Journal of System Simulation
Abstract
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.
Recommended Citation
Chen, Kun; Chen, Liang; Xie, Jiming; Liu, Fengbo; Chen, Taixiong; and Wei, Lukuan
(2025)
"Simulation Study on Adaptive Signal Control of Deformed Intersection Based on LSTM-GNN,"
Journal of System Simulation: Vol. 37:
Iss.
6, Article 1.
DOI: 10.16182/j.issn1004731x.joss.24-1246
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss6/1
First Page
1343
Last Page
1351
CLC
TP391
Recommended Citation
Chen Kun, Chen Liang, Xie Jiming, et al. Simulation Study on Adaptive Signal Control of Deformed Intersection Based on LSTM-GNN[J]. Journal of System Simulation, 2025, 37(6): 1343-1351.
DOI
10.16182/j.issn1004731x.joss.24-1246
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