{"id":"https://openalex.org/W2898628748","doi":"https://doi.org/10.1080/15472450.2018.1493929","title":"A framework for short-term traffic flow forecasting using the combination of wavelet transformation and artificial neural networks","display_name":"A framework for short-term traffic flow forecasting using the combination of wavelet transformation and artificial neural networks","publication_year":2018,"publication_date":"2018-11-02","ids":{"openalex":"https://openalex.org/W2898628748","doi":"https://doi.org/10.1080/15472450.2018.1493929","mag":"2898628748"},"language":"en","primary_location":{"id":"doi:10.1080/15472450.2018.1493929","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2018.1493929","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/A5107995749","display_name":"Seyed Omid Mousavizadeh Kashi","orcid":"https://orcid.org/0000-0002-2651-3571"},"institutions":[{"id":"https://openalex.org/I170013655","display_name":"Isfahan University of Technology","ror":"https://ror.org/00af3sa43","country_code":"IR","type":"education","lineage":["https://openalex.org/I170013655"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Seyed Omid Mousavizadeh Kashi","raw_affiliation_strings":["Department of Transportation Engineering, Isfahan University of Technology, Isfahan, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Transportation Engineering, Isfahan University of Technology, Isfahan, Iran","institution_ids":["https://openalex.org/I170013655"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031347852","display_name":"Meisam Akbarzadeh","orcid":"https://orcid.org/0000-0001-8703-6791"},"institutions":[{"id":"https://openalex.org/I170013655","display_name":"Isfahan University of Technology","ror":"https://ror.org/00af3sa43","country_code":"IR","type":"education","lineage":["https://openalex.org/I170013655"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Meisam Akbarzadeh","raw_affiliation_strings":["Department of Transportation Engineering, Isfahan University of Technology, Isfahan, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Transportation Engineering, Isfahan University of Technology, Isfahan, Iran","institution_ids":["https://openalex.org/I170013655"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5031347852"],"corresponding_institution_ids":["https://openalex.org/I170013655"],"apc_list":null,"apc_paid":null,"fwci":2.6371,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.89098924,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"23","issue":"1","first_page":"60","last_page":"71"},"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":0.998199999332428,"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":0.9955999851226807,"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/transformation","display_name":"Transformation (genetics)","score":0.7301615476608276},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.7074645161628723},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6944696307182312},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.6754510998725891},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.6301605105400085},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5856645107269287},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.5630300045013428},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5114861726760864},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5010664463043213},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4501141309738159},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4432770311832428},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.41934049129486084},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4147370457649231},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3883230686187744},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37184762954711914},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.31160441040992737},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28686094284057617},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19807735085487366}],"concepts":[{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.7301615476608276},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.7074645161628723},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6944696307182312},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6754510998725891},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.6301605105400085},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5856645107269287},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.5630300045013428},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5114861726760864},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5010664463043213},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4501141309738159},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4432770311832428},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.41934049129486084},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4147370457649231},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3883230686187744},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37184762954711914},