使用深度学习来预测城市空气流动的需求
Faheem Ahmed1, Muhammad Ali Memon1, Khairan Rajab2
1Department of Information Technology, University of Sindh, Jamshoro, Sindh, Pakistan.
PeerJ. Computer science
|December 13, 2024
概括
城市空中交通 (UAM) 显示出对短途旅行的前景. 一个深度学习模型,特别是变压器,准确地预测UAM需求,帮助投资决策.
科学领域:
- 运输科学 运输科学
- 人工智能的人工智能
- 城市规划 城市规划
背景情况:
- 城市空中交通 (UAM) 是一个新兴的城市交通概念.
- 评估市场可行性需要准确的需求预测.
- 对UAM部署的财政承诺是一个重大挑战.
研究的目的:
- 调查市场支持UAM部署的能力.
- 为应对UAM需求预测的关键挑战.
- 在此背景下,评估用于时间数据预测的深度学习模型.
主要方法:
- 为时间数据预测提出了一个深度学习模型.
- 使用了15万条记录的基准数据集.
- 用于UAM需求预测的LSTM,GRU和变压器模型进行比较.
主要成果:
- 变压器模型展示了卓越的性能.
- 取得了0.64.6的根平均平方误差 (RMSE).
- 确定了变压器作为UAM需求的高性能模型.
结论:
- 深度学习,特别是变压器模型,对于UAM需求预测是有效的.
- 准确的预测可以更好地分析UAM投资可行性.
- 这项研究支持对UAM市场可行性的决策.
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