{"id":"https://openalex.org/W3033172001","doi":"https://doi.org/10.1109/tvt.2020.2999358","title":"Freeway Travel Time Prediction Using Deep Hybrid Model \u2013 Taking Sun Yat-Sen Freeway as an Example","display_name":"Freeway Travel Time Prediction Using Deep Hybrid Model \u2013 Taking Sun Yat-Sen Freeway as an Example","publication_year":2020,"publication_date":"2020-06-02","ids":{"openalex":"https://openalex.org/W3033172001","doi":"https://doi.org/10.1109/tvt.2020.2999358","mag":"3033172001"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2020.2999358","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2020.2999358","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","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/A5084685060","display_name":"Pei-Ya Ting","orcid":null},"institutions":[{"id":"https://openalex.org/I4210120917","display_name":"Taiwan Semiconductor Manufacturing Company (Taiwan)","ror":"https://ror.org/02wx79d08","country_code":"TW","type":"company","lineage":["https://openalex.org/I4210120917"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Pei-Ya Ting","raw_affiliation_strings":["Taiwan Semiconductor Manufacturing Company, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taiwan Semiconductor Manufacturing Company, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I4210120917"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078279410","display_name":"Tomotaka Wada","orcid":"https://orcid.org/0000-0002-3187-5411"},"institutions":[{"id":"https://openalex.org/I56624758","display_name":"Kansai University","ror":"https://ror.org/03xg1f311","country_code":"JP","type":"education","lineage":["https://openalex.org/I56624758"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomotaka Wada","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Kansai University, Osaka, Japan"],"raw_orcid":"https://orcid.org/0000-0002-3187-5411","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Kansai University, Osaka, Japan","institution_ids":["https://openalex.org/I56624758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030301296","display_name":"Yi-Lun Chiu","orcid":"https://orcid.org/0000-0002-2821-2334"},"institutions":[{"id":"https://openalex.org/I22265921","display_name":"National Central University","ror":"https://ror.org/00944ve71","country_code":"TW","type":"education","lineage":["https://openalex.org/I22265921"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yi-Lun Chiu","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-2821-2334","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I22265921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060250892","display_name":"Min-Te Sun","orcid":"https://orcid.org/0000-0002-8911-3831"},"institutions":[{"id":"https://openalex.org/I22265921","display_name":"National Central University","ror":"https://ror.org/00944ve71","country_code":"TW","type":"education","lineage":["https://openalex.org/I22265921"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Min-Te Sun","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-8911-3831","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I22265921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102709220","display_name":"Kazuya Sakai","orcid":"https://orcid.org/0000-0003-3929-2533"},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuya Sakai","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Tokyo Metropolitan University, Hino, Japan"],"raw_orcid":"https://orcid.org/0000-0003-3929-2533","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Tokyo Metropolitan University, Hino, Japan","institution_ids":["https://openalex.org/I69740276"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001457193","display_name":"Wei\u2010Shinn Ku","orcid":"https://orcid.org/0000-0001-8636-4689"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei-Shinn Ku","raw_affiliation_strings":["Department of Computer Science and Software Engineering, Auburn University, Auburn, USA"],"raw_orcid":"https://orcid.org/0000-0001-8636-4689","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Software Engineering, Auburn University, Auburn, USA","institution_ids":["https://openalex.org/I82497590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111892831","display_name":"Andy An-Kai Jeng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210148468","display_name":"Industrial Technology Research Institute","ror":"https://ror.org/05szzwt63","country_code":"TW","type":"nonprofit","lineage":["https://openalex.org/I4210148468"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Andy An-Kai Jeng","raw_affiliation_strings":["Industrial Technology Research Institute, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Industrial Technology Research Institute, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I4210148468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111634434","display_name":"Jing-Shyang Hwu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210148468","display_name":"Industrial Technology Research Institute","ror":"https://ror.org/05szzwt63","country_code":"TW","type":"nonprofit","lineage":["https://openalex.org/I4210148468"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jing-Shyang Hwu","raw_affiliation_strings":["Industrial Technology Research Institute, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Industrial Technology Research Institute, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I4210148468"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.611,"has_fulltext":false,"cited_by_count":43,"citation_normalized_percentile":{"value":0.88637255,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"69","issue":"8","first_page":"8257","last_page":"8266"},"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":0.9979000091552734,"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/T10524","display_name":"Traffic control and management","score":0.9958999752998352,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6848445534706116},{"id":"https://openalex.org/keywords/travel-time","display_name":"Travel