":[280],"cities":[282],"Doha":[284],"(Qatar)":[285],"Aarhus":[287],"(Denmark)":[288],"demonstrate":[289],"proposed":[292],"approach":[293],"appropriate":[295],"imputing":[297],"predicting":[302],"state.":[305],"It":[306],"accurate":[308],"efficient":[310],"can":[312],"easily":[313],"be":[314],"applied":[315],"other":[317],"datasets.":[319]},"counts_by_year":[{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
