{"id":"https://openalex.org/W4390044085","doi":"https://doi.org/10.1109/tits.2023.3338251","title":"A Flight Arrival Time Prediction Method Based on Cluster Clustering-Based Modular With Deep Neural Network","display_name":"A Flight Arrival Time Prediction Method Based on Cluster Clustering-Based Modular With Deep Neural Network","publication_year":2023,"publication_date":"2023-12-21","ids":{"openalex":"https://openalex.org/W4390044085","doi":"https://doi.org/10.1109/tits.2023.3338251"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2023.3338251","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2023.3338251","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":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100764224","display_name":"Wu Deng","orcid":"https://orcid.org/0000-0002-6524-6760"},"institutions":[{"id":"https://openalex.org/I28813325","display_name":"Civil Aviation University of China","ror":"https://ror.org/03je71k37","country_code":"CN","type":"education","lineage":["https://openalex.org/I28813325"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wu Deng","raw_affiliation_strings":["College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-6524-6760","affiliations":[{"raw_affiliation_string":"College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China","institution_ids":["https://openalex.org/I28813325"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100654297","display_name":"Kunpeng Li","orcid":"https://orcid.org/0009-0003-0409-2627"},"institutions":[{"id":"https://openalex.org/I28813325","display_name":"Civil Aviation University of China","ror":"https://ror.org/03je71k37","country_code":"CN","type":"education","lineage":["https://openalex.org/I28813325"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kunpeng Li","raw_affiliation_strings":["College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China","institution_ids":["https://openalex.org/I28813325"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007345492","display_name":"Huimin Zhao","orcid":"https://orcid.org/0000-0002-8479-9539"},"institutions":[{"id":"https://openalex.org/I28813325","display_name":"Civil Aviation University of China","ror":"https://ror.org/03je71k37","country_code":"CN","type":"education","lineage":["https://openalex.org/I28813325"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huimin Zhao","raw_affiliation_strings":["College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-8479-9539","affiliations":[{"raw_affiliation_string":"College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China","institution_ids":["https://openalex.org/I28813325"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I28813325"],"apc_list":null,"apc_paid":null,"fwci":47.1674,"has_fulltext":false,"cited_by_count":69,"citation_normalized_percentile":{"value":0.99848409,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"25","issue":"6","first_page":"6238","last_page":"6247"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11489","display_name":"Air Traffic Management and Optimization","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T11489","display_name":"Air Traffic Management and Optimization","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9970999956130981,"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/T11599","display_name":"Aviation Industry Analysis and Trends","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2000","display_name":"General Economics, Econometrics and Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6825956106185913},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6647103428840637},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6203666925430298},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5564908981323242},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5507007837295532},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.5438239574432373},{"id":"https://openalex.org/keywords/modular-neural-network","display_name":"Modular neural network","score":0.4952954351902008},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4902963936328888},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40486425161361694},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3750655949115753},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.36385393142700195},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2653447389602661}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6825956106185913},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6647103428840637},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6203666925430298},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5564908981323242},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5507007837295532},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.5438239574432373},{"id":"https://openalex.org/C2781121602","wikidata":"https://www.wikidata.org/wiki/Q3504403","display_name":"Modular neural