13 citations · 27 across the 5 of their papers we have counts for
6 papers
OverFlowLight: Real-Time Gridlock Prevention and Traffic Signal Optimization for Urban Intersections
Mingyuan Li, Boyang Huang, Tianqi Jiang +5
Queue overflow, a severe consequence of urban traffic congestion, occurs when vehicle queues exceed intersection capacity, obstructing upstream traffic and triggering cascading gri…
CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learning
Mingyuan Li, Chunyu Liu, Zhuojun Li +5
Traffic accidents result in millions of injuries and fatalities globally, with a significant number occurring at intersections each year. Traffic Signal Control (TSC) is an effecti…
FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities
Mingyuan Li, Jiahao Wang, Bo Du +2
Effective traffic signal control (TSC) is crucial in mitigating urban congestion and reducing emissions. Recently, reinforcement learning (RL) has been the research trend for TSC.…
DST-GTN: Dynamic Spatio-Temporal Graph Transformer Network for Traffic Forecasting
Songtao Huang, Hongjin Song, Tianqi Jiang +5
Accurate traffic forecasting is essential for effective urban planning and congestion management. Deep learning (DL) approaches have gained colossal success in traffic forecasting…
Efficient Pressure: Improving efficiency for signalized intersections
Qiang Wu, Liang Zhang, Jun Shen +3
Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC)…
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control
Liang Zhang, Qiang Wu, Jun Shen +3
Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem. In this paper, w…