A Global-Local Graph Attention Network for Traffic Forecasting
📰 ArXiv cs.AI
arXiv:2605.16726v1 Announce Type: new Abstract: Traffic forecasting is a significant part of intelligent transportation systems. One of the critical challenges of traffic forecasting is to find spatio-temporal correlations. In recent years, graph convolutional networks and graph attention networks have replaced traditional statistical models to predict future traffic. However, it is complicated for both of them to allow vertices to have far different characters. To address this, we propose the G
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Title: A Global-Local Graph Attention Network for Traffic Forecasting
Abstract:
arXiv:2605.16726v1 Announce Type: new Abstract: Traffic forecasting is a significant part of intelligent transportation systems. One of the critical challenges of traffic forecasting is to find spatio-temporal correlations. In recent years, graph convolutional networks and graph attention networks have replaced traditional statistical models to predict future traffic. However, it is complicated for both of them to allow vertices to have far different characters. To address this, we propose the G
Abstract:
arXiv:2605.16726v1 Announce Type: new Abstract: Traffic forecasting is a significant part of intelligent transportation systems. One of the critical challenges of traffic forecasting is to find spatio-temporal correlations. In recent years, graph convolutional networks and graph attention networks have replaced traditional statistical models to predict future traffic. However, it is complicated for both of them to allow vertices to have far different characters. To address this, we propose the G
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