{"id":"https://openalex.org/W4406215639","doi":"https://doi.org/10.1061/jccee5.cpeng-5980","title":"Exploring the Efficacy of Artificial Intelligence in Speed Prediction: Explainable Machine-Learning Approach","display_name":"Exploring the Efficacy of Artificial Intelligence in Speed Prediction: Explainable Machine-Learning Approach","publication_year":2025,"publication_date":"2025-01-09","ids":{"openalex":"https://openalex.org/W4406215639","doi":"https://doi.org/10.1061/jccee5.cpeng-5980"},"language":"en","primary_location":{"id":"doi:10.1061/jccee5.cpeng-5980","is_oa":false,"landing_page_url":"https://doi.org/10.1061/jccee5.cpeng-5980","pdf_url":null,"source":{"id":"https://openalex.org/S176637136","display_name":"Journal of Computing in Civil Engineering","issn_l":"0887-3801","issn":["0887-3801","1943-5487"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315747","host_organization_name":"American Society of Civil Engineers","host_organization_lineage":["https://openalex.org/P4310315747"],"host_organization_lineage_names":["American Society of Civil Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computing in Civil Engineering","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/A5018868625","display_name":"Vineet Jain","orcid":"https://orcid.org/0000-0002-4662-3081"},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vineet Jain","raw_affiliation_strings":["Sardar Vallabhbhai National Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sardar Vallabhbhai National Institute of Technology","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018288302","display_name":"Rajesh Chouhan","orcid":null},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rajesh Chouhan","raw_affiliation_strings":["Sardar Vallabhbhai National Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sardar Vallabhbhai National Institute of Technology","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084455392","display_name":"Ashish Dhamaniya","orcid":"https://orcid.org/0000-0003-3430-9949"},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ashish Dhamaniya","raw_affiliation_strings":["Sardar Vallabhbhai National Institute of Technology"],"raw_orcid":"https://orcid.org/0000-0003-3430-9949","affiliations":[{"raw_affiliation_string":"Sardar Vallabhbhai National Institute of Technology","institution_ids":["https://openalex.org/I42014448"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I42014448"],"apc_list":null,"apc_paid":null,"fwci":7.5288,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.96480818,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"39","issue":"2","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9843000173568726,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9488999843597412,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6056803464889526},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5336991548538208},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5200159549713135},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.35641559958457947}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6056803464889526},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5336991548538208},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5200159549713135},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.35641559958457947}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1061/jccee5.cpeng-5980","is_oa":false,"landing_page_url":"https://doi.org/10.1061/jccee5.cpeng-5980","pdf_url":null,"source":{"id":"https://openalex.org/S176637136","display_name":"Journal of Computing in Civil Engineering","issn_l":"0887-3801","issn":["0887-3801","1943-5487"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315747","host_organization_name":"American Society of Civil Engineers","host_organization_lineage":["https://openalex.org/P4310315747"],"host_organization_lineage_names":["American Society of Civil Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computing in