Papers › GMAN: A Graph Multi-Attention Network for Traffic Prediction

GMAN: A Graph Multi-Attention Network for Traffic Prediction

11 Nov 2019arXiv:1911.08415archive 2025-07-28

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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alias_draw zhengchuanpan/GMAN/METR/node2vec/node2vec.py official repository unverified Apache-2.0 (permissive) · c71ec4f39a2bea7a · report
alias_setup zhengchuanpan/GMAN/METR/node2vec/node2vec.py official repository unverified Apache-2.0 (permissive) · 0ddb0dd1cf3b67dd · report
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conv2d zhengchuanpan/GMAN/METR/tf_utils.py official repository unverified Apache-2.0 (permissive) · 6122f2f19a4027d8 · report
dropout zhengchuanpan/GMAN/METR/tf_utils.py official repository unverified Apache-2.0 (permissive) · e4716e5fdd0a9483 · report
loadData zhengchuanpan/GMAN/METR/utils.py official repository unverified Apache-2.0 (permissive) · 78c234f574a9d6a2 · report
metric zhengchuanpan/GMAN/METR/utils.py official repository unverified Apache-2.0 (permissive) · 1ba47a23adf07065 · report
placeholder zhengchuanpan/GMAN/METR/model.py official repository unverified Apache-2.0 (permissive) · 87a81a456578efcc · report
placeholder zhengchuanpan/GMAN/PeMS/model.py official repository unverified Apache-2.0 (permissive) · c11afb5b81b2e8bd · report
seq2instance zhengchuanpan/GMAN/METR/utils.py official repository unverified Apache-2.0 (permissive) · 3fc58cdee460bc5d · report

Tasks

DecoderImage DehazingPredictionTraffic Prediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Dehazing KITTI GMAN PSNR 24.64 #2 of 3 Archive leaderboard report
Traffic Prediction NE-BJ GMAN 12 steps MAE 4.80 #3 of 6 Archive leaderboard report
Traffic Prediction PEMS-BAY GMAN MAE @ 12 step 1.92 #12 of 16 Archive leaderboard report
Traffic Prediction PEMS-BAY GMAN RMSE 4.49 #12 of 16 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Multi-Attention NetworkSPEED

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