Ha Sooji, Marchetto Daniel J, Dharur Sameer, Asensio Omar I
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA.
Patterns (N Y). 2021 Jan 22;2(2):100195. doi: 10.1016/j.patter.2020.100195. eCollection 2021 Feb 12.
The transportation sector is a major contributor to greenhouse gas (GHG) emissions and is a driver of adverse health effects globally. Increasingly, government policies have promoted the adoption of electric vehicles (EVs) as a solution to mitigate GHG emissions. However, government analysts have failed to fully utilize consumer data in decisions related to charging infrastructure. This is because a large share of EV data is unstructured text, which presents challenges for data discovery. In this article, we deploy advances in transformer-based deep learning to discover topics of attention in a nationally representative sample of user reviews. We report classification accuracies greater than 91% (F1 scores of 0.83), outperforming previously leading algorithms in this domain. We describe applications of these deep learning models for public policy analysis and large-scale implementation. This capability can boost intelligence for the EV charging market, which is expected to grow to US$27.6 billion by 2027.
交通运输部门是温室气体（GHG）排放的主要贡献者，也是全球不良健康影响的驱动因素。政府政策越来越多地推动采用电动汽车（EV）作为减少温室气体排放的解决方案。然而，政府分析人员在与充电基础设施相关的决策中未能充分利用消费者数据。这是因为很大一部分电动汽车数据是无结构文本，这给数据发现带来了挑战。在本文中，我们运用基于Transformer的深度学习进展，在具有全国代表性的用户评论样本中发现关注的主题。我们报告的分类准确率超过91%（F1分数为0.83），优于该领域以前领先的算法。我们描述了这些深度学习模型在公共政策分析和大规模实施中的应用。这种能力可以提升电动汽车充电市场的智能水平，预计到2027年该市场规模将增长至276亿美元。