network","level":4,"score":0.4952954351902008},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4902963936328888},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40486425161361694},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3750655949115753},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.36385393142700195},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2653447389602661},{"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2023.3338251","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2023.3338251","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1610157109","display_name":null,"funder_award_id":"U2133205","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W193666822","https://openalex.org/W2014702186","https://openalex.org/W2150884987","https://openalex.org/W2586601162","https://openalex.org/W2801208118","https://openalex.org/W2887037553","https://openalex.org/W2923625485","https://openalex.org/W2983461979","https://openalex.org/W3023030391","https://openalex.org/W3028852288","https://openalex.org/W3037669510","https://openalex.org/W3040832692","https://openalex.org/W3139496481","https://openalex.org/W3189632276","https://openalex.org/W3193741318","https://openalex.org/W3217199630","https://openalex.org/W4205994975","https://openalex.org/W4210320959","https://openalex.org/W4214817073","https://openalex.org/W4220863771","https://openalex.org/W4281757494","https://openalex.org/W4308153699","https://openalex.org/W4310485189","https://openalex.org/W4313291741","https://openalex.org/W4322490886","https://openalex.org/W4323033675","https://openalex.org/W4361249171","https://openalex.org/W4372194290","https://openalex.org/W4379409259","https://openalex.org/W4381611284","https://openalex.org/W4384283571","https://openalex.org/W4384787464","https://openalex.org/W4386254442","https://openalex.org/W4386825433","https://openalex.org/W4387004587"],"related_works":["https://openalex.org/W1558201422","https://openalex.org/W2281615551","https://openalex.org/W2317332738","https://openalex.org/W2022575851","https://openalex.org/W2367028704","https://openalex.org/W794512309","https://openalex.org/W14051135","https://openalex.org/W575330181","https://openalex.org/W2789838507","https://openalex.org/W2166879558"],"abstract_inverted_index":{"With":[0],"the":[1,5,20,53,81,119,122,134,139,142,149,152,173,176,185,199],"rapid":[2],"development":[3],"of":[4,19,34,83,121,145,151,161],"air":[6,9],"transportation":[7],"industry,":[8],"traffic":[10],"is":[11,77,104,130],"facing":[12],"a":[13,64,126],"severe":[14],"test.":[15],"The":[16,168],"accurate":[17],"prediction":[18,157,178],"estimated":[21],"arrival":[22],"time":[23,177],"(EAT)":[24],"plays":[25],"an":[26],"important":[27],"role":[28],"in":[29,115,166],"rational":[30],"and":[31,56,58,86,183,196,204],"efficient":[32],"scheduling":[33],"flights.":[35],"Aircraft":[36],"EAT":[37,156],"predictions":[38],"often":[39],"rely":[40],"on":[41,141,158],"aircraft":[42],"performance":[43],"parameters":[44],"or":[45],"physical":[46],"trajectories.":[47],"This":[48,61],"gives":[49],"poor":[50],"consideration":[51],"to":[52,80,106,111,117,132,138],"flight":[54,84,87,159],"environment":[55],"take-off":[57],"landing":[59],"conditions.":[60],"paper":[62],"applies":[63],"data-driven":[65],"methodology":[66],"for":[67],"ETA":[68],"prediction.":[69],"A":[70],"Cluster":[71],"clustering-based":[72],"modular":[73],"integrated":[74],"DNN":[75],"(CC-MIDNN)":[76],"proposed.":[78],"According":[79],"information":[82],"mission":[85],"environment,":[88],"it":[89],"decomposes":[90],"complex":[91],"modeling":[92],"tasks":[93],"into":[94],"multiple":[95],"distinct":[96],"subtasks":[97],"using":[98],"cluster-based":[99],"clustering.":[100],"Then":[101],"Bayesian":[102],"optimization":[103],"used":[105],"design":[107],"corresponding":[108],"parallel":[109],"sub-networks":[110],"learn":[112],"different":[113],"sub-tasks":[114],"order":[116],"reduce":[118],"complexity":[120],"subsequent":[123],"sub-networks.":[124],"Finally,":[125],"new":[127],"integration":[128,197],"scheme":[129],"proposed":[131],"solve":[133],"overfitting":[135],"problem":[136],"due":[137],"reliance":[140],"training":[143],"accuracy":[144,179,203],"sub-modules.":[146],"To":[147],"verify":[148],"effectiveness":[150],"method,":[153],"CC-MIDNN":[154,174,200],"performs":[155],"data":[160],"European":[162],"Airlines":[163],"LIS":[164],"airport":[165],"2017.":[167],"experimental":[169],"results":[170],"show":[171],"that":[172],"improves":[175],"by":[180],"5.92":[181],"minutes":[182],"keeps":[184],"error":[186],"within":[187],"13":[188],"minutes.":[189],"Compared":[190],"with":[191],"other":[192],"deep":[193],"learning":[194],"algorithms":[195],"algorithms,":[198],"has":[201],"high":[202],"stability.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":38},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-04T08:18:43.703281","created_date":"2025-10-10T00:00:00"}