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Machine Learning for Transport Policy Interventions on Air Quality

Lookup NU author(s): Farzaneh FarhadiORCiD, Professor Roberto Palacin, Professor Phil BlytheORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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.

Publication metadata

Author(s): Farhadi F, Palacin R, Blythe P

Publication type: Article

Publication status: Published

Journal: IEEE Access

Year: 2023

Volume: 11

Pages: 43759-43777

Online publication date: 03/05/2023

Acceptance date: 30/04/2023

Date deposited: 09/11/2023

ISSN (electronic): 2169-3536

Publisher: IEEE

URL: https://doi.org/10.1109/ACCESS.2023.3272662

DOI: 10.1109/ACCESS.2023.3272662

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