使用人工智能技术定位碳中和的移动中心.
Madiha Bencekri1, Sion Kim1, Yee Van Fan2,3
1Department of Transportation Engineering/Department of Smart Cities, University of Seoul, Seoul, Republic of Korea.
Scientific reports
|May 29, 2024
概括
这项研究引入了三层人工智能系统,以实现最佳的碳中和移动枢纽放置. 人工智能方法显著减少了日常车辆旅行,提供了巨大的成本节约,并支持可持续发展.
科学领域:
- 城市规划 城市规划
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 越来越需要可持续的运输解决方案.
- 优化移动枢纽位置的挑战.
- 在规划中整合环境和经济因素的重要性.
研究的目的:
- 提出一种基于人工智能的新方案,用于分配碳中和的移动枢纽.
- 优化选址,考虑行程时间,排放和适用性因素.
- 评估拟议的移动中心对环境和经济的影响.
主要方法:
- 利用遗传算法进行初始站点识别和旅行时间优化.
- 采用基于集体的适用性分析,包括土地使用,人口/就业密度和运输近距离.
- 应用了交通分配模型来评估环境 (减少VKT) 和经济影响 (节省成本).
主要成果:
- 基因算法实现了77.7亿的健康值.
- 组合模型显示了高的预测准确性 (95%的R平方训练,53%的测试).
- 识别的枢纽站将每天的车辆行程减少了771,074公里,每年节省2.255亿美元.
结论:
- 三层人工智能计划有效地确定了碳中和移动枢纽的最佳地点.
- 该方法在交通规划中对可持续发展目标 (可持续发展目标11和9) 作出了重大贡献.
- 该框架提供了一种全面的方法来规划具有可衡量的好处的可持续移动中心.
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