基于神经常规微分方程和时空自适应网络的交通流预测模型的构建
Li Ma1,2, Yunshun Wang3, Xiaoshi Lv4
1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China. mali@lzpcc.edu.cn.
Scientific reports
|March 22, 2025
概括
本研究引入了一种新的交通流预测模型,该模型整合了长期和短期的时空特征. 该模型显著提高了各种数据集的预测准确性,超过了现有方法.
科学领域:
- 人工智能的人工智能
- 运输工程 运输工程
- 数据科学数据科学数据科学
背景情况:
- 由于时空幻象和复杂的网络动态,短期流量预测面临着挑战.
- 现有的模型往往难以有效地捕捉长期异质性和动态的短期交通模式.
研究的目的:
- 开发一个先进的流量预测模型,通过整合长期和短期特征来解决时空错觉.
- 增强对潜在的短期流量网络特征的理解.
- 提高短期交通流量和速度预测的准确性和可靠性.
主要方法:
- 提出了一个混合模型,将变压器结构与自我监督的掩盖机制相结合,用于长期的时空特征预训练.
- 引入了一个时空适应模块,用于指导跨时间序列和交通网络的短期预测.
- 设计了一种循环神经普通微分方程 (ODE) 模块,用于动态调整和提取短期时空特征.
主要成果:
- 拟议的模型在传统的时间序列,RNN,图形卷积和变压器模型中显示出显著的预测准确度改进.
- 在不同的基线模型和指标中,平均准确性改进范围从6.57%到45.09%.
- 通过多步实验验验证了运输速度预测的普遍适用性和强大性能.
结论:
- 这种新型模型有效地捕捉了复杂的时空异质性和动态特征,用于准确的短期交通预测.
- 长期预训和短期动态特征提取的整合为交通流量预测提供了强大的方法.
- 该模型对智能运输系统的现实应用充满希望.
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