{"id":"https://openalex.org/W4405073748","doi":"https://doi.org/10.1088/1755-1315/1415/1/012006","title":"ML-ERV: A machine learning-based CO2 emissions model designed for rental vehicles","display_name":"ML-ERV: A machine learning-based CO2 emissions model designed for rental vehicles","publication_year":2024,"publication_date":"2024-12-01","ids":{"openalex":"https://openalex.org/W4405073748","doi":"https://doi.org/10.1088/1755-1315/1415/1/012006"},"language":"en","primary_location":{"id":"doi:10.1088/1755-1315/1415/1/012006","doi":"https://doi.org/10.1088/1755-1315/1415/1/012006","is_oa":true,"landing_page_url":"https://doi.org/10.1088/1755-1315/1415/1/012006","pdf_url":null,"source":{"id":"https://openalex.org/S4210195883","display_name":"IOP Conference Series Earth and Environmental Science","issn_l":"1755-1315","issn":["1755-1315","1755-1307"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IOP Conference Series: Earth and Environmental Science","raw_type":"journal-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1088/1755-1315/1415/1/012006","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056314022","display_name":"Anass Yousfi","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"A Yousfi","raw_affiliation_strings":["166 Hambourg Ave., Le Sud Building, 13008 Marseille, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"166 Hambourg Ave., Le Sud Building, 13008 Marseille, France","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5056314022"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.32137668,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1415","issue":"1","first_page":"012006","last_page":"012006"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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/T12095","display_name":"Vehicle emissions and performance","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10438","display_name":"Energy, Environment, Economic Growth","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10768","display_name":"Electric Vehicles and Infrastructure","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/co2-emissions","display_name":"CO2 emissions","score":0.906000018119812},{"id":"https://openalex.org/keywords/car-rental","display_name":"car rental","score":0.4860000014305115},{"id":"https://openalex.org/keywords/on-road-emissions","display_name":"on-road emissions","score":0.6779999732971191},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.7730000019073486},{"id":"https://openalex.org/keywords/emission-prediction-model","display_name":"emission prediction model","score":0.6840000152587891},{"id":"https://openalex.org/keywords/vehicle-categories","display_name":"vehicle categories","score":0.46799999475479126},{"id":"https://openalex.org/keywords/transmission-type","display_name":"transmission type","score":0.6449999809265137},{"id":"https://openalex.org/keywords/fuel-type","display_name":"fuel type","score":0.9589999914169312},{"id":"https://openalex.org/keywords/climate-change-mitigation","display_name":"climate change mitigation","score":0.40799999237060547}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7000227570533752},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.6875349283218384},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.48583027720451355},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.47912049293518066},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.4668584167957306},{"id":"https://openalex.org/C85502023","wikidata":"https://www.wikidata.org/wiki/Q157171","display_name":"Renting","level":2,"score":0.45642539858818054},{"id":"https://openalex.org/C47737302","wikidata":"https://www.wikidata.org/wiki/Q167336","display_name":"Greenhouse gas","level":2,"score":0.4395096004009247},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.43151798844337463},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.35136517882347107},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34827446937561035},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34814202785491943},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3256242275238037},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3203151226043701},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.07739543914794922},{"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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1088/1755-1315/1415/1/012006","doi":"https://doi.org/10.1088/1755-1315/1415/1/012006","is_oa":true,"landing_page_url":"https://doi.org/10.1088/1755-1315/1415/1/012006","pdf_url":null,"source":{"id":"https://openalex.org/S4210195883","display_name":"IOP