{"id":"https://openalex.org/W3167466965","doi":"https://doi.org/10.1109/iwcmc51323.2021.9498905","title":"MTL-LSTM: Multi-Task Learning-based LSTM for Urban Traffic Flow Forecasting","display_name":"MTL-LSTM: Multi-Task Learning-based LSTM for Urban Traffic Flow Forecasting","publication_year":2021,"publication_date":"2021-06-28","ids":{"openalex":"https://openalex.org/W3167466965","doi":"https://doi.org/10.1109/iwcmc51323.2021.9498905","mag":"3167466965"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc51323.2021.9498905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc51323.2021.9498905","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Wireless Communications and Mobile Computing (IWCMC)","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/A5079512546","display_name":"Mostafa Karimzadeh","orcid":"https://orcid.org/0000-0002-1949-6857"},"institutions":[{"id":"https://openalex.org/I118564535","display_name":"University of Bern","ror":"https://ror.org/02k7v4d05","country_code":"CH","type":"education","lineage":["https://openalex.org/I118564535"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Mostafa Karimzadeh","raw_affiliation_strings":["Institute of Computer Science, University of Bern, Switzerland"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Science, University of Bern, Switzerland","institution_ids":["https://openalex.org/I118564535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089392180","display_name":"Samuel Martin Schwegler","orcid":null},"institutions":[{"id":"https://openalex.org/I118564535","display_name":"University of Bern","ror":"https://ror.org/02k7v4d05","country_code":"CH","type":"education","lineage":["https://openalex.org/I118564535"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Samuel Martin Schwegler","raw_affiliation_strings":["Institute of Computer Science, University of Bern, Switzerland"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Science, University of Bern, Switzerland","institution_ids":["https://openalex.org/I118564535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078322039","display_name":"Zhongliang Zhao","orcid":"https://orcid.org/0000-0002-0979-9272"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongliang Zhao","raw_affiliation_strings":["School of Electronic and Information Engineering, Beihang University, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beihang University, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087658596","display_name":"Torsten Braun","orcid":"https://orcid.org/0000-0001-5968-7108"},"institutions":[{"id":"https://openalex.org/I118564535","display_name":"University of Bern","ror":"https://ror.org/02k7v4d05","country_code":"CH","type":"education","lineage":["https://openalex.org/I118564535"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Torsten Braun","raw_affiliation_strings":["Institute of Computer Science, University of Bern, Switzerland"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Science, University of Bern, Switzerland","institution_ids":["https://openalex.org/I118564535"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008921007","display_name":"Susana Sargento","orcid":"https://orcid.org/0000-0001-8761-8281"},"institutions":[{"id":"https://openalex.org/I4210120471","display_name":"Instituto de Telecomunica\u00e7\u00f5es","ror":"https://ror.org/02ht4fk33","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210120471"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Susana Sargento","raw_affiliation_strings":["Institute de Telecomunica\u00e7\u00f5es, Aveiro, Portugal"],"affiliations":[{"raw_affiliation_string":"Institute de Telecomunica\u00e7\u00f5es, Aveiro, Portugal","institution_ids":["https://openalex.org/I4210120471"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5079512546"],"corresponding_institution_ids":["https://openalex.org/I118564535"],"apc_list":null,"apc_paid":null,"fwci":1.0923,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.74835016,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"564","last_page":"569"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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":1.0,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9980000257492065,"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/T10524","display_name":"Traffic control and management","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.7738134860992432},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6330195665359497},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.6309041380882263},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5982571840286255},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5962313413619995},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5783362984657288},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5597490668296814},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.48889389634132385},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.470468133687973},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45595481991767883},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4424746334552765},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.4300302267074585},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3476889729499817},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.16937300562858582},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.12905749678611755},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