{"id":"https://openalex.org/W766643194","doi":null,"title":"THE USE OF NEURAL NETWORKS AND TIME SERIES MODELS FOR SHORT TERM TRAFFIC FORECASTING: A COMPARATIVE STUDY","display_name":"THE USE OF NEURAL NETWORKS AND TIME SERIES MODELS FOR SHORT TERM TRAFFIC FORECASTING: A COMPARATIVE STUDY","publication_year":1993,"publication_date":"1993-01-01","ids":{"openalex":"https://openalex.org/W766643194","mag":"766643194"},"language":"en","primary_location":{"id":"mag:766643194","doi":null,"is_oa":false,"landing_page_url":"https://trid.trb.org/view/390622","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5055071100","display_name":"Stuart Clark","orcid":"https://orcid.org/0000-0002-1698-1910","observed_orcids":["https://orcid.org/0000-0002-1698-1910"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stuart Clark","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-1698-1910","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111755872","display_name":"Mark S. Dougherty","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mark Dougherty","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5108435510","display_name":"H R Kirby","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"H R Kirby","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.4069,"has_fulltext":false,"cited_by_count":49,"citation_normalized_percentile":{"value":0.96406628,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","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/T11918","display_name":"Forecasting Techniques and Applications","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.0003000000142492354,"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/short-term-traffic-forecasting","display_name":"short-term traffic forecasting","score":0.9340000152587891},{"id":"https://openalex.org/keywords/traffic-flow-prediction","display_name":"traffic flow prediction","score":0.8410000205039978},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.9700000286102295},{"id":"https://openalex.org/keywords/time-series-models","display_name":"time series models","score":0.843999981880188},{"id":"https://openalex.org/keywords/arima","display_name":"ARIMA","score":0.9129999876022339},{"id":"https://openalex.org/keywords/transfer-function","display_name":"transfer function","score":0.9210000038146973},{"id":"https://openalex.org/keywords/linear-regression","display_name":"linear regression","score":0.8029999732971191},{"id":"https://openalex.org/keywords/urban-traffic","display_name":"urban traffic","score":0.4650000035762787},{"id":"https://openalex.org/keywords/model-comparison","display_name":"model comparison","score":0.5669999718666077}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.8383508920669556},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6129682660102844},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5943353772163391},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.590779185295105},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.5813759565353394},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.554424524307251},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5373533964157104},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.48233816027641296},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4725213646888733},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.47038140892982483},{"id":"https://openalex.org/C82257358","wikidata":"https://www.wikidata.org/wiki/Q895497","display_name":"Box\u2013Jenkins","level":4,"score":0.4496333599090576},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4252723455429077},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4213216304779053},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.38433632254600525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28355643153190613},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.269534170627594},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2415858507156372},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14230391383171082},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:766643194","doi":null,"is_oa":false,"landing_page_url":"https://trid.trb.org/view/390622","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7250000238418579,"id":"https://metadata.un.org/sdg/11"},{"display_name":"Industry, innovation and