{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T22:30:55Z","timestamp":1773700255031,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Grupo de Investigaci\u00f3n en Ingenier\u00eda del Transporte, Universidad Polit\u00e9cnica Salesiana, Machine-Engineering Division, Mechanic Engineering Department, Universidad Polit\u00e9cnica de Madrid"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper focuses on the emissions of the three most sold categories of light vehicles: sedans, SUVs, and pickups. The research is carried out through an innovative methodology based on GPS and machine learning in real driving conditions. For this purpose, driving data from the three best-selling vehicles in Ecuador are acquired using a data logger with GPS included, and emissions are measured using a PEMS in six RDE tests with two standardized routes for each vehicle. The data obtained on Route 1 are used to estimate the gears used during driving using the K-means algorithm and classification trees. Then, the relative importance of driving variables is estimated using random forest techniques, followed by the training of ANNs to estimate CO2, CO, NOX, and HC. The data generated on Route 2 are used to validate the obtained ANNs. These models are fed with a dataset generated from 324, 300, and 316 km of random driving for each type of vehicle. The results of the model were compared with the IVE model and an OBD-based model, showing similar results without the need to mount the PEMS on the vehicles for long test drives. The generated model is robust to different traffic conditions as a result of its training and validation using a large amount of data obtained under completely random driving conditions.<\/jats:p>","DOI":"10.3390\/s24072304","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T02:25:32Z","timestamp":1712283932000},"page":"2304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["GPS Data and Machine Learning Tools, a Practical and Cost-Effective Combination for Estimating Light Vehicle Emissions"],"prefix":"10.3390","volume":"24","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\u2014UPM, 28006 Madrid, Spain"},{"name":"Grupo de Investigaci\u00f3n en Ingenier\u00eda del Transporte, Universidad Polit\u00e9cnica Salesiana, Cuenca 010105, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0446-6417","authenticated-orcid":false,"given":"Blanca","family":"Arenas-Ram\u00edrez","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\u2014UPM, 28006 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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\u2014UPM, 28006 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edisson","family":"Jim\u00e9nez","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Ingenier\u00eda del Transporte, Universidad Polit\u00e9cnica Salesiana, Cuenca 010105, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,5]]},"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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