开发人工智能增强优化技术,用于分析和预测公路运输中的模式组合
Huma Rauf1,2, Muhammad Umer3
1Department of Business & Engineering Management, Sir Syed CASE Institute of Technology (SS-CASE-IT), Islamabad, Pakistan.
PloS one
|November 2, 2023
概括
这项研究引入了一种基于人工智能的新方法,用于识别主要的运输排放来源. 将这一点应用于巴基斯坦.
科学领域:
- 环境科学 环境科学
- 人工智能的人工智能
- 运输工程 运输工程
背景情况:
- 运输部门是全球碳排放的重要贡献者.
- 为了实现碳减排目标,具体的减缓策略往往未被定义.
- 开发特定于区域的可持续运输解决方案对于气候行动至关重要.
研究的目的:
- 开发一种简化,特定于区域的技术,用于识别主要的运输排放贡献者.
- 整合人工智能 (AI) 建模与碳减排优化方法.
- 提出可持续的运输混合方案,以实现国家碳减排目标.
主要方法:
- 使用灰色关系等级和随机森林的最小化优化技术被采用.
- 该方法应用于巴基斯坦北旁遮普省的十二种公路运输方式.
- 集成了深度神经网络 (DNN) 建模,以开发减排权衡.
主要成果:
- 在排放排放最多的五种模式 (柴油/燃气轻型/重型车辆,燃气汽车) 减少25%,实现了54.35%的二氧化碳减少.
- 将25%的燃气和柴油轻型车辆替换为50%的汽油轻型车,使2044年的排放稳定到2014年的水平.
- 该技术提供了一种系统的方法,在各种场景下预测排放优化选项.
结论:
- 开发的技术提供了一个通用但可定制的模型,用于创建环保的运输混合物.
- 它为实施模式组合战略以实现碳减排目标提供了一个明确的途径.
- 未来的研究可以将这个模型扩展到包括铁路和航空等多式联运选择.
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