Machine Learning for Transport Policy Interventions on Air Quality

Abstract

Air pollution reduction is a major objective for transport policy makers. This paper considers interventions in the form of clean air zones, and provide a machine learning approach to assess whether the objectives of the policy are achieved under the designed intervention. The dataset from the Newcastle Urban Observatory is used. The paper first tackles the challenge of finding datasets that are relevant to the policy objective. Focusing on the reduction of nitrogen dioxide (NO2) concentrations, different machine learning algorithms are used to build models. The paper then addresses the challenge of validating the policy objective by comparing the NO2 concentrations of the zone in the two cases of with and without the intervention. A recurrent neural network is developed that can successfully predict the NO2 concentration with root mean square error of 0.95.

Keywords

• Air quality
• clean air zone
• data-driven framework
• machine learning
• policy validation
• transportation system