大规模部署智能运输,以帮助实现低碳和清洁的可持续运输
Zhenyu Jia1, Jiawei Yin1, Zeping Cao1
1Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
The Science of the total environment
|July 26, 2024
概括
智能运输速度指导系统 (ITSGS) 通过平滑驾驶行为来减少道路运输排放. 大规模的ITSGS部署为更清洁的运输和扩大低排放区提供了可持续的解决方案.
科学领域:
- 环境科学 环境科学
- 运输工程 运输工程
- 机器学习 机器学习
背景情况:
- 道路运输的深度减排面临着挑战.
- 智能运输速度指导系统 (ITSGS) 为清洁运输提供了一个潜在的解决方案.
- 了解污染物影响因素关系是关键.
研究的目的:
- 开发用于预测车辆排放的机器学习模型.
- 量化ITSGS在城市范围内的环境效益.
- 确定ITSGS实施的最佳条件.
主要方法:
- 编译了现实世界的车辆排放数据集.
- 开发了用于CO2,CO,NOx和THC预测的随机森林模型 (R2 > 0.85).
- 综合排放模型与大规模轨迹数据用于城市分析.
主要成果:
- 在不同高峰时间 (CO2: 5.71-8.16%,NOx: 13.63-16.25%,CO: 13.69-16.45%,THC: 4.84-7.07%) 中,ITSGS证明了显著的减排潜力.
- ITSGS有效地扩大了短暂低排放区.
- 在工作日平坦高峰时段实现最大的环境效益.
结论:
- 通过调节驾驶行为,ITSGS有效地减少了道路交通排放.
- ITSGS为实现低碳运输提供了可行,可持续的解决方案.
- 该研究为可持续的减排战略提供了新的见解.
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