{"id":"https://openalex.org/W3201364331","doi":"https://doi.org/10.1080/15472450.2021.1977639","title":"A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction","display_name":"A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction","publication_year":2021,"publication_date":"2021-09-21","ids":{"openalex":"https://openalex.org/W3201364331","doi":"https://doi.org/10.1080/15472450.2021.1977639","mag":"3201364331"},"language":"en","primary_location":{"id":"doi:10.1080/15472450.2021.1977639","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2021.1977639","pdf_url":null,"source":{"id":"https://openalex.org/S172631016","display_name":"Journal of Intelligent Transportation Systems","issn_l":"1547-2442","issn":["1547-2442","1547-2450"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent Transportation Systems","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/A5031123093","display_name":"Xuecai Xu","orcid":"https://orcid.org/0000-0001-5798-8441"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuecai Xu","raw_affiliation_strings":["School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China;","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035711096","display_name":"Xiaofei Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101015","display_name":"Wuhan Municipal Engineering Design & Research Institute","ror":"https://ror.org/00ycq1k13","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210101015"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofei Jin","raw_affiliation_strings":["Shanghai Municipal Engineering Design Institute (Group) Co., Ltd. (Wuhan Branch), Wuhan, China;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Municipal Engineering Design Institute (Group) Co., Ltd. (Wuhan Branch), Wuhan, China;","institution_ids":["https://openalex.org/I4210101015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023875826","display_name":"Daiquan Xiao","orcid":"https://orcid.org/0000-0002-5481-2819"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daiquan Xiao","raw_affiliation_strings":["School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China;","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101866775","display_name":"Changxi Ma","orcid":"https://orcid.org/0000-0002-0250-5462"},"institutions":[{"id":"https://openalex.org/I3133134087","display_name":"Lanzhou Jiaotong University","ror":"https://ror.org/03144pv92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133134087"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Changxi Ma","raw_affiliation_strings":["School of Traffic and Transportation Engineering, Lanzhou Jiaotong University, Lanzhou, China;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation Engineering, Lanzhou Jiaotong University, Lanzhou, China;","institution_ids":["https://openalex.org/I3133134087"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029977089","display_name":"S.C. Wong","orcid":"https://orcid.org/0000-0003-1169-7045"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"S. C. Wong","raw_affiliation_strings":["Department of Transportation Engineering, University of Hong Kong Pokfulam Road, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Transportation Engineering, University of Hong Kong Pokfulam Road, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101866775"],"corresponding_institution_ids":["https://openalex.org/I3133134087"],"apc_list":null,"apc_paid":null,"fwci":4.2205,"has_fulltext":false,"cited_by_count":64,"citation_normalized_percentile":{"value":0.94595434,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"27","issue":"1","first_page":"1","last_page":"18"},"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":0.9977999925613403,"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":0.9973999857902527,"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/autoregressive-fractionally-integrated-moving-average","display_name":"Autoregressive fractionally integrated moving average","score":0.8083096742630005},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.7615920305252075},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.715757429599762},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.7156988382339478},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5893529057502747},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5234888792037964},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.45007210969924927},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.44279390573501587},{"id":"https://openalex.org/keywords/moving-average","display_name":"Moving average","score":0.4246557056903839},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4135150909423828},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.37766551971435547},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.33229517936706543},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2680233120918274},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.2541763484477997},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2299848198890686},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20682191848754883},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.16421294212341309}],"concepts":[{"id":"https://openalex.org/C1297061","wikidata":"https://www.wikidata.org/wiki/Q4826945","display_name":"Autoregressive fractionally integrated moving average","level":4,"score":0.8083096742630005},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.7615920305252075},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.715757429599762},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.7156988382339478},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5893529057502747},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5234888792037964},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.45007210969924927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44279390573501587},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.4246557056903839},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4135150909423828},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.37766551971435547},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.33229517936706543},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2680233120918274},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2541763484477997},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2299848198890686},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20682191848754883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16421294212341309},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.0},{"id":"https://openalex.org/C2986394398","wikidata":"https://www.wikidata.org/wiki/Q1805477","display_name":"Long