Extensible prototype learning for real-time traffic signal control

Yohee Han, Hyosun Lee, Youngchan Kim

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Congestion resolution continues to remain a challenge even though various signal control systems have been developed for traffic-intersection control. To address this issue, reinforcement learning (RL)-based approaches that focus on solving the associated data-driven problems have been proposed. However, only a few methods have been developed and applied to dual-ring traffic signal control systems. Therefore, we develop an RL-based traffic signal control model for such a system to efficiently allocate the green interval in different oversaturation states of the conflicting phases. The proposed model employs a deep deterministic policy gradient algorithm to optimize the green value in the continuous action space. Further, we develop an extensible prototype learning framework for application to new intersections without additional transfer learning. The proposed model is validated based on morning peak hours in a simulation environment that reflects the actual intersection phase system and minimum green time constraints. The proposed model achieves an average 20% intersection delay reduction, compared with the fixed control method.

Original languageEnglish
Pages (from-to)1181-1198
Number of pages18
JournalComputer-Aided Civil and Infrastructure Engineering
Volume38
Issue number9
DOIs
StatePublished - Jun 2023

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