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The impact of smart traffic interventions on roadside air quality employing machine learning approaches

Munir, Said, Luo, Zhiwen ORCID:, Dixon, Tim, Manla, Ghaithaa, Francis, Daniel, Chen, Haibo and Liu, Ye 2022. The impact of smart traffic interventions on roadside air quality employing machine learning approaches. Transportation Research Part D: Transport and Environment 110 , 103408. 10.1016/j.trd.2022.103408

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In this paper, the impact of smart traffic interventions on air quality was assessed in Thatcham, West Berkshire, UK. The intervention linked NO2 levels with the cycle time of the traffic lights. When NO2 levels exceeded a certain threshold, the strategy was triggered, which reduced the traffic congestion by turning the traffic lights green. Eight Earthsense Zephyrs air quality sensors and nine inductive-loop traffic detectors were installed in Thatcham to simultaneously monitor the air quality and traffic flows, respectively. Compared to the pre-intervention period, the observed NO2 concentrations decreased in June, July and August and increased in September 2021, however, this does not reveal the true effect of smart traffic intervention. Using the observed data on the days with- and without-exceedances, we developed two machine learning models to predict the Business-as-usual (BAU) air quality level, i.e., a generalised additive model for average concentration and a quantile regression model for peak concentration. Our results demonstrated that average predicted concentrations (BAU) were lower than the observed concentrations (with intervention) by 12.45 %. However, we found that peak concentrations decreased by 20.54 %.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Architecture
Additional Information: This is an open access article under the terms of the CC-BY Attribution 4.0 International license.
Publisher: Elsevier
ISSN: 1361-9209
Date of First Compliant Deposit: 11 August 2022
Date of Acceptance: 23 July 2022
Last Modified: 10 Nov 2022 11:45

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