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A novel strategy to reduce waiting times by seamlessly integrating traffic flow prediction and (deep) reinforcement learning has been proposed in this paper. Employing an innovative reward function combined with a long short-term memory (LSTM) based forecasting module, future road densities, vehicle speeds, and traffic volume can be accurately anticipated, guiding the dynamic control of traffic light duration. The agent’s action selection aligns with environmental conditions in our framework, contributing to a more comprehensive approach to traffic management. Simulation assessments performed within the SUMO, i.e., Simulation of Urban Mobility, platform validate the effectiveness of our proposed strategy. Compared to the most competitive strategies in the literature, our approach can reduce travel time and fuel consumption by up to 18.1%, revealing the superior performance of our strategy over the closely related designs in the literature.

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Adaptive DRL-Based Traffic Signal Control with an Infused LSTM Prediction Model

  • 会议论文

  • 发表日期：

    2026年

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  • Ying-Tsu Tseng,
  • Huei-Wen Ferng

摘要

A novel strategy to reduce waiting times by seamlessly integrating traffic flow prediction and (deep) reinforcement learning has been proposed in this paper. Employing an innovative reward function combined with a long short-term memory (LSTM) based forecasting module, future road densities, vehicle speeds, and traffic volume can be accurately anticipated, guiding the dynamic control of traffic light duration. The agent’s action selection aligns with environmental conditions in our framework, contributing to a more comprehensive approach to traffic management. Simulation assessments performed within the SUMO, i.e., Simulation of Urban Mobility, platform validate the effectiveness of our proposed strategy. Compared to the most competitive strategies in the literature, our approach can reduce travel time and fuel consumption by up to 18.1%, revealing the superior performance of our strategy over the closely related designs in the literature.