在货运运营中的碳排放强度建模使用可解释的人工智能在现实世界条件下
Saket Ranjan1, Shiva Nagendra Saragur Madanayak1
1Environmental Engineering Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, India.
ACS omega
|March 9, 2026
概括
在港口的重型运输车辆 (HVV) 排放大量的二氧化碳. 先进的机器学习和XAI方法揭示了驱动这些排放的运营因素,突出了更好的货物管理和排放标准的需要.
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:
- 总部位于港口的重型运输车辆 (HDV) 是二氧化碳排放和能源消耗的重要贡献者.
- 现实世界的操作因素和驾驶行为对这些排放有很大的影响.
- 传统模型很难在动态环境中捕捉复杂的,非线性排放决定因素.
研究的目的:
- 评估现实世界运营因素和驾驶行为对HV二氧化碳排放和能源使用的影响.
- 应用可解释的机器学习和可解释的人工智能 (XAI) 来评估变量影响.
- 提高排放预测模型的准确性和可解释性.
主要方法:
- 使用XGBoost,与传统的回归和整体方法相比,具有更高的预测准确度.
- 实现了可解释的人工智能 (XAI) 框架,特别是夏普利添加式解释 (SHAP),用于特征解释性.
- 分析了基于港口的HVVs的真实世界运营数据.
主要成果:
- XGBoost在R平方方面表现出高达46%的改善,估计错误减少了80%以上.
- SHAP分析量化了特征贡献,揭示了二氧化碳排放的主要驱动因素.
- 在动态运行条件下证实了高二氧化碳排放水平.
结论:
- 现实世界的运行条件导致重型车的大量二氧化碳排放.
- 改进的运输和货物管理策略对于减排至关重要.
- 纳入二氧化碳和燃料效率标准的监管框架对于气候目标和创新至关重要.
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