GMAN: A Graph Multi-Attention Network for Traffic Prediction (1911.08415v2)

Abstract: 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.

• The paper introduces GMAN, a novel model that leverages spatio-temporal attention blocks and transform attention to enhance long-term traffic prediction accuracy.
• It employs an encoder-decoder architecture combining spatial (node2vec for sensor data) and temporal embeddings with gated fusion to capture dynamic dependencies.
• Experimental results on Xiamen and PeMS datasets show significant improvements in MAE and RMSE over traditional and deep learning baselines, reinforcing its practical value.

Overview of "GMAN: A Graph Multi-Attention Network for Traffic Prediction"

The paper "GMAN: A Graph Multi-Attention Network for Traffic Prediction" by Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi, presents a novel approach to long-term traffic prediction using advanced graph neural network techniques. The proposed model, GMAN, leverages spatio-temporal attention mechanisms to capture the intricate dependencies in traffic data across both spatial and temporal dimensions.

Technical Approach

The core innovation of GMAN lies in its architecture, which adopts an encoder-decoder framework. Both the encoder and the decoder are composed of multiple spatio-temporal attention blocks (ST-Attention blocks). The architecture is further enhanced by a transform attention layer designed to mitigate error propagation problems that are prevalent in long-term predictions. Specifically, GMAN integrates the following components:

1. Spatio-Temporal Embedding (STE):
   • Spatial Embedding: Utilizes node2vec to encode vertices representing traffic sensors into vectors that preserve the road network's structural information.
   • Temporal Embedding: Encodes time features (day-of-week, time-of-day) and fuses this with the spatial embedding to consider dynamic changes in traffic conditions.
2. ST-Attention Block:
   • Spatial Attention: Captures the dynamic spatial correlations between sensors in the road network. It includes a multi-head attention mechanism optimized to handle large-scale networks.
   • Temporal Attention: Models non-linear correlations over time steps, incorporating time context to enhance prediction accuracy.
   • Gated Fusion: Combines spatial and temporal representations using a gating mechanism to adaptively control the flow of spatial and temporal dependencies.
3. Transform Attention:
   • This layer models direct relationships between historical and future time steps, effectively reducing error propagation through internal representations.

Experimental Evaluation

The efficacy of GMAN was validated on two real-world datasets: the Xiamen dataset (traffic volume) and the PeMS dataset (traffic speed). The results demonstrated the superiority of GMAN over traditional time-series methods (e.g., ARIMA), machine learning models (e.g., SVR), and other advanced deep learning techniques such as STGCN, DCRNN, and Graph WaveNet.

• Xiamen Dataset:
  • GMAN achieved a MAE improvement up to 4% for 1-hour ahead predictions compared to state-of-the-art methods.
• PeMS Dataset:
  • Similarly, GMAN outperformed other baseline models with significant improvements in MAE, RMSE, and MAPE for long-term predictions.

Practical and Theoretical Implications

Practical Implications:

• GMAN's enhanced prediction accuracy is particularly beneficial for traffic management agencies, allowing them to make well-informed decisions to alleviate congestion.
• The model's fault-tolerance capabilities ensure reliable predictions even when portions of input data are missing, addressing common real-world issues such as sensor failures and data transmission losses.

Theoretical Implications:

• The novel attention mechanisms introduced in GMAN can be adapted and extended to other spatio-temporal prediction problems beyond traffic, such as environmental monitoring and social network analysis.
• The integration of transform attention as a strategy to counter error propagation sets a precedent for future models targeting long-term sequence predictions.

Future Developments

Anticipated future work involves extending the GMAN framework to other domains requiring spatio-temporal predictions, such as water consumption forecasting and energy grid management. Exploring variations in the attention mechanisms and adapting the model to different granularities of temporal and spatial resolutions could yield further insights and improvements.

In conclusion, the proposed GMAN model presents a significant step forward in the domain of long-term traffic prediction, offering a compelling blend of robust technical architecture and practical application potential.