Skip to content
Log in
Open access

Attention-based Spatio-Temporal Graph Convolutional Networks-enhanced deep reinforcement learning for adaptive traffic signal control in urban traffic networks.

Jing Wang Xiaopeng Wang Yang Mo Chao Huang Yanan Li Guanghe Li Hao Zhang
Jul 2026 · Scientific Reports · 0 citations
Medicine

TL;DR

A novel adaptive traffic signal control framework by integrating Attention-based Spatio-Temporal Graph Convolutional Networks (ASTGCN) with Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is proposed, providing a scalable and data-driven solution for intelligent traffic signal control in urban traffic networks, supporting the development of smart mobility systems.

Abstract

Urban traffic congestion remains a critical challenge due to the inflexibility of conventional fixed-time or rule-based traffic signal control systems, which cannot adapt to real-time dynamic traffic flows. Existing deep reinforcement learning (DRL)-based adaptive signal control methods often neglect complex spatio-temporal dependencies in urban road networks, and graph-based spatio-temporal models (e.g., ASTGCN) are mostly limited to traffic flow prediction rather than signal control optimization. To address these gaps, this study proposes a novel adaptive traffic signal control framework by integrating Attention-based Spatio-Temporal Graph Convolutional Networks (ASTGCN) with Multi-Agent Deep Deterministic Policy Gradient (MADDPG). The ASTGCN module effectively captures spatial topological correlations and temporal dynamic patterns of traffic flow via graph attention and temporal convolution, while the MADDPG framework realizes centralized training and decentralized execution to solve multi-intersection cooperative control under partial observability. Experiments are conducted on the SUMO simulation platform with a four-intersection road network, using both real-world and stochastic traffic flows for validation. Comparative results with fixed-time control, Max-Pressure, centralized/distributed DRL, and vanilla MADDPG baselines show that the proposed method reduces traffic congestion by 20%-25% and achieves superior performance in convergence speed, generalization ability, extreme traffic load resilience, and non-uniform traffic adaptation. Ablation studies further verify the effectiveness of the ASTGCN-based spatio-temporal feature extraction component. This framework provides a scalable and data-driven solution for intelligent traffic signal control in urban traffic networks, supporting the development of smart mobility systems.

DOI PubMed Semantic Scholar
Read PDF

Similar papers

Open access Jul 2026

Traffic Flow Prediction System Based on Spatiotemporal Graph Neural Network

This framework introduces an adaptive graph learning module that dynamically infers meaningful connectivity relationships among traffic sensors—not relying on fixed geographic or distance-based assumptions—but instead leveraging real-time traffic correlations and node-level embeddings, enabling effective modeling of both localized spatial interactions and multi-scale temporal dependencies across varying prediction horizons.

Zhengxu Luan, Huan Wang, Miaobowen Wang et al. · 0 citations
Aug 2026

STGNN transformer multi step traffic flow prediction model based on dynamic adaptive attention graph learning

Traffic congestion has become a major obstacle to urban mobility and quality of life due to rapid urbanization and an increase in car ownership. Accurate traffic flow prediction is important for some intelligent transportation systems such as traffic guidance and signal control. The traditional methods including ARIMA and support vector machines cannot deal well with the complicated spatial and temporal correlations in large-scale traffic data. At present, most spatiotemporal graph neural networks (STGNNs) mainly depend on fixed graph structures which are not adaptive to the varying traffic conditions. We suggest a Dynamic Adaptive Attention Graph Learning based STGNN Transformer model (DASTGNN-Transformer) for multi-step traffic flow prediction. The model uses a multi-step Transformer decoder to identify global long-term temporal dependencies and obtains local spatiotemporal features by means of spatiotemporal graph convolutional encoders. Attention maps are generated from real-time data by a dynamic adaptive graph learning module to reflect the changing location relationships. Experiments conducted on PeMSD4 (containing 307 nodes) and PeMSD8 (with 170 nodes) datasets indicate that DASTGNN-Transformer outperforms the mainstream baselines such as STGCN, adaptive graph networks and iTransformer in terms of RMSE of 0.072–0.085, MAE of 0.061–0.079 and MAPE of 8.2%–9.5%. The ablation studies have confirmed the contributions of each module: the RMSE increases to 0.12–0.20 when the dynamic graph module is omitted. Furthermore, the robustness tests have demonstrated consistent performance even in the presence of noise (the RMSE only increases to 0.142 at a noise level of 0.5) and across different datasets. This research provides an integrated approach for dynamic spatiotemporal dependency modeling which significantly enhances the multi-step prediction accuracy.

Xiao-Li Wang · 0 citations
Open access Aug 2026

Building Urban Traffic Flow Prediction Model Using Spatio-Temporal Graph Convolutional Networks

The proposed position-aware spatio-temporal modeling strategy provides a practical reference for information fusion and dynamic state estimation in large-scale wireless sensing networks and electromagnetic signal-driven monitoring systems, supporting future intelligent perception and communication infrastructures.

J. Sun, Y. Liu, Y. Dou et al. · 0 citations
Open access Jul 2026

Recurrent Graph Neural Network Hybrid Model for Spatio-Temporal Traffic Flow Prediction in Intelligent Transportation Systems

TETRA is proposed, a hybrid spatio-temporal traffic forecasting model that integrates Graph Convolutional Networks (GCNs) with Extended Long Short-Term Memory (xLSTM) to capture complex multi-timescale temporal patterns, including congestion propagation and delayed recovery dynamics, which are not well represented by conventional recurrent models.

Norman Bereczki, Vilmos Simon · 0 citations
Open access 2026

Robustness of Spatio-Temporal Graph Neural Networks for Traffic Forecasting Under Realistic Sensor Degradation

A robust focused comparative evaluation of seven traffic forecasting approaches suggests that traffic forecasting models should be assessed not only by clean-data accuracy but also by their robustness under degraded sensing conditions before deployment in real intelligent transportation systems.

Shreya N. Desai, Kasim Ishaque Ghanchi, Ali Mehdi Mirza et al. · 0 citations
Conference Open access 2025

Predicting Traffic Flow Using Attention and Spatiotemporal Graph Convolutional Networks

A prediction model that incorporates multiple attention mechanisms with spatiotemporal graph convolutional networks (HASTGCN) and designs a spatiotemporal map convolution module to collaboratively model the dynamic spatiotemporal connection of traffic flow collaboratively model is used.

Chu-xia Chen · 0 citations