将替代燃料汽车的采用与社会经济状况和空气质量指数联系起来
Linking Alternative Fuel Vehicles Adoption with Socioeconomic Status and Air Quality Index
摘要：
这是一项关于替代燃料车辆的潜在广泛使用的研究，将其与相应消费者的社会经济状况以及对由此产生的空气质量指数的影响联系起来。这一领域的研究旨在利用机器学习技术，以促进适当的政策，促进电动汽车等替代燃料汽车的普及，并对不同的人口群体给予应有的公正。皮尔森相关系数被部署在社会经济数据，空气质量指数和替代燃料汽车数据之间的关系建模。根据社会经济因素，采用线性回归对采用替代燃料汽车的空气质量指数进行预测建模。这项工作使人工智能为社会公益服务。
英文摘要：
This is a study on the potential widespread usage of alternative fuel vehicles, linking them with the socio-economic status of the respective consumers as well as the impact on the resulting air quality index. Research in this area aims to leverage machine learning techniques in order to promote appropriate policies for the proliferation of alternative fuel vehicles such as electric vehicles with due justice to different population groups. Pearson correlation coefficient is deployed in the modeling the relationships between socio-economic data, air quality index and data on alternative fuel vehicles. Linear regression is used to conduct predictive modeling on air quality index as per the adoption of alternative fuel vehicles, based on socio-economic factors. This work exemplifies artificial intelligence for social good.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
