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ADSTGCN:一个动态自适应的深度时空图形卷积网络,用于多步骤的交通预测
Zhengyan Cui1, Junjun Zhang1, Giseop Noh2
1Department of Computer Information Engineering, Cheongju University, Cheongju 28503, Republic of Korea.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了动态自适应深度时空图卷积网络 (ADSTGCN),以改善多步骤的流量预测. 该模型克服了过度平滑,并提高了适应动态交通条件的灵活性.
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:
- 多步骤的交通预测是复杂的,因为动态的交通条件.
- 图形卷积网络 (GCNs) 提取空间流量数据,但通常遭受浅层架构和过度平滑的影响.
- 由于固定的节点结构,现有的模型缺乏灵活性.
研究的目的:
- 开发一种新的流量预测模型,解决现有的GCNs的局限性.
- 为了减轻用于交通预测的更深的GCN中的过度平滑现象.
- 增强用于现实世界网络的流量预测模型的适应性和灵活性.
主要方法:
- 拟议的动态自适应深度时空图卷积网络 (ADSTGCN).
- 实现了动态隐藏层连接和适应性重量调整,以打击过度光滑.
- 引入了一个参数共享适应矩阵,用于学习空间依赖性和网络结构适应.
主要成果:
- 在更深层的GCN中,ADSTGCN有效地解决了过度平滑的问题.
- 该模型在多步骤的交通预测中表现得更好.
- 对高速公路和城市道路网络的评估显示出有希望的结果.
结论:
- ADSTGCN提供了一种更强大,更灵活的交通预测方法.
- 适应机制增强了模型捕捉动态交通模式的能力.
- 拟议的方法显示了现实世界交通预测应用的巨大潜力.
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