{"id":"https://openalex.org/W4310007446","doi":"https://doi.org/10.1109/ictc55196.2022.9952906","title":"Data Preprocessing Method for ANN-Based Travel Time Estimation Using Insufficient GPS Data","display_name":"Data Preprocessing Method for ANN-Based Travel Time Estimation Using Insufficient GPS Data","publication_year":2022,"publication_date":"2022-10-19","ids":{"openalex":"https://openalex.org/W4310007446","doi":"https://doi.org/10.1109/ictc55196.2022.9952906"},"language":"en","primary_location":{"id":"doi:10.1109/ictc55196.2022.9952906","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc55196.2022.9952906","pdf_url":null,"source":{"id":"https://openalex.org/S4363607740","display_name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","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 13th International Conference on Information and Communication Technology Convergence (ICTC)","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/A5027405733","display_name":"Hye Young An","orcid":"https://orcid.org/0000-0001-6195-026X"},"institutions":[{"id":"https://openalex.org/I163753206","display_name":"Chungbuk National University","ror":"https://ror.org/02wnxgj78","country_code":"KR","type":"education","lineage":["https://openalex.org/I163753206"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hye Young An","raw_affiliation_strings":["Chungbuk national university,Information and Communication Engineering,Chungju,Korea","Information and Communication Engineering, Chungbuk national university, Chungju, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chungbuk national university,Information and Communication Engineering,Chungju,Korea","institution_ids":["https://openalex.org/I163753206"]},{"raw_affiliation_string":"Information and Communication Engineering, Chungbuk national university, Chungju, Korea","institution_ids":["https://openalex.org/I163753206"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073051296","display_name":"Won Seok Choi","orcid":"https://orcid.org/0000-0002-4203-0457"},"institutions":[{"id":"https://openalex.org/I163753206","display_name":"Chungbuk National University","ror":"https://ror.org/02wnxgj78","country_code":"KR","type":"education","lineage":["https://openalex.org/I163753206"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Won Seok Choi","raw_affiliation_strings":["Chungbuk national university,Information and Communication Engineering,Chungju,Korea","Information and Communication Engineering, Chungbuk national university, Chungju, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chungbuk national university,Information and Communication Engineering,Chungju,Korea","institution_ids":["https://openalex.org/I163753206"]},{"raw_affiliation_string":"Information and Communication Engineering, Chungbuk national university, Chungju, Korea","institution_ids":["https://openalex.org/I163753206"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026721662","display_name":"Seong Gon Choi","orcid":"https://orcid.org/0000-0002-5326-8321"},"institutions":[{"id":"https://openalex.org/I163753206","display_name":"Chungbuk National University","ror":"https://ror.org/02wnxgj78","country_code":"KR","type":"education","lineage":["https://openalex.org/I163753206"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seong Gon Choi","raw_affiliation_strings":["Chungbuk national university,Information and Communication Engineering,Chungju,Korea","Information and Communication Engineering, Chungbuk national university, Chungju, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chungbuk national university,Information and Communication Engineering,Chungju,Korea","institution_ids":["https://openalex.org/I163753206"]},{"raw_affiliation_string":"Information and Communication Engineering, Chungbuk national university, Chungju, Korea","institution_ids":["https://openalex.org/I163753206"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I163753206"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.2304878,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1917","last_page":"1922"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9976999759674072,"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":0.7332360744476318},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.7082746028900146},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.6280516982078552},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5751662850379944},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5503831505775452},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4987528324127197},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4861257076263428},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4818176329135895},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4566236734390259},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4118935763835907},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36704206466674805},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34826040267944336}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7332360744