{"id":"https://openalex.org/W3162720187","doi":"https://doi.org/10.3390/rs13101919","title":"Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting","display_name":"Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting","publication_year":2021,"publication_date":"2021-05-14","ids":{"openalex":"https://openalex.org/W3162720187","doi":"https://doi.org/10.3390/rs13101919","mag":"3162720187"},"language":"en","primary_location":{"id":"doi:10.3390/rs13101919","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13101919","pdf_url":"https://www.mdpi.com/2072-4292/13/10/1919/pdf?version=1621269403","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/13/10/1919/pdf?version=1621269403","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101625420","display_name":"Deqi Chen","orcid":"https://orcid.org/0000-0001-9854-9736"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Deqi Chen","raw_affiliation_strings":["MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"],"affiliations":[{"raw_affiliation_string":"MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007807794","display_name":"Xuedong Yan","orcid":"https://orcid.org/0000-0003-0120-9183"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xuedong Yan","raw_affiliation_strings":["MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"],"affiliations":[{"raw_affiliation_string":"MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029689557","display_name":"Xiaobing Liu","orcid":"https://orcid.org/0000-0003-1142-5816"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaobing Liu","raw_affiliation_strings":["MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"],"affiliations":[{"raw_affiliation_string":"MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100406719","display_name":"Liwei Wang","orcid":"https://orcid.org/0000-0002-7068-0748"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liwei Wang","raw_affiliation_strings":["MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"],"affiliations":[{"raw_affiliation_string":"MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045723332","display_name":"Fengxiao Li","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengxiao Li","raw_affiliation_strings":["MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"],"affiliations":[{"raw_affiliation_string":"MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100610620","display_name":"Shurong Li","orcid":"https://orcid.org/0000-0002-6719-8969"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shurong Li","raw_affiliation_strings":["MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"],"affiliations":[{"raw_affiliation_string":"MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5007807794"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":0.4745,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.61525068,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"13","issue":"10","first_page":"1919","last_page":"1919"},"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/T10524","display_name":"Traffic control and management","score":0.9972000122070312,"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"}},{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9966999888420105,"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":0.7989502549171448},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6999138593673706},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.6876310110092163},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5390680432319641},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.5276721119880676},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5041729211807251},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.49918341636657715},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48388680815696716},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.45034074783325195},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4413301646709442},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4239833950996399},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4233779311180115},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40915486216545105},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08286410570144653}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7989502549171448},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6999138593673706},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.6876310110092163},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5390680432319641},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.5276721119880676},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5041729211807251},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.49918341636657715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48388680815696716},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.45034074783325195},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4413301646709442},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4239833950996399},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4233779311180115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40915486216545105},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08286410570144653},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs13101919","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13101919","pdf_url":"https://www.mdpi.com/2072-4292/13/10/1919/pdf?version=1621269403","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b8846cf2c08a4012afa4bd6a897e9239","is_oa":true,"landing_page_url":"https://doaj.org/article/b8846cf2c08a4012afa4bd6a897e9239","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 13, Iss 10, p 1919 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/13/10/1919/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs13101919","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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":"Remote Sensing","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs13101919","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13101919","pdf_url":"https://www.mdpi.com/2072-4292/13/10/1919/pdf?version=1621269403","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8100000023841858,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3162720187.pdf","grobid_xml":"https://content.openalex.org/works/W3162720187.