Adaptive Graph Attention and Long Short-Term Memory-Based Networks for Traffic Prediction

Taomei Zhu (), Maria Jesus Lopez Boada and Beatriz Lopez Boada
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Taomei Zhu: Department of Mechanical Engineering, Carlos III University of Madrid, 28911 Madrid, Spain
Maria Jesus Lopez Boada: Department of Mechanical Engineering, Carlos III University of Madrid, 28911 Madrid, Spain
Beatriz Lopez Boada: Department of Mechanical Engineering, Carlos III University of Madrid, 28911 Madrid, Spain

Mathematics, 2024, vol. 12, issue 2, 1-18

Abstract: While the increased availability of traffic data is allowing us to better understand urban mobility, research on data-driven and predictive modeling is also providing new methods for improving traffic management and reducing congestion. In this paper, we present a hybrid predictive modeling architecture, namely GAT-LSTM, by incorporating graph attention (GAT) and long short-term memory (LSTM) networks for handling traffic prediction tasks. In this architecture, GAT networks capture the spatial dependencies of the traffic network, LSTM networks capture the temporal correlations, and the Dayfeature component incorporates time and external information (such as day of the week, extreme weather conditions, holidays, etc.). A key attention block is designed to integrate GAT, LSTM, and the Dayfeature components as well as learn and assign weights to these different components within the architecture. This method of integration is proven effective at improving prediction accuracy, as shown by the experimental results obtained with the PeMS08 open dataset, and the proposed model demonstrates state-of-the-art performance in these experiments. Furthermore, the hybrid model demonstrates adaptability to dynamic traffic conditions, different prediction horizons, and various traffic networks.

Keywords: traffic prediction; graph attention networks; long short-term memory networks; adaptive attention; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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