time","score":0.5771190524101257},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5415544509887695},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5322743654251099},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5288732051849365},{"id":"https://openalex.org/keywords/toll","display_name":"Toll","score":0.4909954369068146},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.48188501596450806},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4798114597797394},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.46019983291625977},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.45638635754585266},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45307374000549316},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.4206105172634125},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.38210564851760864},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3383907079696655},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3359678089618683},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2818044424057007}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6848445534706116},{"id":"https://openalex.org/C2985733770","wikidata":"https://www.wikidata.org/wiki/Q1233007","display_name":"Travel time","level":2,"score":0.5771190524101257},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5415544509887695},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5322743654251099},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5288732051849365},{"id":"https://openalex.org/C2778025104","wikidata":"https://www.wikidata.org/wiki/Q408004","display_name":"Toll","level":2,"score":0.4909954369068146},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.48188501596450806},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4798114597797394},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.46019983291625977},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.45638635754585266},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45307374000549316},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.4206105172634125},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.38210564851760864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3383907079696655},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3359678089618683},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2818044424057007},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2020.2999358","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2020.2999358","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W626441390","https://openalex.org/W793757263","https://openalex.org/W2011636111","https://openalex.org/W2052848632","https://openalex.org/W2064675550","https://openalex.org/W2069929199","https://openalex.org/W2074108366","https://openalex.org/W2082280406","https://openalex.org/W2112364454","https://openalex.org/W2144623333","https://openalex.org/W2145094598","https://openalex.org/W2152196380","https://openalex.org/W2168332608","https://openalex.org/W2479611522","https://openalex.org/W2598842688","https://openalex.org/W2605113527","https://openalex.org/W2793547515","https://openalex.org/W2914060790","https://openalex.org/W2964199361","https://openalex.org/W2997574889","https://openalex.org/W6619978402","https://openalex.org/W6681096077","https://openalex.org/W6735809762","https://openalex.org/W6736792253"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W576156795","https://openalex.org/W65202228","https://openalex.org/W2359940423","https://openalex.org/W2053810344","https://openalex.org/W3023977068","https://openalex.org/W1989219277","https://openalex.org/W2342057333","https://openalex.org/W4292790983","https://openalex.org/W4207028894"],"abstract_inverted_index":{"As":[0],"the":[1,63,67,72,79,88,92,113,119,125,129,133,146,151,155,160,171,188,194,216],"population":[2],"keeps":[3],"growing,":[4],"traffic":[5,38,64,212],"congestion":[6],"happens":[7],"more":[8,10,32],"and":[9,34,71,85,108,154,162,228],"often.":[11],"Consequently,":[12],"travel":[13,24,47,73,126,172,190],"time":[14,25,48,74,106,127,173,191],"has":[15],"become":[16],"an":[17],"important":[18],"indicator":[19],"of":[20,66,91,193,225],"driving":[21],"experience.":[22],"Accurate":[23],"information":[26],"helps":[27],"drivers":[28],"plan":[29],"their":[30],"route":[31],"wisely":[33],"thus":[35],"effectively":[36,117],"alleviate":[37],"congestion.":[39],"In":[40],"this":[41,58],"research,":[42],"we":[43],"propose":[44],"a":[45,135,204],"vehicle":[46,130],"prediction":[49,75,114,192,226],"model":[50,157],"for":[51,78,128,175],"freeway":[52],"traffic.":[53],"The":[54,99],"data":[55,94,100,213],"used":[56,110],"in":[57,141,223],"research":[59],"are":[60,101,109,183],"derived":[61],"from":[62],"dataset":[65],"Taiwan":[68],"Freeway":[69,82],"Bureau,":[70],"is":[76,95,139],"made":[77],"Sun":[80],"Yat-sen":[81],"between":[83],"Taipei":[84],"Hsinchu.":[86],"First,":[87],"missing":[89],"value":[90],"raw":[93],"imputed":[96],"by":[97,200],"Autoencoder.":[98],"then":[102],"segmented":[103],"according":[104],"to":[105,111,123],"series":[107],"build":[112],"model.":[115],"To":[116,167],"capture":[118],"hidden":[120],"features":[121],"required":[122],"predict":[124],"traveling":[131],"on":[132,210],"freeway,":[134],"deep":[136],"learning":[137],"architecture":[138],"adopted":[140],"our":[142],"system,":[143],"which":[144],"includes":[145],"GRU":[147,161],"neural":[148],"network":[149],"model,":[150,153],"XGBoost":[152,163],"Hybrid":[156],"that":[158,187,215],"combines":[159],"through":[164],"linear":[165],"regression.":[166],"increase":[168],"computational":[169],"efficiency,":[170],"predictions":[174],"consecutive":[176],"toll":[177],"gates":[178],"every":[179],"5":[180],"minutes":[181],"apart":[182],"pre-computed":[184],"offline,":[185],"so":[186],"online":[189],"whole":[195],"trip":[196],"can":[197,219],"be":[198],"obtained":[199],"simply":[201],"summing":[202],"up":[203],"few":[205],"numbers.":[206],"Experimental":[207],"results":[208],"based":[209],"actual":[211],"show":[214],"proposed":[217],"system":[218],"achieve":[220],"good":[221],"performance":[222],"terms":[224],"accuracy":[227],"execution":[229],"time.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
