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Reinforcement learning optimal adapt...
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University of Toronto (Canada).
Reinforcement learning optimal adaptive control strategy for freeway ramp metering.
紀錄類型:
書目-電子資源 : 單行本
正題名/作者:
Reinforcement learning optimal adaptive control strategy for freeway ramp metering./
作者:
Veljanovska, Kostandina.
面頁冊數:
78 p.
附註:
Source: Masters Abstracts International, Volume: 42-03, page: 1003.
Contained By:
Masters Abstracts International42-03.
標題:
Engineering, Civil. -
電子資源:
Download fulltext (下載全文)
ISBN:
0612841804
Reinforcement learning optimal adaptive control strategy for freeway ramp metering.
Veljanovska, Kostandina.
Reinforcement learning optimal adaptive control strategy for freeway ramp metering.
- 78 p.
Source: Masters Abstracts International, Volume: 42-03, page: 1003.
Thesis (M.A.Sc.)--University of Toronto (Canada), 2003.
Ramp metering is a freeway control method capable of preventing operational breakdown due to excessive total demand by limiting entry to the main line using on-ramp traffic lights. When the control strategy is properly designed, it has the potential to eliminate congestion near on-ramps, avoid blockage of exit ramps, influence route choice behaviour, and enhance traffic safety via safer merging. None of the existing ramp metering strategies is adaptive from a control theory point of view, i.e. controller parameters are not changing while working.
ISBN: 0612841804Subjects--Topical Terms:
1000005694
Engineering, Civil.
Reinforcement learning optimal adaptive control strategy for freeway ramp metering.
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Source: Masters Abstracts International, Volume: 42-03, page: 1003.
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Ramp metering is a freeway control method capable of preventing operational breakdown due to excessive total demand by limiting entry to the main line using on-ramp traffic lights. When the control strategy is properly designed, it has the potential to eliminate congestion near on-ramps, avoid blockage of exit ramps, influence route choice behaviour, and enhance traffic safety via safer merging. None of the existing ramp metering strategies is adaptive from a control theory point of view, i.e. controller parameters are not changing while working.
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This thesis introduces a new reinforcement learning approach to closed-loop adaptive optimal freeway ramp metering. Reinforcement learning is a powerful machine-learning tool used for unsupervised learning especially in stochastic environments. Its continuous learning capability enables a truly adaptive control strategy.
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