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- W2624967856 abstract "Freeways are the major arteries of the transportation networks. In most major cities in North America, including Toronto, infrastructure expansion has fallen behind transportation demand, causing escalating congestion problems. It has been realized that infrastructure expansion cannot provide a complete solution to congestion problems owed to economic limitations, induced demand, and, in metropolitan areas, simply lack of space. Furthermore, the drop in freeway throughput due to congestion exacerbates the problem even more during rush hours at the time the capacity is needed the most. Dynamic traffic control measures provide a set of cost effective congestion mitigation solutions, among which ramp metering (RM) is the most effective approach. This thesis proposes a novel optimal ramp control (metering) system that coordinates the actions of multiple on-ramps in a decentralized structure. The proposed control system is based on reinforcement learning (RL); therefore, the control agent learn the optimal action from interaction with the environment and without reliance on any a priori mathematical model. The agents are designed to function optimally in both independent and coordinated modes. Therefore, the whole system is robust to communication or individual agent’s failure. The RL agents employ function approximation to directly represent states and action with continuous variables instead of relying on discrete state-action tables. Use of function approximation significantly speeds up the learning" @default.
- W2624967856 created "2017-06-23" @default.
- W2624967856 creator A5078758172 @default.
- W2624967856 date "2014-11-01" @default.
- W2624967856 modified "2023-09-27" @default.
- W2624967856 title "Decentralized Coordinated Optimal Ramp Metering using Multi-agent Reinforcement Learning" @default.
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