A new reinforcement learning-based variable speed limit control approach to improve traffic efficiency against freeway jam waves

Conventional reinforcement learning (RL) models of variable speed limit (VSL) control systems (and traffic control systems in general) cannot be trained in real traffic process because new control actions are usually explored randomly, which may result in high costs (delays) due to exploration and learning. For this reason, existing RL-based...

An Extended Linear Quadratic Model Predictive Control Approach for Multi-Destination Urban Traffic Networks

This paper extends an existing linear quadratic model predictive control (LQMPC) approach to multi-destination traffic networks, where the correct origin-destination (OD) relations are preserved. In the literature, the LQMPC approach has been presented for efficient routing and intersection signal control. The optimization problem in the...

Resolving freeway jam waves by discrete first-order model-based predictive control of variable speed limits

In this paper we develop a fast model predictive control (MPC) approach for variable speed limit coordination to resolve freeway jam waves. Existing MPC approaches that are based on the second-order traffic flow models suffer from high computation load due to the non-linear and non-convex optimization formulation. In recent years, simplified...

New Extended Discrete First-Order Model to Reproduce Propagation of Jam Waves

This paper proposes an extension of the discrete Lighthill–Whitham–Richards model of the cell transmission model type to reproduce capacity drop and the propagation of jam waves. Recent studies have tried to incorporate the capacity drop into discrete first-order traffic flow models for traffic optimization purposes. It was found that the inflow...