{"id":"https://openalex.org/W4285813948","doi":"https://doi.org/10.1109/iwcmc55113.2022.9824928","title":"WGAN-GP and LSTM based Prediction Model for Aircraft 4- D Traj ectory","display_name":"WGAN-GP and LSTM based Prediction Model for Aircraft 4- D Traj ectory","publication_year":2022,"publication_date":"2022-05-30","ids":{"openalex":"https://openalex.org/W4285813948","doi":"https://doi.org/10.1109/iwcmc55113.2022.9824928"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc55113.2022.9824928","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc55113.2022.9824928","pdf_url":null,"source":{"id":"https://openalex.org/S4363605313","display_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 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/A5100637601","display_name":"Lei Zhang","orcid":"https://orcid.org/0000-0002-8481-5914"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lei Zhang","raw_affiliation_strings":["School of Computer and Information Engineering, Henan University,Kaifeng,China,475004"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Henan University,Kaifeng,China,475004","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100704450","display_name":"Huiping Chen","orcid":"https://orcid.org/0000-0002-0227-9100"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huiping Chen","raw_affiliation_strings":["School of Computer and Information Engineering, Henan University,Kaifeng,China,475004"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Henan University,Kaifeng,China,475004","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051223530","display_name":"Peiyan Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peiyan Jia","raw_affiliation_strings":["School of Computer and Information Engineering, Henan University,Kaifeng,China,475004"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Henan University,Kaifeng,China,475004","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056608045","display_name":"Zhihong Tian","orcid":"https://orcid.org/0000-0002-9409-5359"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhihong Tian","raw_affiliation_strings":["Cyberspace Institute of Advanced Technology Guangdong University,Guangzhou,China,510006"],"affiliations":[{"raw_affiliation_string":"Cyberspace Institute of Advanced Technology Guangdong University,Guangzhou,China,510006","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060514022","display_name":"Xiaojiang Du","orcid":"https://orcid.org/0000-0003-4235-9671"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaojiang Du","raw_affiliation_strings":["Stevens Institute of Technology,Department of Electrical and Computer Engineering,Hoboken,NJ,USA,07030"],"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology,Department of Electrical and Computer Engineering,Hoboken,NJ,USA,07030","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100637601"],"corresponding_institution_ids":["https://openalex.org/I173899330"],"apc_list":null,"apc_paid":null,"fwci":1.1096,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76257155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"937","last_page":"942"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11489","display_name":"Air Traffic Management and Optimization","score":0.9994000196456909,"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":0.9994000196456909,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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":0.9962000250816345,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.851286768913269},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7148193717002869},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7096484899520874},{"id":"https://openalex.org/keywords/air-traffic-control","display_name":"Air traffic control","score":0.6557072401046753},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5635241270065308},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49480336904525757},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4403618574142456},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4347985088825226},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43097811937332153},{"id":"https://openalex.org/keywords/air-traffic-management","display_name":"Air traffic management","score":0.4226265847682953},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4095362424850464},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16803592443466187}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.851286768913269},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7148193717002869},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7096484899520874},{"id":"https://openalex.org/C166961238","wikidata":"https://www.wikidata.org/wiki/Q221395","display_name":"Air traffic control","level":2,"score":0.6557072401046753},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5635241270065308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49480336904525757},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4403618574142456},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4347985088825226},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43097811937332153},{"id":"https://openalex.org/C2776777543","wikidata":"https://www.wikidata.org/wiki/Q1361182","display_name":"Air traffic management","level":3,"score":0.4226265847682953},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4095362424850464},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16803592443466187},{"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},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwcmc55113.2022.9824928","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc55113.2022.9824928","pdf_url":null,"source":{"id":"https://openalex.org/S4363605313","display_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2097356905","https://openalex.org/W2102113734","https://openalex.org/W2335418428","https://openalex.org/W2379203248","https://openalex.org/W2385945733","https://openalex.org/W2511826256","https://openalex.org/W2889809831","https://openalex.org/W2904308181","https://openalex.org/W2904977651","https://openalex.org/W2963943323","https://openalex.org/W2971194525","https://openalex.org/W3000241400","https://openalex.org/W3041517319","https://openalex.org/W3044432665","https://openalex.org/W3104340740","https://openalex.org/W3140593863","https://openalex.org/W3205305833","https://openalex.org/W3214095867","https://openalex.org/W4295521014","https://openalex.org/W6735913928","https://openalex.org/W6781154782"],"related_works":["https://openalex.org/W3099765033","https://openalex.org/W2135905813","https://openalex.org/W2043775914","https://openalex.org/W631350582","https://openalex.org/W2008390173","https://openalex.org/W2585807273","https://openalex.org/W2091753509","https://openalex.org/W4205399654","https://openalex.org/W2519895254","https://openalex.org/W1973192480"],"abstract_inverted_index":{"The":[0,29,47,142],"rapid":[1],"growth":[2],"of":[3,81,147,163,195],"air":[4,26,62],"traffic":[5,27,63],"flow":[6],"has":[7,20],"brought":[8],"the":[9,17,42,79,85,108,127,135,139,152,161,175,180,192,196],"airspace":[10],"capacity":[11],"close":[12],"to":[13,39,57,68,78,150],"saturation":[14],"and,":[15],"at":[16],"same":[18],"time,":[19],"resulted":[21],"in":[22,41,133,160,179,186],"great":[23],"stress":[24],"for":[25],"controllers.":[28],"4-":[30,197],"D":[31,198],"trajectory-based":[32],"operation":[33],"system":[34,48],"is":[35,104,131],"an":[36],"important":[37],"solution":[38],"problems":[40],"current":[43,86,187],"civil":[44],"aviation":[45],"field.":[46],"mainly":[49],"relies":[50],"on":[51],"accurate":[52],"4-D":[53,87],"trajectory":[54,59,82,88],"prediction":[55,89,100,140,177,193],"technology":[56,90],"share":[58],"information":[60],"among":[61],"control,":[64],"airlines,":[65],"and":[66,74,99,117,138],"aircraft":[67],"achieve":[69],"coordinated":[70],"decision-making":[71],"between":[72],"flight":[73],"control.":[75],"However,":[76],"due":[77],"complexity":[80],"data":[83,97,149],"processing,":[84],"cannot":[91],"meet":[92],"actual":[93],"needs.":[94],"Therefore,":[95],"a":[96],"generation":[98,136],"network":[101,130],"model":[102,144,182],"(DGPNM)":[103],"proposed.":[105],"It":[106],"integrates":[107],"Wasserstein":[109],"generative":[110],"adversarial":[111],"networks":[112],"with":[113,171],"gradient":[114],"penalty":[115],"(WGAN-GP)":[116],"long-short-term":[118],"memory":[119],"(LSTM)":[120],"neu-ral":[121],"networks.":[122],"With":[123],"its":[124],"outstanding":[125],"performance,":[126],"LSTM":[128,164],"neural":[129],"utilized":[132],"both":[134],"module":[137],"module.":[141],"proposed":[143,181],"generates":[145],"plenty":[146],"sample":[148],"enlarge":[151],"train":[153],"set,":[154],"so":[155],"overfitting":[156],"could":[157],"be":[158],"reduced":[159],"process":[162],"training.":[165],"Experimental":[166],"results":[167],"prove":[168],"that":[169,185],"compared":[170],"other":[172],"classical":[173],"methods,":[174],"altitude":[176],"accuracy":[178,194],"far":[183],"exceeds":[184],"research":[188],"results,":[189],"which":[190],"improves":[191],"trajectory.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