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.31160441040992737},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28686094284057617},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19807735085487366},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/15472450.2018.1493929","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2018.1493929","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W43001522","https://openalex.org/W228380312","https://openalex.org/W594114979","https://openalex.org/W952236061","https://openalex.org/W1544613517","https://openalex.org/W1551942241","https://openalex.org/W1875626450","https://openalex.org/W1967051094","https://openalex.org/W1970460434","https://openalex.org/W1972340269","https://openalex.org/W1976027313","https://openalex.org/W1977405947","https://openalex.org/W1988420953","https://openalex.org/W1991410369","https://openalex.org/W2002033255","https://openalex.org/W2003812729","https://openalex.org/W2007610809","https://openalex.org/W2013758377","https://openalex.org/W2032131451","https://openalex.org/W2036848256","https://openalex.org/W2039211048","https://openalex.org/W2040297119","https://openalex.org/W2041382447","https://openalex.org/W2061062671","https://openalex.org/W2061358509","https://openalex.org/W2064064283","https://openalex.org/W2074991364","https://openalex.org/W2079662306","https://openalex.org/W2083589559","https://openalex.org/W2083612784","https://openalex.org/W2085740461","https://openalex.org/W2086686542","https://openalex.org/W2090717427","https://openalex.org/W2109563136","https://openalex.org/W2111807801","https://openalex.org/W2111991989","https://openalex.org/W2131767615","https://openalex.org/W2144830515","https://openalex.org/W2163535990","https://openalex.org/W2166662814","https://openalex.org/W2265497615","https://openalex.org/W2339042578","https://openalex.org/W2347115965","https://openalex.org/W2963420343","https://openalex.org/W4238609481","https://openalex.org/W4243821471","https://openalex.org/W4255272544"],"related_works":["https://openalex.org/W3184559891","https://openalex.org/W1977489332","https://openalex.org/W2297152540","https://openalex.org/W3013645199","https://openalex.org/W2106583216","https://openalex.org/W4381189085","https://openalex.org/W3182459386","https://openalex.org/W2042102941","https://openalex.org/W2379949015","https://openalex.org/W3045028796"],"abstract_inverted_index":{"The":[0,138,185,209,215],"main":[1],"objective":[2],"of":[3,55,60,76,89,117,135,159,178,221,237],"this":[4],"paper":[5],"is":[6,70,98,105,141],"to":[7,21,28,72,107,145,181,189],"develop":[8],"a":[9,182,202,206],"framework":[10],"for":[11,149,253,263],"short-term":[12],"traffic":[13,77,157],"flow":[14,22,57,78,110,119,192,220,249],"forecasting":[15],"models":[16],"with":[17,229,241,257,267],"high":[18],"accuracy.":[19],"Due":[20],"oscillations,":[23],"the":[24,29,41,43,46,49,56,74,87,100,109,114,118,132,136,155,172,176,179,191,198,219,226,248,264],"real-time":[25],"information":[26],"presented":[27],"drivers":[30],"through":[31],"variable":[32],"message":[33],"signs,":[34],"etc.,":[35],"may":[36],"not":[37,52],"be":[38],"valid":[39],"by":[40],"time":[42],"driver":[44],"reaches":[45],"location.":[47],"On":[48],"other":[50],"hand,":[51],"all":[53],"compartments":[54],"signal":[58],"are":[59,123],"same":[61,199,227],"importance":[62],"in":[63,79,193,205],"determining":[64],"its":[65],"future":[66,81,133,151],"state.":[67],"A":[68],"model":[69,97,186,210,217],"developed":[71],"predict":[73,190],"value":[75],"near":[80],"(next":[82],"5\u201335":[83],"minutes)":[84],"based":[85],"on":[86,131,197,225,250],"combination":[88],"wavelet":[90],"transformation":[91,104],"and":[92,143,201,236,262],"artificial":[93],"neural":[94,139],"networks.":[95],"This":[96],"called":[99],"hybrid":[101],"WT-ANN.":[102],"Wavelet":[103],"set":[106,142],"denoise":[108],"signal,":[111],"i.e.,":[112],"filtering":[113],"unimportant":[115],"fluctuations":[116,122],"signal.":[120,137],"Unimportant":[121],"those":[124],"that":[125,170],"have":[126],"little":[127],"or":[128],"no":[129],"effect":[130],"condition":[134],"network":[140],"trained":[144],"use":[146],"previous":[147],"data":[148,158],"predicting":[150],"flow.":[152],"To":[153],"implement":[154],"system,":[156],"US-101":[160],"were":[161],"used":[162,188],"from":[163],"Next":[164],"Generation":[165],"Simulation":[166],"(NGSIM).":[167],"Results":[168],"show":[169],"removing":[171],"noises":[173],"has":[174],"improved":[175],"accuracy":[177],"prediction":[180],"great":[183],"extent.":[184],"was":[187],"three":[194],"different":[195,203,207,265],"locations":[196,252],"highway":[200,204,266],"country.":[208],"rendered":[211],"highly":[212],"reliable":[213],"predictions.":[214],"proposed":[216],"predicts":[218,247],"next":[222,238,254],"5":[223,255],"min":[224,240,256],"location":[228],"2.5%":[230],"Mean":[231],"Absolute":[232],"Percentage":[233],"Error":[234],"(MAPE)":[235],"35":[239],"less":[242,258],"than":[243,259],"12%":[244],"MAPE.":[245,269],"It":[246],"downstream":[251],"8%":[260],"MAPE":[261],"2.3%":[268]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}