{"id":"https://openalex.org/W2990668842","doi":"https://doi.org/10.1109/itsc.2019.8917383","title":"Time Series Traffic Prediction via Hybrid Neural Networks","display_name":"Time Series Traffic Prediction via Hybrid Neural Networks","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2990668842","doi":"https://doi.org/10.1109/itsc.2019.8917383","mag":"2990668842"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2019.8917383","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2019.8917383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","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/A5065433030","display_name":"Shengjian Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shengjian Zhao","raw_affiliation_strings":["School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012540840","display_name":"Shu Lin","orcid":"https://orcid.org/0000-0003-1056-3926"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu Lin","raw_affiliation_strings":["School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016973102","display_name":"Jungang Xu","orcid":"https://orcid.org/0000-0002-3994-1401"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jungang Xu","raw_affiliation_strings":["School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065433030"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.7925,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.74708171,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1671","last_page":"1676"},"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/T10524","display_name":"Traffic control and management","score":0.9948999881744385,"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"}},{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9830999970436096,"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":0.816886305809021},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.6368194818496704},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5819025039672852},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5685304403305054},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5541424751281738},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5307690501213074},{"id":"https://openalex.org/keywords/salience","display_name":"Salience (neuroscience)","score":0.5255476236343384},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5102708339691162},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5057501792907715},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4996938705444336},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.4920806288719177},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.48806726932525635},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.43370482325553894},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08296090364456177}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.816886305809021},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.6368194818496704},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5819025039672852},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5685304403305054},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5541424751281738},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5307690501213074},{"id":"https://openalex.org/C108154423","wikidata":"https://www.wikidata.org/wiki/Q1469792","display_name":"Salience (neuroscience)","level":2,"score":0.5255476236343384},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5102708339691162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5057501792907715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4996938705444336},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.4920806288719177},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.48806726932525635},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.43370482325553894},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08296090364456177},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2019.8917383","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2019.8917383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","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":41,"referenced_works":["https://openalex.org/W626441390","https://openalex.org/W1803002526","https://openalex.org/W1811254738","https://openalex.org/W1924770834","https://openalex.org/W1947481528","https://openalex.org/W1973943669","https://openalex.org/W1996058270","https://openalex.org/W2014843617","https://openalex.org/W2030525099","https://openalex.org/W2036785686","https://openalex.org/W2066377449","https://openalex.org/W2077537883","https://openalex.org/W2101207830","https://openalex.org/W2108196201","https://openalex.org/W2110052353","https://openalex.org/W2132711183","https://openalex.org/W2139606794","https://openalex.org/W2142925364","https://openalex.org/W2145039203","https://openalex.org/W2159128662","https://openalex.org/W2160507653","https://openalex.org/W2171234954","https://openalex.org/W2528639018","https://openalex.org/W2530386080","https://openalex.org/W2579495707","https://openalex.org/W2624190409","https://openalex.org/W2741097030","https://openalex.org/W2788134583","https://openalex.org/W2802508687","https://openalex.org/W2907554505","https://openalex.org/W2962706528","https://openalex.org/W2963078493","https://openalex.org/W2963124587","https://openalex.org/W2963149042","https://openalex.org/W4289083414","https://openalex.org/W6619978402","https://openalex.org/W6638742206","https://openalex.org/W6640212811","https://openalex.org/W6640617836","https://openalex.org/W6680930200","https://openalex.org/W6685670348"],"related_works":["https://openalex.org/W4384112194","https://openalex.org/W2783354812","https://openalex.org/W2103009189","https://openalex.org/W4312958259","https://openalex.org/W4390813131","https://openalex.org/W2349383066","https://openalex.org/W4308259661","https://openalex.org/W4328132048","https://openalex.org/W1969901537","https://openalex.org/W2376202349"],"abstract_inverted_index":{"Traffic":[0],"time":[1,29,91,142,206],"series":[2,30,92,207],"prediction":[3,168,208],"is":[4,61,95,139,198],"becoming":[5],"increasingly":[6],"important":[7],"in":[8,56,106,179],"many":[9,49,201],"real":[10],"applications,":[11],"ranging":[12],"from":[13,119],"resource":[14],"scheduling":[15],"of":[16,20,35,54,193,204],"company,":[17],"make":[18],"decisions":[19],"government,":[21],"abnormal":[22],"data":[23,55,97,122],"detection":[24],"and":[25,68,99,123,149],"so":[26,59],"on.":[27],"History":[28],"describes":[31],"the":[32,48,52,65,90,135,140,167,176],"overall":[33],"variation":[34],"data,":[36],"such":[37],"as":[38],"upward,":[39],"downward":[40],"or":[41],"steady":[42],"trend.":[43],"In":[44,77,170],"reality,":[45],"due":[46],"to":[47,63,73,88,114,130,143,152,165],"factors":[50,67],"affecting":[51],"value":[53],"traffic":[57,147,172,205],"fields,":[58],"it":[60,138,151],"difficult":[62],"consider":[64],"comprehensive":[66],"use":[69,81,150],"an":[70],"appropriate":[71],"model":[72],"correctly":[74],"represent":[75],"it.":[76],"this":[78,159,191],"paper,":[79],"We":[80],"a":[82],"novel":[83],"hybrid":[84,194],"network":[85],"called":[86],"TreNet":[87,94,145,174],"predict":[89,153],"value.":[93,155],"target":[96],"drive":[98],"does":[100],"not":[101],"require":[102],"any":[103],"prior":[104],"knowledge":[105],"practice.":[107],"It":[108],"uses":[109,124],"convolutional":[110],"neural":[111,195],"networks":[112,128,196],"(CNN)":[113],"extract":[115],"local":[116],"salience":[117],"features":[118],"adjacent":[120],"raw":[121],"long-short":[125],"term":[126],"memory":[127],"(LSTM)":[129],"capture":[131,162],"long-range":[132],"memory.":[133],"On":[134],"other":[136,184],"hand,":[137],"first":[141],"apply":[144],"on":[146],"field":[148],"future":[154],"Experiments":[156],"demonstrate":[157],"that":[158,190],"method":[160],"can":[161],"useful":[163],"information":[164],"enhance":[166],"performance.":[169],"seven":[171],"datasets,":[173],"achieves":[175],"best":[177],"performance":[178],"six":[180],"datasets":[181],"compared":[182],"with":[183],"methods.":[185],"The":[186],"experimental":[187],"results":[188],"show":[189],"kind":[192],"architecture":[197],"suitable":[199],"for":[200],"different":[202],"types":[203],"problems.":[209]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