Conference Series Earth and Environmental Science","issn_l":"1755-1315","issn":["1755-1315","1755-1307"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IOP Conference Series: Earth and Environmental Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1088/1755-1315/1415/1/012006","doi":"https://doi.org/10.1088/1755-1315/1415/1/012006","is_oa":true,"landing_page_url":"https://doi.org/10.1088/1755-1315/1415/1/012006","pdf_url":null,"source":{"id":"https://openalex.org/S4210195883","display_name":"IOP Conference Series Earth and Environmental Science","issn_l":"1755-1315","issn":["1755-1315","1755-1307"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IOP Conference Series: Earth and Environmental Science","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9786999821662903,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"},{"score":0.4943999946117401,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1529362454","https://openalex.org/W2086576573","https://openalex.org/W2137836806","https://openalex.org/W2155816622","https://openalex.org/W2338730565","https://openalex.org/W2624325693","https://openalex.org/W2936562521","https://openalex.org/W3048202244","https://openalex.org/W3146929222","https://openalex.org/W3168441630","https://openalex.org/W3191456473","https://openalex.org/W3194924852","https://openalex.org/W4296564528","https://openalex.org/W4309533590","https://openalex.org/W4317952475"],"related_works":["https://openalex.org/W3011239835","https://openalex.org/W4312534362","https://openalex.org/W2915047625","https://openalex.org/W3121369812","https://openalex.org/W3178576217","https://openalex.org/W4316658904","https://openalex.org/W4304606463","https://openalex.org/W2933969434","https://openalex.org/W4200374151","https://openalex.org/W4247388746"],"abstract_inverted_index":{"Abstract":[0],"On-road":[1],"carbon":[2],"dioxide":[3],"(CO2":[4],")":[5],"emissions":[6,45,189],"from":[7,46],"rental":[8,47,73],"vehicles":[9],"are":[10],"very":[11],"much":[12],"affected":[13],"by":[14],"vehicle":[15,19,93,103],"category,":[16],"types":[17],"of":[18,145,166],"transmission,":[20],"fuel":[21],"type":[22],"and":[23,38,53,85,121,163],"traveled":[24,64],"distance.":[25],"In":[26],"this":[27],"paper,":[28],"four":[29],"machine":[30,140,175],"learning":[31,141],"models":[32,50],"(decision":[33],"trees,":[34],"random":[35],"forest,":[36],"AdaBoost":[37],"XGBoost)":[39],"were":[40,51],"used":[41],"to":[42,138],"predict":[43],"CO2":[44,110,122,177,188],"vehicles.":[48],"The":[49,59,125,170],"trained":[52,181],"tested":[54],"on":[55,107],"Carbookr\u2019s":[56],"bookings":[57],"dataset.":[58],"results":[60,171],"show":[61,172],"that":[62,129,173],"the":[63,66,69,72,75,79,82,86,92,97,102,108,132,146,152,157,164,174,186],"distance,":[65],"included":[67],"mileage,":[68],"ACRISS":[70],"code,":[71],"duration,":[74],"driver\u2019s":[76],"home":[77],"country,":[78],"country":[80,87,98],"where":[81,88,99],"driver":[83],"works":[84],"he":[89,100],"pickups":[90],"up":[91],"as":[94,96],"well":[95],"returns":[101],"had":[104],"clear":[105],"impacts":[106],"transient":[109],"emission":[111,123,178],"rates.":[112,124],"There":[113],"was":[114],"a":[115],"positive":[116],"correlation":[117],"between":[118],"these":[119],"features":[120],"findings":[126],"also":[127],"indicate":[128],"XGBoost":[130,183],"achieves":[131],"best":[133],"accuracy":[134],"performance":[135],"when":[136],"compared":[137],"other":[139],"algorithms":[142],"in":[143],"terms":[144],"Root":[147],"Mean":[148,153,158],"Squared":[149],"Error":[150,155,161],"(RMSE),":[151],"Absolute":[154,159],"(MAE),":[156],"Percentage":[160],"(MAPE)":[162],"Coefficient":[165],"Determination":[167],"(R":[168],"2).":[169],"learning-based":[176],"model":[179],"ML-ERV":[180],"using":[182],"algorithm":[184],"outperforms":[185],"state-of-the-art":[187],"calculators.":[190]},"counts_by_year":[],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-10-10T00:00:00"}
