{"id":"https://openalex.org/W2918209037","doi":"https://doi.org/10.1109/isc2.2018.8656924","title":"A Machine Learning Approach to Short-Term Traffic Flow Prediction: A Case Study of Interstate 64 in Missouri","display_name":"A Machine Learning Approach to Short-Term Traffic Flow Prediction: A Case Study of Interstate 64 in Missouri","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2918209037","doi":"https://doi.org/10.1109/isc2.2018.8656924","mag":"2918209037"},"language":"en","primary_location":{"id":"doi:10.1109/isc2.2018.8656924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isc2.2018.8656924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Smart Cities Conference (ISC2)","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/A5035179934","display_name":"Osama A. Mohammed","orcid":"https://orcid.org/0000-0002-2586-4046"},"institutions":[{"id":"https://openalex.org/I47838141","display_name":"Saint Louis University","ror":"https://ror.org/01p7jjy08","country_code":"US","type":"education","lineage":["https://openalex.org/I47838141"]},{"id":"https://openalex.org/I135414105","display_name":"St Louis Community College","ror":"https://ror.org/04kc77756","country_code":"US","type":"education","lineage":["https://openalex.org/I135414105"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Osama Mohammed","raw_affiliation_strings":["Parks College of Engineering, Aviation and Technology, Saint Louis University, St. Louis, MO, USA"],"affiliations":[{"raw_affiliation_string":"Parks College of Engineering, Aviation and Technology, Saint Louis University, St. Louis, MO, USA","institution_ids":["https://openalex.org/I135414105","https://openalex.org/I47838141"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066164547","display_name":"Jalil Kianfar","orcid":"https://orcid.org/0000-0002-9543-1481"},"institutions":[{"id":"https://openalex.org/I135414105","display_name":"St Louis Community College","ror":"https://ror.org/04kc77756","country_code":"US","type":"education","lineage":["https://openalex.org/I135414105"]},{"id":"https://openalex.org/I47838141","display_name":"Saint Louis University","ror":"https://ror.org/01p7jjy08","country_code":"US","type":"education","lineage":["https://openalex.org/I47838141"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jalil Kianfar","raw_affiliation_strings":["Parks College of Engineering, Aviation and Technology, Saint Louis University, St. Louis, MO, USA"],"affiliations":[{"raw_affiliation_string":"Parks College of Engineering, Aviation and Technology, Saint Louis University, St. Louis, MO, USA","institution_ids":["https://openalex.org/I135414105","https://openalex.org/I47838141"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5035179934"],"corresponding_institution_ids":["https://openalex.org/I135414105","https://openalex.org/I47838141"],"apc_list":null,"apc_paid":null,"fwci":2.7134,"has_fulltext":false,"cited_by_count":46,"citation_normalized_percentile":{"value":0.89543821,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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":0.9973000288009644,"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":0.9950000047683716,"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/gradient-boosting","display_name":"Gradient boosting","score":0.6977105140686035},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6891080737113953},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6167954206466675},{"id":"https://openalex.org/keywords/metering-mode","display_name":"Metering mode","score":0.6034542918205261},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.5367817282676697},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.46785834431648254},{"id":"https://openalex.org/keywords/advanced-traffic-management-system","display_name":"Advanced Traffic Management System","score":0.4340323209762573},{"id":"https://openalex.org/keywords/occupancy","display_name":"Occupancy","score":0.4230797290802002},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.41767966747283936},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.41617250442504883},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.41409873962402344},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3215833902359009},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.28079837560653687},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2519269287586212},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.17148646712303162},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.09864574670791626}],"concepts":[{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6977105140686035},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6891080737113953},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6167954206466675},{"id":"https://openalex.org/C30905978","wikidata":"https://www.wikidata.org/wiki/Q815598","display_name":"Metering mode","level":2,"score":0.6034542918205261},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.5367817282676697},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.46785834431648254},{"id":"https://openalex.org/C42693407","wikidata":"https://www.wikidata.org/wiki/Q4686317","display_name":"Advanced