Civil Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1856236505","https://openalex.org/W1967444754","https://openalex.org/W2030902482","https://openalex.org/W2553942547","https://openalex.org/W2575125657","https://openalex.org/W2604663865","https://openalex.org/W2618851150","https://openalex.org/W2891503716","https://openalex.org/W2952053404","https://openalex.org/W2963095307","https://openalex.org/W2980458068","https://openalex.org/W2994624540","https://openalex.org/W2996705655","https://openalex.org/W3036890389","https://openalex.org/W3096533615","https://openalex.org/W3097953753","https://openalex.org/W3116286104","https://openalex.org/W3198148990","https://openalex.org/W4210651971","https://openalex.org/W4224239039","https://openalex.org/W4230612334","https://openalex.org/W4280634280","https://openalex.org/W4283068826","https://openalex.org/W4295926836","https://openalex.org/W4308151343","https://openalex.org/W4311290777","https://openalex.org/W4321506221","https://openalex.org/W4366085876","https://openalex.org/W4366498279","https://openalex.org/W4377104278","https://openalex.org/W4386170706"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"The":[0,150,255],"primary":[1],"concern":[2],"regarding":[3],"road":[4,86,226],"design":[5],"elements":[6],"is":[7,12,28],"traffic":[8,32,83,101,223,239,269],"stream":[9,33,90,126,230],"speed,":[10,127,231],"which":[11,70],"considered":[13],"a":[14,21,171],"good":[15],"indicator":[16],"of":[17,24,43,55,81,113,152,189,221],"travel":[18],"quality.":[19],"Although":[20],"great":[22],"deal":[23],"time":[25],"and":[26,58,85,91,106,145,158,180,218,225,235],"effort":[27],"needed":[29],"to":[30,64,184,212],"estimate":[31],"speed":[34,118],"from":[35],"the":[36,44,53,66,79,110,114,154,159,163,166,186,190,208,247,251,262],"field,":[37],"it":[38],"still":[39],"frequently":[40],"falls":[41],"short":[42],"designers\u2019":[45],"requirements.":[46],"To":[47],"address":[48],"this,":[49],"this":[50],"study":[51,77,256],"explored":[52],"efficacy":[54],"artificial":[56],"intelligence,":[57],"used":[59,176,183],"eXplainable":[60],"Machine":[61],"Learning":[62],"(XML)":[63],"determine":[65],"underlying":[67],"mechanisms":[68],"through":[69],"machine-learning":[71,132],"models":[72,120,133,155],"arrive":[73],"at":[74],"predictions.":[75,254],"This":[76,242],"investigated":[78],"impact":[80],"categorized":[82,222],"volume":[84,224],"width":[87,227],"on":[88,228],"predicting":[89,168,229],"vehicle-specific":[92],"speeds,":[93],"considering":[94],"five":[95,115],"significant":[96],"vehicle":[97,116],"categories":[98],"in":[99],"India\u2019s":[100],"stream.":[102],"Because":[103],"small":[104],"cars":[105],"two-wheelers":[107],"account":[108],"for":[109,121,207,260],"highest":[111],"proportion":[112],"categories,":[117],"prediction":[119],"these":[122,233],"vehicles,":[123],"along":[124],"with":[125,237,244],"are":[128,258],"proposed.":[129],"Four":[130],"tree-based":[131],"were":[134],"used:":[135],"decision":[136],"tree":[137,143],"(DT),":[138],"random":[139],"forest":[140],"(RF),":[141],"extra":[142],"(ET),":[144],"eXtreme":[146],"Gradient":[147],"Boosting":[148],"(XGB).":[149],"performance":[151,188,266],"all":[153],"was":[156,175,205],"validated,":[157],"outcomes":[160,257],"showed":[161],"that":[162],"RF":[164,210],"had":[165],"best":[167],"speed.":[169],"Furthermore,":[170],"test":[172],"data":[173],"set":[174],"as":[177],"an":[178],"independent":[179],"unseen":[181],"sample":[182],"assess":[185],"final":[187],"developed":[191],"model":[192,211],"fits":[193],"impartially.":[194],"A":[195],"post":[196],"hoc":[197],"explanation":[198],"technique,":[199],"i.e.,":[200],"SHapley":[201],"Additive":[202],"exPlanations":[203],"(SHAP),":[204],"employed":[206],"complex":[209],"interpret.":[213],"SHAP":[214,245],"indicated":[215],"various":[216],"positive":[217],"negative":[219],"impacts":[220],"illustrating":[232],"relationships":[234],"aligning":[236],"established":[238],"engineering":[240],"principles.":[241],"analysis":[243],"validated":[246],"causal":[248],"relationship":[249],"behind":[250],"ML":[252],"model\u2019s":[253],"useful":[259],"managing":[261],"urban":[263],"roadway":[264],"network":[265],"under":[267],"mixed":[268],"conditions.":[270]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-25T09:21:30.201066","created_date":"2025-10-10T00:00:00"}
