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深度时空依赖的卷积LSTM网络用于流量预测
Jie Tang1, Rong Zhu2, Fengyun Wu3
1School of Modern Information Industry, Guangzhou College of Commerce, Guangzhou, 510000, China. tangjiehold@163.com.
Scientific reports
|April 6, 2025
概括
本研究介绍了STDConvLSTM,这是一种深度学习模型,通过解决空间和时间不平衡来改善流量预测. 它的新的注意力机制提高了智能交通系统和智能城市的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 运输工程 运输工程
背景情况:
- 智能交通系统 (ITS) 和智能城市依赖于准确的交通流量预测.
- 现有的方法与空间和时间数据失衡作斗争,限制了预测准确度.
- 有效的交通预测有助于交通管理,旅行计划和资源分配.
研究的目的:
- 提出一种新的深度学习算法,STDConvLSTM,用于准确的流量预测.
- 通过引入一个取决于空间的注意力机制来解决空间失衡.
- 用一个依赖时间的注意力机制来解决时间失衡.
主要方法:
- 开发了STDConvLSTM,这是一个集新的注意力机制的深度学习算法.
- 实现了一个取决于空间的注意力机制,以适应地调整内核大小以适应空间特征.
- 设计了一个依赖时间的注意力机制,以赋予历史时间步骤的不同重要性.
主要成果:
- 在两个真实世界的交通数据集上取得了良好的性能.
- 证明了拟议的空间依赖注意力机制在处理空间不平衡方面的有效性.
- 验证了依赖时间的注意力机制在解决时间失衡方面的有效性.
结论:
- STDConvLSTM有效地克服了交通流量预测中的空间和时间不平衡.
- 提出的注意力机制为ITS和智能城市应用中的深度学习提供了显著的进步.
- 准确的交通流量预测对于优化城市流动和资源管理至关重要.
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