10307824611663818}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7738134860992432},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6330195665359497},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.6309041380882263},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5982571840286255},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5962313413619995},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5783362984657288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5597490668296814},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.48889389634132385},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.470468133687973},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45595481991767883},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4424746334552765},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.4300302267074585},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3476889729499817},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.16937300562858582},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.12905749678611755},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10307824611663818},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iwcmc51323.2021.9498905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc51323.2021.9498905","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"},{"id":"pmh:oai:boris.unibe.ch:155234","is_oa":false,"landing_page_url":"https://boris.unibe.ch/155234/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401086","display_name":"Bern Open Repository and Information System (University of Bern)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I118564535","host_organization_name":"University of Bern","host_organization_lineage":["https://openalex.org/I118564535"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Karimzadeh, Mostafa; Schwegler, Samuel Martin; Zhao, Zhongliang; Braun, Torsten; Sargento, Susana (28 June 2021). MTL-LSTM: Multi-Task Learning-based LSTM for Urban Traffic Flow Forecasting. In: 17th International Wireless Communications & Mobile Computing Conference - IWCMC 2021 (pp. 564-569). IEEE 10.1109/IWCMC51323.2021.9498905 <http://dx.doi.org/10.1109/IWCMC51323.2021.9498905>","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8399999737739563}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W626441390","https://openalex.org/W1522301498","https://openalex.org/W1968719911","https://openalex.org/W1973943669","https://openalex.org/W1982978808","https://openalex.org/W2013640190","https://openalex.org/W2036785686","https://openalex.org/W2064675550","https://openalex.org/W2097443710","https://openalex.org/W2163922914","https://openalex.org/W2171234954","https://openalex.org/W2470641485","https://openalex.org/W2557283755","https://openalex.org/W2624871570","https://openalex.org/W2729281059","https://openalex.org/W2742079690","https://openalex.org/W2773229229","https://openalex.org/W2782672556","https://openalex.org/W2793820729","https://openalex.org/W2951877600","https://openalex.org/W2976882027","https://openalex.org/W4246435914","https://openalex.org/W6619978402","https://openalex.org/W6674683336","https://openalex.org/W6739365718","https://openalex.org/W6739977115","https://openalex.org/W6742058293","https://openalex.org/W6760385162"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W3032952384","https://openalex.org/W3017902212","https://openalex.org/W3007866289","https://openalex.org/W4390987329","https://openalex.org/W2361581724"],"abstract_inverted_index":{"Predicting":[0],"traffic":[1,21,63,88,114,127],"flow":[2,89,115,128],"in":[3,129],"large":[4],"cities":[5],"is":[6],"beneficial":[7],"for":[8,65],"a":[9,133],"wide":[10],"range":[11],"of":[12,74,144],"applications,":[13],"including":[14],"vehicle":[15,18],"navigation":[16],"services,":[17],"routing,":[19],"and":[20,36,97,138],"congestion":[22],"management.":[23],"In":[24],"this":[25],"scenario,":[26],"deep":[27],"learning":[28],"approaches":[29],"such":[30],"as":[31],"Recurrent":[32],"Neural":[33],"Networks":[34],"(RNN)":[35],"its":[37],"variant":[38],"Long":[39],"Short":[40],"Term":[41],"Memory":[42],"(LSTM)":[43],"are":[44],"excellent":[45],"alternatives":[46],"due":[47],"to":[48,51,71,93,112,146],"their":[49],"ability":[50],"learn":[52,60],"long-term":[53],"dependencies.":[54],"However,":[55],"these":[56],"neural":[57],"networks":[58],"only":[59],"the":[61,109,122,149],"temporal":[62,96],"information":[64,76],"each":[66],"trajectory":[67],"(moving":[68],"object),":[69],"failing":[70],"take":[72],"advantage":[73],"spatial":[75,98],"shared":[77],"by":[78],"neighboring":[79],"trajectories.":[80,102,119],"This":[81],"paper":[82],"introduces":[83],"MTL-LSTM":[84],"(Multi-Task":[85],"Learning-based":[86],"LSTM)":[87],"estimator,":[90],"which":[91],"attempts":[92],"explore":[94,113],"both":[95],"dependencies":[99],"among":[100],"adjacent":[101],"Specifically,":[103],"we":[104,125],"employ":[105],"LSTM":[106],"predictors":[107],"with":[108],"MTL":[110],"approach":[111],"patterns":[116],"across":[117],"urban":[118],"To":[120],"examine":[121],"proposed":[123],"model,":[124],"predict":[126],"Porto's":[130],"city":[131],"using":[132],"data":[134],"set":[135],"from":[136],"buses":[137],"taxies.":[139],"Our":[140],"experiments":[141],"show":[142],"improvements":[143],"10%":[145],"15%":[147],"over":[148],"state-of-the-art.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}