DRL-ED: A deep reinforcement learning with encoder–decoder method for traffic flow prediction

Accurate traffic flow prediction assists transport authorities in addressing transportation problems. However, most forecasting methods encounter challenges in simultaneously modeling dynamic topologies and capturing long-range spatiotemporal dependencies. To this end, this paper proposes a Deep Rei...

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Vydáno v:	Engineering applications of artificial intelligence Ročník 160; s. 111823
Hlavní autoři:	Xia, Dawen, Chen, Yan, Zhang, Wenyong, Wei, Xiaoduo, Hu, Yang, Li, Yantao, Li, Huaqing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 23.11.2025
Témata:	Attention mechanism Correlation graph convolutional network Deep deterministic policy gradient Gated recurrent unit Intelligent transportation systems Traffic flow prediction Deep deterministic policy gradient Gated recurrent unit Attention mechanism Traffic flow prediction Correlation graph convolutional network Intelligent transportation systems
ISSN:	0952-1976
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Shrnutí:	Accurate traffic flow prediction assists transport authorities in addressing transportation problems. However, most forecasting methods encounter challenges in simultaneously modeling dynamic topologies and capturing long-range spatiotemporal dependencies. To this end, this paper proposes a Deep Reinforcement Learning with Encoder-Decoder (DRL-ED) method for traffic flow prediction. The proposed method incorporates an encoder-decoder architecture, which is equipped with an attention mechanism, a gated recurrent unit (GRU) network, and a correlation graph convolutional network (CorrGCN). Specifically, the attention mechanism facilitates the capture of spatiotemporal relationships by the GRU and CorrGCN networks, improving the accuracy and robustness of traffic flow prediction. Moreover, the GRU network contains short- and long-term memory units, enabling the discovery of temporal correlations across different time scales. This enables the method to consider both short-term fluctuations and long-term trends, allowing for more accurate prediction of traffic flow changes. Finally, the CorrGCN network aggregates node features through multi-layer graph convolution, enabling step-by-step information propagation to extract both local and global spatial features, thereby comprehensively understanding inter-site traffic flow relationships. The deep deterministic policy gradient (DDPG) algorithm is employed, enabling the CorrGCN network to automatically learn and update the dynamic neighbor matrix by the actor-critic framework combined with the soft update and empirical playback mechanism. This enables the network to adaptively adjust the adjacency matrix according to traffic data in different periods or scenarios, capturing spatiotemporal correlations in graph data. Experimental results demonstrate that DRL-ED outperforms 15 baseline methods in prediction accuracy.
ISSN:	0952-1976
DOI:	10.1016/j.engappai.2025.111823