{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:02:01Z","timestamp":1775066521877,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T00:00:00Z","timestamp":1573603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1551731"],"award-info":[{"award-number":["1551731"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO2 and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO2 and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO2 and CO.<\/jats:p>","DOI":"10.3390\/s19224941","type":"journal-article","created":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T09:11:27Z","timestamp":1573636287000},"page":"4941","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8038-2381","authenticated-orcid":false,"given":"Hai-Bang","family":"Ly","sequence":"first","affiliation":[{"name":"University of Transport Technology, Hanoi 100000, Vietnam"}]},{"given":"Lu Minh","family":"Le","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Vietnam National University of Agriculture, Gia Lam, Hanoi 100000, Vietnam"}]},{"given":"Luong Van","family":"Phi","sequence":"additional","affiliation":[{"name":"University of Transport Technology, Hanoi 100000, Vietnam"}]},{"given":"Viet-Hung","family":"Phan","sequence":"additional","affiliation":[{"name":"University of Transport and Communications, Ha Noi 100000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4157-7717","authenticated-orcid":false,"given":"Van Quan","family":"Tran","sequence":"additional","affiliation":[{"name":"University of Transport Technology, Hanoi 100000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9707-840X","authenticated-orcid":false,"given":"Binh Thai","family":"Pham","sequence":"additional","affiliation":[{"name":"University of Transport Technology, Hanoi 100000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1603-5000","authenticated-orcid":false,"given":"Tien-Thinh","family":"Le","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"}]},{"given":"Sybil","family":"Derrible","sequence":"additional","affiliation":[{"name":"Department of Civil and Materials Engineering, Institute of Environmental Science and Policy, University of Illinois at Chicago, Chicago, IL 60607, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"174","DOI":"10.3141\/2191-22","article-title":"Macroscopic Model of Greenhouse Gas Emissions for Municipalities","volume":"2191","author":"Derrible","year":"2010","journal-title":"Transp. 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