多尺度时空图神经网络用于城市交通流量预测
Hui Chen1, Jian Huang2, Yong Lu3
1School of Computer and Artificial Intelligence, Foshan University, Foshan, 528225, China. chenhui@fosu.edu.cn.
Scientific reports
|July 23, 2025
概括
这项研究引入了一个新的时空图神经网络与多时间尺度 (STGMS) 复杂的城市交通流预测. 与现有模型相比,STGMS显著提高了交通预测准确度.
科学领域:
- 城市规划和交通科学 城市规划和交通科学
- 人工智能和机器学习
- 复杂系统分析 复杂系统分析
背景情况:
- 由于内部和外部因素,城市交通流表现出复杂的,非线性时空模式.
- 准确的交通流量预测是具有挑战性的,但对于高效的城市流动至关重要.
- 现有的模型难以捕捉交通流传播的复杂动态.
研究的目的:
- 提出一种新的深度学习模型,用于增强城市交通流量预测.
- 解决交通数据中复杂的非线性时空模式所带来的挑战.
- 开发一种能够整合多个时间尺度的流量特征的模型.
主要方法:
- 开发了一个带有多时间尺度 (STGMS) 的空间时间图神经网络.
- 实施了多个时间尺度的特征分解策略,以分离流量信号和残留物.
- 设计了一个统一的时空特征编码模块,用于集成的特征表示.
- 训练模型将多时间尺度的时空特征映射到未来的交通流.
主要成果:
- 与四个真实数据集的11个基线模型相比,STGMS表现出优异的性能.
- 在平均绝对误差 (MAE) 中获得了17.69%的平均改善率.
- 在根平均平方误差 (RMSE) 中实现了15.65%的平均改善率,在平均绝对百分比误差 (MAPE) 中达到10.30%.
结论:
- 拟议的STGMS模型有效地捕捉了复杂的城市交通流动动态.
- STGMS在交通流量预测准确度方面提供了显著的改进.
- 多时间尺度的方法是提高交通预测模型性能的关键.
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