Traffic Management System","level":3,"score":0.4340323209762573},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.4230797290802002},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.41767966747283936},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.41617250442504883},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.41409873962402344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3215833902359009},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.28079837560653687},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2519269287586212},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.17148646712303162},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.09864574670791626},{"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/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical 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},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isc2.2018.8656924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isc2.2018.8656924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Smart Cities Conference (ISC2)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320307855","display_name":"World Bank Group","ror":"https://ror.org/00ae7jd04"},{"id":"https://openalex.org/F4320309638","display_name":"Missouri Department of Transportation","ror":"https://ror.org/012c35a11"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1583812233","https://openalex.org/W1971547598","https://openalex.org/W2007062458","https://openalex.org/W2012051283","https://openalex.org/W2036785686","https://openalex.org/W2088794999","https://openalex.org/W2325932209","https://openalex.org/W2466549854","https://openalex.org/W2496320243","https://openalex.org/W2510068669","https://openalex.org/W2510808060","https://openalex.org/W2593182953","https://openalex.org/W2611314312","https://openalex.org/W2743198946","https://openalex.org/W2751650042","https://openalex.org/W2774505043","https://openalex.org/W2782738497","https://openalex.org/W2793820729","https://openalex.org/W2794457829","https://openalex.org/W2807904376","https://openalex.org/W2894932612","https://openalex.org/W2977389501","https://openalex.org/W4235138236","https://openalex.org/W6659849045","https://openalex.org/W6742123511"],"related_works":["https://openalex.org/W4282043467","https://openalex.org/W2105697914","https://openalex.org/W3093197249","https://openalex.org/W1968324288","https://openalex.org/W1540010871","https://openalex.org/W2384104878","https://openalex.org/W3023979140","https://openalex.org/W2904068067","https://openalex.org/W2288817554","https://openalex.org/W2903775874"],"abstract_inverted_index":{"Proactive":[0],"traffic":[1,12,26,34,61,70,82,93,169,179,184,204],"management":[2,50],"is":[3],"a":[4,29,55,101,105,110,195],"subset":[5],"of":[6,75,89,168,175,202],"smart":[7],"mobility":[8],"applications":[9],"in":[10,17,84,118,122,177,206],"which":[11],"control":[13,35],"strategies":[14],"are":[15,28,78],"implemented":[16],"advance":[18],"to":[19,21,32,80,128],"respond":[20],"anticipated":[22],"roadway":[23,76],"conditions.":[24],"Predicted":[25],"flows":[27],"key":[30],"input":[31],"proactive":[33,39,43,48],"systems,":[36],"such":[37,67],"as":[38,68],"freeway":[40],"ramp":[41],"metering,":[42],"variable":[44],"speed":[45],"limits,":[46],"and":[47,73,109,130,173,211],"incident":[49],"systems.":[51],"This":[52],"paper":[53],"proposes":[54],"machine":[56,214],"learning":[57],"approach":[58],"for":[59,92],"short-term":[60,85],"flow":[62,83,94],"prediction":[63,95,180,185],"where":[64],"prevailing":[65],"conditions,":[66],"the":[69,123,132,138,146,153,157,166,178,183,188,200,207],"volume,":[71],"speed,":[72,171],"occupancy":[74],"segments,":[77],"used":[79,127],"predict":[81],"intervals.":[86],"Four":[87],"categories":[88],"predictive":[90,140],"methods":[91,141],"were":[96,126,142],"investigated:":[97],"deep":[98],"neural":[99],"networks,":[100],"distributed":[102,147,208],"random":[103,148,209],"forest,":[104,210],"gradient":[106,212],"boosting":[107,213],"machine,":[108],"generalized":[111],"linear":[112],"model.":[113],"Data":[114],"from":[115,199],"Interstate":[116],"64":[117],"St.":[119],"Louis,":[120],"Missouri,":[121],"United":[124],"States":[125],"calibrate":[129],"evaluate":[131],"models.":[133,215],"The":[134,161],"results":[135],"obtained":[136,155],"by":[137,156],"four":[139],"very":[143],"similar,":[144],"with":[145],"forest":[149],"model":[150],"slightly":[151],"outperforming":[152],"models":[154],"other":[158],"three":[159],"methods.":[160],"case":[162],"study":[163],"showed":[164],"that":[165],"inclusion":[167,201],"flow,":[170],"occupancy,":[172],"time":[174],"day":[176],"process":[181],"reduces":[182],"error.":[186],"However,":[187],"two-sample":[189],"Kolmogorov-Smirnov":[190],"test":[191],"did":[192],"not":[193],"show":[194],"statistically":[196],"significant":[197],"benefit":[198],"upstream":[203],"data":[205]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":6}],"updated_date":"2026-03-25T14:56:36.534964","created_date":"2025-10-10T00:00:00"}