通过大数据和人工智能对运输CO2排放的洞察力
Zhenyu Luo1, Tingkun He1, Zhaofeng Lv1
1State Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.
Patterns (New York, N.Y.)
|April 23, 2025
概括
运输中的大数据可以帮助减少碳排放. 人工智能,包括机器学习和深度学习,有助于分析复杂的数据,以深入了解二氧化碳排放.
科学领域:
- 运输科学 运输科学
- 环境科学环境科学
- 数据科学是数据科学.
背景情况:
- 交通运输部门在减少二氧化碳排放方面面临着挑战.
- 大数据为深度脱碳提供了机会,但复杂且庞大.
- 从运输大数据中提取可操作的见解是很困难的.
研究的目的:
- 讨论运输大数据的应用,以了解二氧化碳排放.
- 引入人工智能 (AI) 模型用于数据同化和解释.
- 为数据分析提出利用机器学习 (ML) 和深度学习 (DL) 的方法.
主要方法:
- 审查运输部门大数据的使用情况.
- 引入人工智能模型,如机器学习和深度学习.
- 建议ML用于低维数据解释和DL用于时空数据可预测性.
主要成果:
- 人工智能模型可以有效地同化和解释复杂的运输大数据.
- 机器学习适用于分析低维数据集.
- 深度学习擅长提高数据的可预测性,通过多个时间尺度的空间连接来提高数据的可预测性.
结论:
- 跨学科的合作对于克服算法,数据和计算方面的挑战至关重要.
- 人工智能为利用大数据实现运输脱碳提供了强大的工具.
- 有效的数据解释和模型应用是理解和减少排放的关键.
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