CycLight: learning traffic signal cooperation with a cycle-level strategy

no code implementations • 16 Jan 2024 • Gengyue Han, Xiaohan Liu, Xianyue Peng, Hao Wang, Yu Han

This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems.

Decision Making Reinforcement Learning (RL)

Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Control

no code implementations • 17 Oct 2023 • Xianyue Peng, Hang Gao, Hao Wang, H. Michael Zhang

Over the years, reinforcement learning has emerged as a popular approach to develop signal control and vehicle platooning strategies either independently or in a hierarchical way.

Multi-agent Reinforcement Learning reinforcement-learning

Joint Optimization of Traffic Signal Control and Vehicle Routing in Signalized Road Networks using Multi-Agent Deep Reinforcement Learning

no code implementations • 16 Oct 2023 • Xianyue Peng, Hang Gao, Gengyue Han, Hao Wang, Michael Zhang

In this paper, we propose a joint optimization approach for traffic signal control and vehicle routing in signalized road networks.