预测CO2排放和车辆在十字路口的能源使用的方法
1Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959, Rzeszow, Poland. mmadziel@prz.edu.pl.
Scientific reports
|February 22, 2025
概括
这项研究开发了先进的模型,以准确预测繁忙的城市十字路口的二氧化碳排放和车辆能源消耗. 极端梯度提升 (XGBoost) 显示了最高的准确性,改善了交通管理和基础设施规划.
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:
- 目前的排放模型与复杂的城市交叉路口动态,特别是停车和移动交通斗争.
- 准确预测车辆排放量和能源消耗对于城市可持续性至关重要.
- 交通繁忙的十字路口是城市空气污染和能源浪费的重要贡献者.
研究的目的:
- 开发和验证城市十字路口二氧化碳排放和车辆能源消耗的新型预测模型.
- 在现实城市驾驶条件下提高交通排放建模的准确性.
- 为改善道路基础设施规划和交通管理战略提供工具.
主要方法:
- 使用了机器学习算法,包括线性回归,LASSO,Ridge,随机森林和极端梯度提升 (XGBoost).
- 应用基于密度的应用与噪音的空间聚类 (DBSCAN) 用于交叉特定的数据分组.
- 使用便携式排放测量系统和Hioki 3390功率分析仪收集真实世界的驾驶数据.
主要成果:
- 与其他模型相比,极端梯度提升 (XGBoost) 在预测排放和能源消耗方面表现出更高的准确性.
- 开发的模型已成功验证并应用于交通模拟软件 (Vissim).
- DBSCAN有效地聚合了交叉点特定数据,使得更有针对性和更准确的分析成为可能.
结论:
- 先进的预测模型对城市十字路口交通分析的现有方法提供了显著的改进.
- 这些发现支持在城市交通网络中优化能源使用和减少二氧化碳排放.
- 该研究为城市规划和交通管理中的数据驱动决策提供了基础,以减轻环境影响.
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