Reducing urban traffic congestion using deep learning and model predictive control
This article proposes a deep learning (DL)-based control algorithm—DL velocity-based
model predictive control (VMPC)—for reducing traffic congestion with slowly time-varying …
model predictive control (VMPC)—for reducing traffic congestion with slowly time-varying …
Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control Strategy
Traffic congestion in metropolitan areas causes several significant challenges, such as
longer travel times, decreased productivity, increased fuel consumption and vehicle …
longer travel times, decreased productivity, increased fuel consumption and vehicle …
Simulation Evaluation of a Large-Scale Implementation of Virtual-Phase Link–Based Model Predictive Control
Traffic congestion is a serious problem in the US, and traffic signal control is one of the
effective solutions to congestion. Previous research on model predictive control (MPC) …
effective solutions to congestion. Previous research on model predictive control (MPC) …
Traffic Signal Control for Large-Scale Urban Traffic Networks: Real-World Experiments using Vision-based Sensors
Effective control of traffic signals plays a critical role in ensuring smooth vehicle flow in urban
areas. Expertly engineered traffic signal controllers can considerably minimize travel delays …
areas. Expertly engineered traffic signal controllers can considerably minimize travel delays …
Model Predictive Control for Urban Traffic Signals with Stability Guarantees*
Traditional traffic signal control focuses more on the optimization aspects whereas the
stability and robustness of the closed-loop system are less studied. This paper aims to …
stability and robustness of the closed-loop system are less studied. This paper aims to …