Journal
2018 5TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE 2018)
Volume -, Issue -, Pages 241-245Publisher
IEEE
DOI: 10.1109/ICISCE.2018.00058
Keywords
Traffic flow forecasting; Graph Neural Network; Sequence to sequence modeling; Attention mechanism
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For the road networks containing multiple intersections and links, the traffic flow forecasting is essentially a time series forecasting problem on graphs. The task is challenging due to (1) complex spatiotemporal dependence among traffic flows of the whole road network and (2) sharp non-linearity and dynamic nature under different conditions. In this paper, by extending the LSTM to have graph attention structure in both the input-to-state and state-to-state transitions, we propose the Graph Attention LSTM Network (GAT-LSTM) and use it to build an end-to-end trainable encoder-forecaster model to solve the multi-link traffic flow forecasting problem. Experiment results show that our GAT-LSTM network could capture spatiotemporal correlations better and has achieved improvement of 15% - 16% over state-of-the-art baseline.
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