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

Munir, S., Luo, Z. ORCID: https://orcid.org/0000-0002-2082-3958, Dixon, T. ORCID: https://orcid.org/0000-0002-4513-6337, Manla, G., Francis, D., Chen, H. and Liu, Y. (2022) The impact of smart traffic interventions on roadside air quality employing machine learning approaches. Transportation Research Part D: Transport and Environment, 110. 103408. ISSN 1361-9209 doi: 10.1016/j.trd.2022.103408

Abstract/Summary

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
URI	https://reading-clone.eprints-hosting.org/id/eprint/106866
Item Type	Article
Refereed	Yes
Divisions	Science > School of the Built Environment > Urban Living group
Publisher	Elsevier
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