{"id":"https://openalex.org/W2979622241","doi":"https://doi.org/10.1109/access.2019.2945821","title":"Research on the Air Traffic Flow Prediction Using a Deep Learning Approach","display_name":"Research on the Air Traffic Flow Prediction Using a Deep Learning Approach","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2979622241","doi":"https://doi.org/10.1109/access.2019.2945821","mag":"2979622241"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2945821","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2945821","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2019.2945821","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100410375","display_name":"Hong Liu","orcid":"https://orcid.org/0000-0003-4543-7663"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Liu","raw_affiliation_strings":["National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066617875","display_name":"Yi Lin","orcid":"https://orcid.org/0000-0002-7194-5023"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Lin","raw_affiliation_strings":["National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-7194-5023","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007904414","display_name":"Zhengmao Chen","orcid":"https://orcid.org/0000-0001-9861-7110"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengmao Chen","raw_affiliation_strings":["National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057805812","display_name":"Dongyue Guo","orcid":"https://orcid.org/0000-0003-0393-5197"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongyue Guo","raw_affiliation_strings":["National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100326967","display_name":"Jianwei Zhang","orcid":"https://orcid.org/0000-0002-5491-1745"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianwei Zhang","raw_affiliation_strings":["National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075590723","display_name":"Hailong Jing","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hailong Jing","raw_affiliation_strings":["National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24185976"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.6257,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.83359641,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"7","issue":null,"first_page":"148019","last_page":"148030"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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/T11489","display_name":"Air Traffic Management and Optimization","score":0.9983999729156494,"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/T10370","display_name":"Traffic and Road Safety","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.8291106224060059},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.679787278175354},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5720930099487305},{"id":"https://openalex.org/keywords/air-traffic-control","display_name":"Air traffic control","score":0.5577276945114136},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5575745105743408},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5377846360206604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4964962601661682},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.48315757513046265},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.454490602016449},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.4381345510482788},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4148745536804199},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3562736511230469},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.09348279237747192}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8291106224060059},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.679787278175354},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5720930099487305},{"id":"https://openalex.org/C166961238","wikidata":"https://www.wikidata.org/wiki/Q221395","display_name":"Air traffic control","level":2,"score":0.5577276945114136},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5575745105743408},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5377846360206604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4964962601661682},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.48315757513046265},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.454490602016449},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.4381345510482788},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4148745536804199},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3562736511230469},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.09348279237747192},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2945821","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2945821","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e10d22dfa68a4a7d9f39bb61a3f3338f","is_oa":true,"landing_page_url":"https://doaj.org/article/e10d22dfa68a4a7d9f39bb61a3f3338f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 148019-148030 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2945821","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2945821","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.47999998927116394}],"awards":[{"id":"https://openalex.org/G81533810","display_name":null,"funder_award_id":"U1833115","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W28988658","https://openalex.org/W186922744","https://openalex.org/W639708223","https://openalex.org/W1538131130","https://openalex.org/W1546771929","https://openalex.org/W1922658220","https://openalex.org/W1964796242","https://openalex.org/W1983364832","https://openalex.org/W2010502769","https://openalex.org/W2014933206","https://openalex.org/W2036785686","https://openalex.org/W2083238230","https://openalex.org/W2110645419","https://openalex.org/W2119464071","https://openalex.org/W2129481613","https://openalex.org/W2135604164","https://openalex.org/W2140341559","https://openalex.org/W2145527836","https://openalex.org/W2153418633","https://openalex.org/W2156303437","https://openalex.org/W2265846598","https://openalex.org/W2269942773","https://openalex.org/W2324154403","https://openalex.org/W2332338831","https://openalex.org/W2377546673","https://openalex.org/W2385171804","https://openalex.org/W2399733683","https://openalex.org/W2504641815","https://openalex.org/W2508429489","https://openalex.org/W2510808060","https://openalex.org/W2521095061","https://openalex.org/W2525689581","https://openalex.org/W2890485603","https://openalex.org/W3111669925","https://openalex.org/W3113871131","https://openalex.org/W4240905265","https://openalex.org/W6632100814","https://openalex.org/W6632360823","https://openalex.org/W6653349023","https://openalex.org/W6659849045","https://openalex.org/W6677950568","https://openalex.org/W6680583718","https://openalex.org/W6682864246","https://openalex.org/W6693505360"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4391621807","https://openalex.org/W4388561773","https://openalex.org/W2123753634","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,27,49,95,112,117,180,198,208],"improve":[3],"the":[4,9,29,51,65,78,84,119,123,130,146,159,186,191,205,213,218,228,231],"accuracy":[5],"and":[6,31,73,87,100,151,201,237],"robustness":[7],"of":[8,80,188,193,212,241],"air":[10,33,242],"traffic":[11,34,42,52,131,160,243],"prediction,":[12],"in":[13,55,176],"this":[14],"paper,":[15],"a":[16,56],"recurrent":[17],"3D":[18,66,85],"convolutional":[19],"neural":[20,82],"network":[21],"(R-3DCNN)":[22],"based":[23],"model":[24,124,156,233],"is":[25,46,61,126,158,196],"proposed":[26,48,107,214,232],"consider":[28,113],"spatial":[30],"temporal":[32,189],"transitions":[35],"comprehensively.":[36],"A":[37,59],"new":[38],"data":[39],"representation,":[40],"i.e.,":[41],"situation":[43],"graphics":[44],"(TSG),":[45],"firstly":[47],"illustrate":[50],"flow":[53,161,244],"situations":[54,132],"single":[57],"instant.":[58,172],"TSG":[60,104,108],"generated":[62],"by":[63,77,128,140],"splitting":[64],"earth":[67],"space":[68],"with":[69,137,221,245],"fixed":[70],"grid":[71],"map":[72],"flight":[74,135,164,249],"levels.":[75,250],"Motivated":[76],"applications":[79],"deep":[81],"network,":[83],"CNN":[86],"long":[88],"short-term":[89],"memory":[90],"(LSTM)":[91],"blocks":[92],"are":[93,178],"introduced":[94],"extract":[96],"high-level":[97],"features":[98],"(spatial":[99],"temporal)":[101],"from":[102],"an":[103],"sequence.":[105],"The":[106,155,173],"also":[109],"allows":[110],"us":[111],"some":[114],"real-time":[115,142],"factors":[116],"enrich":[118],"input":[120,125,194],"information.":[121],"Thus,":[122],"determined":[127],"combining":[129],"on":[133,162,247],"different":[134,163,248],"levels":[136,165],"areas":[138],"affected":[139],"other":[141],"factors,":[143,224],"such":[144],"as":[145],"adverse":[147],"weathers,":[148],"important":[149],"activities":[150],"general":[152],"aviation":[153],"flights.":[154],"output":[157],"for":[166],"studied":[167],"airspaces":[168],"at":[169],"next":[170],"prediction":[171,206,219,239],"busiest":[174],"routes":[175],"China":[177],"used":[179],"conduct":[181],"evaluation":[182],"experiments.":[183],"To":[184],"determine":[185],"influence":[187],"dependencies,":[190],"length":[192],"sequence":[195],"set":[197],"30,":[199],"60":[200],"90":[202],"minutes":[203],"before":[204],"instant":[207],"select":[209],"optimal":[210],"architecture":[211],"model.":[215],"By":[216],"evaluating":[217],"results":[220,240],"three":[222],"statistical":[223],"we":[225],"can":[226,234],"draw":[227],"conclusion":[229],"that":[230],"obtain":[235],"accurate":[236],"stable":[238],"distribution":[246]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}