memory","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/15472450.2021.1977639","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2021.1977639","pdf_url":null,"source":{"id":"https://openalex.org/S172631016","display_name":"Journal of Intelligent Transportation Systems","issn_l":"1547-2442","issn":["1547-2442","1547-2450"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G111462165","display_name":null,"funder_award_id":"52062027","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8569300321","display_name":"\u57fa\u4e8e\u4e8b\u6545\u5206\u6790\u4e0e\u98ce\u9669\u5ea6\u91cf\u7684\u5371\u9669\u8d27\u7269\u8fd0\u8f93\u8f66\u961f\u9c81\u68d2\u8c03\u5ea6\u65b9\u6cd5\u7814\u7a76","funder_award_id":"71861023","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":55,"referenced_works":["https://openalex.org/W39592964","https://openalex.org/W1875626450","https://openalex.org/W1964725609","https://openalex.org/W1964965419","https://openalex.org/W1967681834","https://openalex.org/W1975362087","https://openalex.org/W1982978808","https://openalex.org/W1984969638","https://openalex.org/W2001612902","https://openalex.org/W2002044606","https://openalex.org/W2004353783","https://openalex.org/W2005436527","https://openalex.org/W2024369567","https://openalex.org/W2028259931","https://openalex.org/W2029050814","https://openalex.org/W2036848256","https://openalex.org/W2039211048","https://openalex.org/W2040297119","https://openalex.org/W2042859557","https://openalex.org/W2049952439","https://openalex.org/W2059128538","https://openalex.org/W2069472645","https://openalex.org/W2070321592","https://openalex.org/W2071291375","https://openalex.org/W2082533141","https://openalex.org/W2083238230","https://openalex.org/W2090192376","https://openalex.org/W2097498150","https://openalex.org/W2131739422","https://openalex.org/W2131767615","https://openalex.org/W2131819535","https://openalex.org/W2137393925","https://openalex.org/W2139606794","https://openalex.org/W2150152686","https://openalex.org/W2339042578","https://openalex.org/W2516321972","https://openalex.org/W2521560026","https://openalex.org/W2551136249","https://openalex.org/W2593182953","https://openalex.org/W2734454202","https://openalex.org/W2769010373","https://openalex.org/W2772202358","https://openalex.org/W2789788750","https://openalex.org/W2799109291","https://openalex.org/W2891280833","https://openalex.org/W2892302657","https://openalex.org/W2898628748","https://openalex.org/W2900207796","https://openalex.org/W2916664939","https://openalex.org/W2918600339","https://openalex.org/W2939224083","https://openalex.org/W2955819484","https://openalex.org/W2972461593","https://openalex.org/W4237748447","https://openalex.org/W4253708194"],"related_works":["https://openalex.org/W3190289737","https://openalex.org/W2604942034","https://openalex.org/W3115491726","https://openalex.org/W4400988174","https://openalex.org/W2073847544","https://openalex.org/W2567143433","https://openalex.org/W2620501840","https://openalex.org/W2085237988","https://openalex.org/W3032070712","https://openalex.org/W2518538438"],"abstract_inverted_index":{"Intelligent":[0],"traffic":[1,13,25,33,49,77,116,170,198,224],"control":[2,34],"and":[3,18,35,43,67,91,118,146,200,230],"guidance":[4,36],"system":[5],"is":[6,28,208],"an":[7,219],"effective":[8],"way":[9],"to":[10,74,110,140],"solve":[11],"urban":[12],"congestion,":[14],"improve":[15],"road":[16],"capacity":[17],"guarantee":[19],"drivers'":[20],"travel":[21],"safety,":[22],"while":[23],"short-term":[24,197,223],"flow":[26,50,155,171,225],"prediction":[27,202,226],"the":[29,40,44,85,97,105,112,119,133,142,147,153,157,168,178,186,190,196,201,205,213,222],"core":[30],"of":[31,47,88,100,115,125,156,204,212],"intelligent":[32],"system.":[37],"To":[38],"investigate":[39],"long-term":[41,86,191],"memory":[42,87,192],"dynamic":[45,98],"feature":[46,99],"short-time":[48,76],"time":[51],"series,":[52],"a":[53],"hybrid":[54,158,162,206],"model":[55,66,73,82,107,137,163,188,207],"was":[56,108,138,164],"proposed":[57,161],"by":[58,166],"integrating":[59],"autoregressive":[60,69,126],"fractionally":[61],"integrated":[62,127],"moving":[63,128],"average":[64,129],"(ARFIMA)":[65],"nonlinear":[68,101,143],"(NAR)":[70],"neural":[71,93,135],"network":[72,94,136],"predict":[75,111,195],"flow,":[78,117,199],"in":[79,173],"which":[80],"ARFIMA":[81,106,187],"can":[83,95,193],"address":[84],"linear":[89,113],"component":[90,114],"NAR":[92,134],"accommodate":[96],"residual":[102,144],"component.":[103],"First,":[104],"employed":[109],"results":[120,149,183],"were":[121,150],"compared":[122],"with":[123,227],"those":[124],"(ARIMA)":[130],"model.":[131,159],"Next,":[132],"adopted":[139],"forecast":[141],"components,":[145],"weighted":[148],"considered":[151],"as":[152],"predicted":[154],"The":[160,182,216],"validated":[165],"using":[167],"cross-sectional":[169],"data":[172],"California":[174],"freeways":[175],"obtained":[176],"from":[177],"open-access":[179],"PeMS":[180],"database.":[181],"showed":[184],"that":[185,211],"considering":[189],"effectively":[194],"accuracy":[203],"better":[209],"than":[210],"singular":[214],"models.":[215],"findings":[217],"provide":[218],"alternative":[220],"for":[221],"lower":[228],"error":[229],"higher":[231],"accuracy.":[232]},"counts_by_year":[{"year":2026,"cited_by_count":11},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}