476318},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.7082746028900146},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.6280516982078552},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5751662850379944},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5503831505775452},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4987528324127197},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4861257076263428},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4818176329135895},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4566236734390259},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4118935763835907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36704206466674805},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34826040267944336},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ictc55196.2022.9952906","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc55196.2022.9952906","pdf_url":null,"source":{"id":"https://openalex.org/S4363607740","display_name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","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 13th International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G1001985467","display_name":null,"funder_award_id":"2020R1A6A1A12047945","funder_id":"https://openalex.org/F4320311649","funder_display_name":"Ministry of Education"}],"funders":[{"id":"https://openalex.org/F4320311649","display_name":"Ministry of Education","ror":"https://ror.org/036nq5137"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2003404844","https://openalex.org/W2036785686","https://openalex.org/W2107761673","https://openalex.org/W2142470746","https://openalex.org/W2790949251","https://openalex.org/W2947207002","https://openalex.org/W2947215606","https://openalex.org/W2948001208","https://openalex.org/W2949285127","https://openalex.org/W2955949722","https://openalex.org/W3011122779","https://openalex.org/W3018747149"],"related_works":["https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2482165163","https://openalex.org/W120741642","https://openalex.org/W138569904","https://openalex.org/W3010890513","https://openalex.org/W2487298868","https://openalex.org/W4220726286"],"abstract_inverted_index":{"We":[0],"propose":[1,217],"a":[2,12,15,44,151,163,171,180,189,198,218,239,253,276,286],"data":[3,21,83,89,177,204,234,237,246],"preprocessing":[4,84],"method":[5,199],"for":[6,23,248],"estimating":[7],"the":[8,20,38,41,70,96,147,167,174,202,206,210,223,227,233,245,260,282],"travel":[9,35,57,148,283],"time":[10,36,58,149,153,284],"of":[11,43,86,170,179,182,205,263,281,285],"path":[13,45,49],"and":[14,32,62,75,106,120,123,230],"system":[16,219],"model":[17,164,220,243],"to":[18,40,55,72,110,145,200,226,268],"supplement":[19],"required":[22,247],"Artificial":[24],"Neural":[25],"Network":[26],"(ANN)":[27],"training.":[28,100],"Providing":[29],"an":[30,91],"accurate":[31,279],"reliable":[33],"estimated":[34],"from":[37,99],"origin":[39],"destination":[42],"aids":[46],"in":[47,94,139],"efficient":[48],"planning.":[50],"An":[51],"ANN":[52,207],"is":[53,143,193,250,265],"used":[54],"estimate":[56,146],"based":[59,78],"on":[60,79],"historical":[61],"real-time":[63],"traffic":[64,112],"data.":[65,81,113,213],"Machine":[66],"learning":[67,264,269],"algorithms":[68],"include":[69],"ability":[71],"find":[73],"patterns":[74],"infer":[76],"results":[77],"raw":[80,88],"The":[82,273],"capabilities":[85],"this":[87],"play":[90],"important":[92],"role":[93],"getting":[95],"best":[97],"outcomes":[98],"There":[101],"are":[102,134],"fixed":[103,115],"sensor-based":[104,108,116,125],"approaches":[105,109,117,126],"mobile":[107,124],"collecting":[111],"However,":[114,131],"have":[118],"cost":[119],"implementation":[121],"constraints,":[122],"require":[127],"numerous":[128],"probe":[129,132],"vehicles.":[130],"vehicles":[133],"not":[135],"everywhere.":[136],"In":[137,214],"addition,":[138,215],"urban":[140],"areas,":[141],"it":[142,255],"challenging":[144],"because":[150],"long":[152],"interval":[154],"can":[155,256],"pass":[156],"through":[157,238],"2\u20133":[158],"sections.":[159],"Therefore,":[160],"we":[161,196,216],"present":[162,197],"that":[165,221,259],"predicts":[166],"fractional":[168],"velocity":[169,212],"segment":[172],"using":[173],"last":[175],"three":[176],"points":[178,187],"set":[181],"Global":[183],"Positioning":[184],"System":[185],"(GPS)":[186],"within":[188],"segment.":[190,287],"If":[191],"there":[192],"insufficient":[194],"data,":[195],"fill":[201],"input":[203],"by":[208],"copying":[209],"previous":[211],"divides":[222],"training":[224,236,249],"according":[225],"standard":[228],"deviation":[229],"uses":[231],"all":[232,270],"as":[235],"K-fold":[240],"cross":[241],"validation":[242],"if":[244],"insufficient.":[251],"As":[252],"result,":[254],"be":[257],"seen":[258],"error":[261],"rate":[262],"reduced":[266],"compared":[267],"at":[271],"once.":[272],"outcome":[274],"enables":[275],"slightly":[277],"more":[278],"prediction":[280]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}