Journal of System Simulation ›› 2025, Vol. 37 ›› Issue (6): 1343-1351.doi: 10.16182/j.issn1004731x.joss.24-1246

• Modeling and Simulation of New Quality Transportation Systems •    

Simulation Study on Adaptive Signal Control of Deformed Intersection Based on LSTM-GNN

Chen Kun, Chen Liang, Xie Jiming, Liu Fengbo, Chen Taixiong, Wei Lukuan   

  1. School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650093, China
  • Received:2024-11-11 Revised:2025-03-04 Online:2025-06-20 Published:2025-06-18
  • Contact: Chen Liang

Abstract:

Aiming at the traffic congestion at deformed intersections, an improved adaptive traffic signal control scheme based on deep learning is designed, the scheme integrates the adaptive signal control of LSTM and GNN at deformed intersections. LSTM is used to capture the dependence between time series traffic data, while GNN is used to construct a spatial interaction model between lanes. By integrating the information of time and space dimensions, the model can dynamically adjust the phase duration of signal lights according to real-time traffic conditions. The results indicate that the LSTM-GNN adaptive control scheme improves overall traffic throughput efficiency by approximately 17.3% compared to traditional fixed-signal control.

Key words: deep learning, LSTM, GNN, traffic signal control, deformed intersection, adaptive control, traffic flow optimization, time-space dependence

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