5 papers
A Hierarchical Signal Coordination and Control System Using a Hybrid Model-based and Reinforcement Learning Approach
Xianyue Peng, Shenyang Chen, H. Michael Zhang
Signal control in urban corridors faces the dual challenge of maintaining arterial traffic progression while adapting to demand variations at local intersections. We propose a hier…
A Multi-scale Perimeter Control and Route Guidance System for Large-scale Road Networks
Xianyue Peng, Hao Wang, Shenyang Chen +1
Perimeter control and route guidance are effective ways to reduce traffic congestion and improve traffic efficiency by controlling the spatial and temporal traffic distribution on…
CycLight: learning traffic signal cooperation with a cycle-level strategy
Gengyue Han, Xiaohan Liu, Xianyue Peng +2
This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems. Unlike most traditi…
Joint Optimization of Traffic Signal Control and Vehicle Routing in Signalized Road Networks using Multi-Agent Deep Reinforcement Learning
Xianyue Peng, Hang Gao, Gengyue Han +2
Urban traffic congestion is a critical predicament that plagues modern road networks. To alleviate this issue and enhance traffic efficiency, traffic signal control and vehicle rou…
Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Control
Xianyue Peng, Shenyang Chen, Hang Gao +2
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.…