Traffic signal optimization for multiple intersections based on reinforcement learning

Jaun Gu, Minhyuck Lee, Chulmin Jun, Yohee Han, Youngchan Kim, Junwon Kim

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


In order to deal with dynamic traffic flow, adaptive traffic signal controls using reinforcement learning are being studied. However, most of the related studies are difficult to apply to the real field considering only mathematical optimization. In this study, we propose a reinforcement learning‐based signal optimization model with constraints. The proposed model maintains the sequence of typical signal phases and considers the minimum green time. The model was trained using Simulation of Urban MObility (SUMO), a microscopic traffic simulator. The model was evaluated in the virtual environment similar to a real road with multiple intersections connected. The performance of the proposed model was analyzed by comparing the delay and number of stops with a reinforcement learning model that did not consider constraints and a fixed‐time model. In a peak hour, the proposed model reduced the delay from 3 min 15 s to 2 min 15 s and the number of stops from 11 to 4.7 compared to the fixed‐time model.

Original languageEnglish
Article number10688
JournalApplied Sciences (Switzerland)
Issue number22
StatePublished - 1 Nov 2021


  • Adaptive traffic signal control
  • Deep Q‐network
  • Multiple intersections
  • Reinforcement learning
  • Traffic signal optimization


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