用混合模型同时捕捉道路网时空动态，提升交通速度预测精度。
Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies
- 结合SGCN与LSTM，动态建模道路间复杂空间关系与时间序列模式。
- 在PEMS-BAY数据集上MAE降低12.3%，RMSE下降14.1%，MAPE减少16.5%。
- 适合交通管理、智能导航系统等需要高精度实时预测的场景。
大量交通会导致交通事故增加、空气污染加剧及时间浪费。掌握任意路段的交通速度对缓解拥堵具有重要意义。现有研究多通过固定加权图上的图卷积网络（GCNs）建模空间依赖，但节点间关系常为动态交互。本文提出信号增强图卷积网络长短期记忆模型（SGCN-LSTM），同时捕捉交通数据的时间模式与复杂的空间依赖。在PEMS-BAY道路网络数据集上的大量实验表明，该模型在均方误差（MAE）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）方面显著优于基准模型。
原文摘要 · Abstract (English)
Large amounts of traffic can lead to negative effects such as increased car accidents, air pollution, and significant time wasted. Understanding traffic speeds on any given road segment can be highly beneficial for traffic management strategists seeking to reduce congestion. While recent studies have primarily focused on modeling spatial dependencies by using graph convolutional networks (GCNs) over fixed weighted graphs, the relationships between nodes are often more complex, with edges that interact dynamically. This paper addresses both the temporal patterns in traffic data and the intricate spatial dependencies by introducing the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Extensive experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness, yielding significant improvements in Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) compared to benchmark models on the same dataset.
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