{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T14:58:38Z","timestamp":1781794718929,"version":"3.54.5"},"reference-count":32,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T00:00:00Z","timestamp":1632355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This article proposes a methodology for the estimation of emissions in real driving conditions, based on board diagnostics data and machine learning, since it has been detected that there are no models for estimating pollutants without large measurement campaigns. For this purpose, driving data are obtained by means of a data logger and emissions through a portable emissions measurement system in a real driving emissions test. The data obtained are used to train artificial neural networks that estimate emissions, having previously estimated the relative importance of variables through random forest techniques. Then, by the application of the K-means algorithm, labels are obtained to implement a classification tree and thereby determine the selected gear by the driver. These models were loaded with a data set generated covering 1218.19 km of driving. The results generated were compared to the ones obtained by applying the international vehicle emissions model and with the results of the real driving emissions test, showing evidence of similar results. The main contribution of this article is that the generated model is stronger in different traffic conditions and presents good results at the speed interval with small differences at low average driving speeds because more than half of the vehicle\u2019s trip occurs in urban areas, in completely random driving conditions. These results can be useful for the estimation of emission factors with potential application in vehicular homologation processes and the estimation of vehicular emission inventories.<\/jats:p>","DOI":"10.3390\/s21196344","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"6344","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Estimation of Pollutant Emissions in Real Driving Conditions Based on Data from OBD and Machine Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4231-2267","authenticated-orcid":false,"given":"N\u00e9stor Diego","family":"Rivera-Campoverde","sequence":"first","affiliation":[{"name":"Machine-Engineering Division, Escuela T\u00e9cnica Superior de Ingenieros Industriales\u2014ETSII, Universidad Polit\u00e9cnica de Madrid, 2 Jos\u00e9 Gutierrez Abascal Street, 28006 Madrid, Spain"},{"name":"Grupo de Investigaci\u00f3n en Ingenier\u00eda del Transporte, Universidad Polit\u00e9cnica Salesiana, Calle Vieja 1230 and Elia Liut, 010105 Cuenca, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Luis","family":"Mu\u00f1oz-Sanz","sequence":"additional","affiliation":[{"name":"Machine-Engineering Division, Escuela T\u00e9cnica Superior de Ingenieros Industriales\u2014ETSII, Universidad Polit\u00e9cnica de Madrid, 2 Jos\u00e9 Gutierrez Abascal Street, 28006 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0446-6417","authenticated-orcid":false,"given":"Blanca del Valle","family":"Arenas-Ramirez","sequence":"additional","affiliation":[{"name":"Instituto Universitario de Investigaci\u00f3n del Autom\u00f3vil Francisco Aparicio Izquierdo\u2014INSIA-UPM, Escuela T\u00e9cnica Superior de Ingenieros Industriales\u2014ETSII, Universidad Polit\u00e9cnica de Madrid, 2 Jos\u00e9 Gutierrez Abascal Street, 28006 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.atmosenv.2016.12.014","article-title":"A tunnel study to validate motor vehicle emission prediction software in Australia","volume":"151","author":"Smit","year":"2017","journal-title":"Atmos. 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