Machine Learning for Transport Policy Interventions on Air Quality
Machine Learning for Transport Policy Interventions on Air Quality
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DOI：
10.1109/access.2023.3272662
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发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
F. Farhadi;R. Palacin;P. Blythe
中科院分区：
文献类型：
--
作者：
F. Farhadi;R. Palacin;P. Blythe
减少空气污染是交通政策制定者的一个主要目标。本文考虑了清洁空气区形式的干预措施，并提供了一种机器学习方法来评估在设计的干预措施下是否实现了政策目标。使用来自纽卡斯尔城市天文台的数据集。本文首先解决了寻找与政策目标相关的数据集的挑战。着眼于降低二氧化氮（NO2）浓度，使用不同的机器学习算法来建立模型。然后，本文讨论了验证的政策目标的挑战，通过比较NO2浓度的区域在两种情况下的干预和不干预。一个递归神经网络的开发，可以成功地预测NO2浓度的均方根误差为0.95。
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.