GMAN: A Graph Multi-Attention Network for Traffic Prediction

11 Nov 2019  ·  Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi ·

Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at https://github.com/zhengchuanpan/GMAN.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Dehazing KITTI GMAN PSNR 24.64 # 2
Traffic Prediction NE-BJ GMAN 12 steps MAE 4.80 # 3
Traffic Prediction PEMS-BAY GMAN MAE @ 12 step 1.92 # 10
RMSE 4.49 # 7

Methods