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- W69799889 abstract "Dynamic traffic control measures provide a set of cost effective congestion mitigation solutions for the escalating congestion problems of metropolitan areas, among which ramp metering (RM) is an effective approach. While independently controlling on-ramps can effectively prevent freeway breakdown, this may sacrifice the users of the on-ramp for the benefit of others. Additionally, limited queue capacity of on-ramps will limit RM effectiveness. In this paper, the authors present a coordinated and decentralized freeway ramp metering algorithm by metering the rates of adjacent on-ramps to efficiently utilize the queue space available on all on-ramps along freeways. The controllers are designed based on multi-agent reinforcement learning, to obtain metering rates without the need for a mathematical model of the freeway. The agents utilize a function approximation method to overcome the curse of dimensionality issue associated with conventional reinforcement learning approaches. The proposed RM control system is applied to a calibrated microsimulation model of the Gardiner Expressway in Toronto, Canada. The Gardiner expressway is the main freeway running through Downtown Toronto and suffers from extended periods of congestion. The proposed coordinated RM algorithm when applied to the Gardiner model resulted in 50% reduction in total travel time compared with the base case scenario and significantly outperformed approaches based on the well-known ALINEA RM algorithm. This improvement was attained without compromising the permissible on-ramps maximum queue limit." @default.
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- W69799889 date "2015-01-01" @default.
- W69799889 modified "2023-09-24" @default.
- W69799889 title "Decentralized Coordinated Optimal Ramp Metering: Application to the Gardiner Expressway in Downtown Toronto" @default.
- W69799889 hasPublicationYear "2015" @default.
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