Carbon Emission Reduction in Traffic Control: A Signal Timing Optimization Method Based on Rainbow DQN

Carbon Emission Reduction in Traffic Control: A Signal Timing Optimization Method Based on Rainbow DQN

To improve intersection traffic flow and reduce vehicle energy consumption and emissions at intersections, a signal optimization method based on deep reinforcement learning (DRL) is proposed. The algorithm uses Rainbow DQN as the core framework, incorporating vehicle position, speed, and acceleratio...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Juan Lv, Zhaowei Wang, Jianxiao Ma
Format: Artykuł
Język:English
Wydane: MDPI AG 2025-01-01
Seria:Applied Sciences
Hasła przedmiotowe:
traffic management
signal timing optimization
multi-objective optimization
deep reinforcement learning (DRL)
CO<sub>2</sub> reduction
Dostęp online:https://www.mdpi.com/2076-3417/15/3/1101

Internet

https://www.mdpi.com/2076-3417/15/3/1101

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