机器学习的应用用于预测轻型车辆的CO2排放
Jeffrey Udoh1, Joan Lu1, Qiang Xu1
1Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
机器学习使用全球协调轻型车辆测试程序数据准确预测车辆的二氧化碳排放. 一个决策树模型实现了高准确度,使得可以做出明智的决定来减少运输中的温室气体排放.
科学领域:
- 环境科学 环境科学
- 运输工程 运输工程
- 数据科学数据科学数据科学
背景情况:
- 运输产生的温室气体 (GHG) 排放对气候变化作出了重大贡献.
- 车辆排放测试对于监管合规和环境保护至关重要.
- 全球协调轻型车辆测试程序 (WLTP) 是测量轻型车辆排放量的全球标准.
研究的目的:
- 开发轻型车辆二氧化碳排放的准确预测模型.
- 利用机器学习进行增强的车辆排放分析.
- 支持明智决策,减少与运输相关的温室气体排放.
主要方法:
- 由英国车辆认证局 (VCA) 收集的使用车辆排放数据.
- 训练和评估六个回归机器学习模型来预测二氧化碳排放.
- 实施了决策树回归模型,以提高其精度.
主要成果:
- 决策树回归模型显示了最高的准确性,平均绝对误差 (MAE) 为2.20%,平均绝对百分比误差 (MAPE) 为1.69%.
- 开发了一个网络应用程序,用于实时估计二氧化碳排放量.
- 该模型基于WLTP数据有效预测二氧化碳排放.
结论:
- 机器学习和人工智能方法是促进交通部门可持续发展的有效工具.
- 准确的二氧化碳排放预测有助于制定有针对性的温室气体减排战略.
- 开发的应用程序使用户能够根据环境利益做出明智的选择.
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