强大的交通预测与解的时空图形神经网络
IEEE transactions on neural networks and learning systems
|December 11, 2025
概括
本研究介绍了解时空 (DIST) 图神经网络,以进行可靠的交通预测. DIST通过将稳定的流量特征与动态依赖关系脱而出,改善预测准确度,解决现实世界的分布变化.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 交通预测的时空图神经网络 (STGNNs) 经常由于外部因素引起的分布变化而失败.
- 假设独立且相同分布的 (i.i.d.) 在现实场景中,交通数据经常被侵犯.
研究的目的:
- 开发一个强大的交通预测框架,适应分布变化.
- 为了明确地将不变的交通特征与动态的时空空间依赖关系脱而出.
主要方法:
- 提出了解时空 (DIST) 图神经网络框架.
- 引入了因果关系驱动的学习目标,以将不变变量与外源因素分开.
- 开发了一个时空图形建模模块和一个图形扰动模块,用于适应性依赖性捕获和拓变异模拟.
主要成果:
- DIST框架学习了对分布转移强大的拓无学表示.
- 该模型成功地识别并利用不变特征来提高交通预测准确度.
- 在真实世界数据上的实验表明,拟议的方法优于现有方法.
结论:
- 拟议的DIST框架提供了一个可靠的解决方案,用于在分布转移的情况下预测流量.
- 明确地解开不变特征可以提高模型的弹性和预测性能.
- 该方法为开发更可靠的智能运输系统提供了一条途径.
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