An Adaptive Signal Control Model for Intersection Based on Deep Reinforcement Learning Considering Carbon Emissions
Year: 2025
Type: article
Abstract: To address the needs of enhancing adaptive control and reducing emissions at intersections within intelligent traffic signal systems, this study innovatively proposes a deep reinforcement learning signal control model tailored for mixed traffic flows. Addressing shortcomings in existing models that overlook mixed traffic scenarios, neglect optimization of CO2 emissions, and overly rely on high-performance algorithms, our model utilizes vehicle queue length, average speed, numbers of gasoline and electric vehicles, and signal phases as state information. It employs a fixed-phase strategy to decide between maintaining or switching signal states and incorporates a reward function that balances vehicle CO2 emissions and waiting times, significantly lowering intersection carbon emissions. Following training with reinforcement learning algorithms, the model consistently demonstrates effective control outcomes. Simulation results using the SUMO platform reveal that our designed reward mechanism facilitates the rapid and stable convergence of intelligent agents. Compared with Fixed Time Control (FTC), Actuated Traffic Signal Control (ATSC), and Fuel-ECO TSC (FECO-TSC) methods, our model achieves superior performance in average waiting times and CO2 emissions. Even across scenarios with gasoline–electric vehicle ratios of 25–75%, 50–50%, and 75–25%, the model exhibits significant advantages. These simulations validate the model’s rationality and effectiveness in promoting low-carbon travel and efficient signal control.
Source: Electronics
Authors: Lin Duan, Hongxing Zhao
Institution: Lanzhou Jiaotong University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G4257931736, G4298078804
OpenAlex ID: W4409606458