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W43001522","https://openalex.org/W1485009520","https://openalex.org/W1990368529","https://openalex.org/W2015284954","https://openalex.org/W2038031271","https://openalex.org/W2064675550","https://openalex.org/W2070193873","https://openalex.org/W2097443710","https://openalex.org/W2106752938","https://openalex.org/W2135604164","https://openalex.org/W2194775991","https://openalex.org/W2466554409","https://openalex.org/W2483502061","https://openalex.org/W2517194566","https://openalex.org/W2528639018","https://openalex.org/W2582510569","https://openalex.org/W2605516844","https://openalex.org/W2695874637","https://openalex.org/W2756203131","https://openalex.org/W2792476967","https://openalex.org/W2884604806","https://openalex.org/W2901504064","https://openalex.org/W2902008947","https://openalex.org/W2912017789","https://openalex.org/W2946537597","https://openalex.org/W2946782700","https://openalex.org/W2949072481","https://openalex.org/W2960749694","https://openalex.org/W2964051675","https://openalex.org/W2979864289","https://openalex.org/W2997415482","https://openalex.org/W2997848713","https://openalex.org/W2998652672","https://openalex.org/W2999982936","https://openalex.org/W3005147439","https://openalex.org/W3006854884","https://openalex.org/W3021728802","https://openalex.org/W3040628978","https://openalex.org/W3041377882","https://openalex.org/W3046369858","https://openalex.org/W3096877845","https://openalex.org/W3097856495","https://openalex.org/W3103720336","https://openalex.org/W3118731330","https://openalex.org/W3118842298","https://openalex.org/W3123852972","https://openalex.org/W3132234960","https://openalex.org/W6687483927","https://openalex.org/W6788157418"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2015747722","https://openalex.org/W2362050182","https://openalex.org/W2084856301","https://openalex.org/W2382418233","https://openalex.org/W2127970246","https://openalex.org/W4382618745","https://openalex.org/W2885125400","https://openalex.org/W1001352512","https://openalex.org/W2931688134"],"abstract_inverted_index":{"Urban":[0],"road":[1],"intersection":[2,38,45,54,133,237],"bottleneck":[3],"has":[4,212],"become":[5],"an":[6],"important":[7],"factor":[8],"in":[9,30,240],"causing":[10],"traffic":[11,15,46,55,142,200,248],"delay":[12],"and":[13,32,62,71,108,121,135,178,195,230,242],"restricting":[14],"efficiency.":[16,231,255],"It":[17],"is":[18,49],"essential":[19],"to":[20,36,51,81,102,130,150,191,250],"explore":[21],"the":[22,25,43,53,59,72,105,118,122,132,152,155,158,176,186,193,199,205,209,213,236,252],"prediction":[23,63,207,215,223,228],"of":[24,42,67,139,154,165,171,198],"operating":[26],"performance":[27,239],"at":[28,110],"intersections":[29,111,164],"real-time":[31,241],"formulate":[33],"corresponding":[34],"strategies":[35],"alleviate":[37],"delay.":[39],"However,":[40],"because":[41],"sophisticated":[44],"condition,":[47],"it":[48],"difficult":[50],"capture":[52,192],"Spatio-temporal":[56,141,194],"features":[57],"by":[58],"traditional":[60,206],"data":[61,69,101,161],"methods.":[64],"The":[65,181,217],"development":[66],"big":[68],"technology":[70],"deep":[73,92],"learning":[74,93],"model":[75,95,120,188,211],"provides":[76],"us":[77,190],"a":[78,89,137,168],"good":[79],"chance":[80],"address":[82],"this":[83,86,233],"challenge.":[84],"Therefore,":[85],"paper":[87],"proposes":[88],"multi-task":[90],"fusion":[91],"(MFDL)":[94],"based":[96],"on":[97],"massive":[98],"floating":[99,145,159],"car":[100,146,160],"effectively":[103],"predict":[104],"passing":[106],"time":[107,115],"speed":[109],"over":[112],"different":[113],"estimation":[114],"granularity.":[116],"Moreover,":[117],"grid":[119],"fuzzy":[123],"C-means":[124],"(FCM)":[125],"clustering":[126],"method":[127,234],"are":[128,173],"developed":[129],"identify":[131],"area":[134],"derive":[136],"set":[138],"key":[140],"parameters":[143],"from":[144,162],"data.":[147],"In":[148],"order":[149],"validate":[151],"effectiveness":[153],"proposed":[156,210],"model,":[157],"ten":[163],"Beijing":[166],"with":[167,204],"sampling":[169],"rate":[170],"3s":[172],"adopted":[174],"for":[175,247],"training":[177],"test":[179],"process.":[180],"experiment":[182],"result":[183],"shows":[184],"that":[185],"MFDL":[187],"enables":[189],"topology":[196],"feature":[197],"state":[201],"efficiently.":[202],"Compared":[203],"method,":[208],"best":[214],"performance.":[216],"interplay":[218],"between":[219],"these":[220],"two":[221],"targeted":[222],"variables":[224],"can":[225,243],"significantly":[226],"improve":[227,251],"accuracy":[229],"Thereby,":[232],"predicts":[235],"operation":[238,254],"provide":[244],"valuable":[245],"insights":[246],"managers":[249],"intersection\u2019s":[253]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}