infrastructure","score":0.4943999946117401,"id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2175683139","https://openalex.org/W2340405486","https://openalex.org/W2614836847","https://openalex.org/W2388354524","https://openalex.org/W4401706551","https://openalex.org/W2368743394","https://openalex.org/W2896507969","https://openalex.org/W2890731373","https://openalex.org/W637664645","https://openalex.org/W4285325680"],"abstract_inverted_index":{"Short":[0],"term":[1],"forecasts":[2],"of":[3,71,80,84,91,97,128,190,202,208,210,220,344,366],"traffic":[4,300],"flow":[5,140,145,183,192,262,303],"at":[6,141,146,193,291],"a":[7,82,89,118,126,142,187,248,335,345],"site":[8,119,143,196,306],"may":[9,301],"need":[10,47],"to":[11,36,48,73,86,109,259,307,355],"be":[12,25,34,65,74,353],"based":[13],"on":[14],"inputs":[15],"from":[16,41,93,304],"several":[17],"other":[18,327],"sites":[19,148,246,278],"and":[20,120,124,144,177,197,205,215,325,357],"times.":[21],"Neural":[22],"networks":[23],"can":[24,33,63,352],"readily":[26],"used":[27,133,235],"for":[28,88,102,117,125,243,275],"this":[29],"purpose,":[30],"because":[31],"they":[32],"trained":[35],"describe":[37],"the":[38,42,54,69,94,107,139,150,161,182,191,194,206,211,228,245,252,260,265,276,310,315,330,342,363,367],"output":[39],"levels":[40],"input":[43],"data,":[44],"without":[45],"any":[46],"specify":[49],"in":[50,149,160,251,295,321],"advance":[51],"what":[52],"form":[53,70],"relationships":[55],"should":[56],"take.":[57],"Conventional":[58],"time":[59,152,164,188],"series":[60],"analysis":[61],"techniques":[62,170],"also":[64],"used,":[66],"but":[67,254,264],"require":[68],"model":[72],"pre-specified.":[75],"The":[76,130,200,231],"paper":[77],"reports":[78],"results":[79],"fitting":[81],"range":[83],"models":[85,132,212,225,234,340,351],"data":[87,101,241,274],"number":[90],"links":[92],"instrumented":[95],"city":[96],"Leicester,":[98],"using":[99],"SCOOT":[100],"5-minute":[103,312],"intervals":[104],"extracted":[105],"via":[106],"link":[108],"Nottingham":[110],"University.":[111],"Two":[112],"test":[113],"situations":[114],"were":[115,134,166,323],"investigated;":[116],"its":[121,198],"adjoining":[122,147,277],"links,":[123],"chain":[127],"links.":[129],"statistical":[131,222],"ordinary":[135],"linear":[136,155],"regression":[137,156],"(between":[138],"previous":[151,162],"period);":[153],"discounted":[154],"(in":[157,180],"which":[158,181],"flows":[159],"n":[163],"periods":[165],"included);":[167],"classical":[168],"Box-Jenkins":[169],"(auto-regressive":[171],"integrated":[172],"moving":[173],"average,":[174],"or":[175],"ARIMA);":[176],"transfer":[178],"functions":[179],"is":[184,283],"modelled":[185],"as":[186,279,285,287],"function":[189],"prediction":[195],"neighbour).":[199],"treatment":[201],"missing":[203],"values":[204],"goodness":[207],"fit":[209,266],"are":[213,236],"described;":[214],"it":[216,288],"was":[217,268,317],"concluded":[218],"that,":[219],"these":[221],"techniques,":[223],"ARIMA":[224,339],"generally":[226],"gave":[227],"better":[229],"performance.":[230],"neural":[232,349],"network":[233,350],"briefly":[237],"summarised.":[238],"Using":[239],"historic":[240],"alone":[242],"all":[244],"showed":[247],"substantial":[249],"under-prediction":[250],"peak,":[253],"otherwise":[255],"fairly":[256],"good":[257],"adherence":[258],"original":[261],"profile;":[263],"overall":[267],"substantially":[269],"improved":[270],"by":[271],"including":[272],"current":[273],"well.":[280],"(This":[281],"result":[282],"not":[284],"surprising":[286],"might":[289],"seem":[290],"first":[292],"sight,":[293],"because,":[294],"an":[296],"urban":[297],"area,":[298],"some":[299],"well":[302],"one":[305],"another":[308],"within":[309],"same":[311],"intervals).":[313],"Overall,":[314],"conclusion":[316],"reached":[318],"that":[319,326],"differences":[320],"performance":[322],"slight":[324],"considerations":[328],"become":[329],"determining":[331],"factor":[332],"when":[333],"choosing":[334],"forecasting":[336],"method.":[337],"Whilst":[338],"have":[341],"advantage":[343],"more":[346],"explicit":[347],"structure,":[348],"easier":[354],"develop":[356],"incorporate":[358],"into":[359],"local":[360],"code.":[361],"For":[362],"covering":[364],"abstract":[365],"seminar":[368],"see":[369],"IRRD":[370],"862693.":[371]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":4},{"year":2013,"cited_by_count":4},{"year":2012,"cited_by_count":7}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}