Abstract:

Accurate traffic flow prediction is critical for urban transportation; however, abnormal events in traffic flow, such as severe weather or emergency road conditions, present significant challenges in maintaining the accuracy of current traffic flow prediction models. Most existing models fail to accurately capture complex dynamic patterns of traffic data and often require substantial computational resources. To address these issues, this paper proposes a Spatio-Temporal Generative Adversarial Clustering Graph Convolutional Network (CG-STGAN) for abnormal traffic flow prediction. The model uses a generator to capture and simulate normal traffic flow data, while a discriminator compares the generated data with the original data to detect abnormalities. Adversarial training helps improve the ability of the model to identify abnormal traffic flows. In addition, a Cluster Graph Convolutional Network (Cluster-GCN) is introduced to divide the traffic graph into multiple subgraphs, thereby limiting graph convolution neighborhood expansion, reducing memory usage, and improving prediction accuracy. A graph convolutional gated recurrent unit is employed to extract short-term spatiotemporal features, while a Long Short-Term Memory (LSTM) module learns long-term dependencies. The results of experiments demonstrate that CG-STGAN outperforms baseline models in abnormal traffic flow prediction.

Key words: traffic flow prediction, abnormal traffic flow, spatio-temporal dynamic features, Generative Adversarial Network (GAN), Cluster Graph Convolutional Network (Cluster-GCN)

摘要：

准确的交通流预测对城市交通至关重要, 而交通流中的异常事件(如恶劣天气、紧急道路状况等)导致的异常交通流, 对现阶段交通流模型预测的准确性提出严峻挑战。现有的交通流预测模型大多难以有效捕捉交通流数据的复杂动态模式, 且计算资源开销较大。为了解决这些问题, 本文提出一种用于异常交通流预测的时空生成对抗聚类图卷积网络(CG-STGAN)。构建一个基于时空生成对抗网络的模型, 其中, 生成器用于捕捉并模拟正常交通流数据, 鉴别器则对比生成数据与原始数据, 判断其是否异常。通过对抗训练, 生成器和鉴别器协同提升了异常交通流的检测能力。此外, 提出聚类图卷积网络, 将交通图分解为多个子图, 以限制图卷积的邻域扩张, 从而降低内存使用并提高预测准确性。最后, 结合图卷积门控循环单元挖掘交通流的短期时空特征, 并通过长短期记忆(LSTM)模块学习交通流的长期依赖。实验结果表明, 与基线模型相比, CG-STGAN在异常交通流预测方面表现出最佳性能。

关键词: 交通流预测, 异常交通流, 时空动态特性, 生成对抗网络, 聚类图卷积网络