Distributed predictive cruise control based on reinforcement learning and validation on microscopic traffic simulation
Distributed predictive cruise control based on reinforcement learning and validation on microscopic traffic simulation
- Neue Suche nach: Mynuddin, Mohammed
- Neue Suche nach: Gao, Weinan
- Neue Suche nach: Mynuddin, Mohammed
- Neue Suche nach: Gao, Weinan
This study proposes a novel distributed predictive cruise control (PCC) algorithm based on reinforcement learning. The algorithm aims at reducing idle time and maintaining an adjustable speed depending on the traffic signals. The effectiveness of the proposed approach has been validated through Paramics microscopic traffic simulations by proposing a scenario in Statesboro, Georgia. For different traffic demands, the travel time and fuel consumption rate of vehicles are compared between non-PCC and PCC algorithms. Microscopic traffic simulation results demonstrate that the proposed PCC algorithm will reduce the fuel consumption rate by 4.24% and decrease the average travel time by 3.78%.
Dokumentinformationen
Distributed predictive cruise control based on reinforcement learning and validation on microscopic traffic simulation
IET Intelligent Transport Systems ; 14 , 5 ; 270-277
2020-03-24
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
reinforcement learning , nonPCC , velocity control , traffic demands , road vehicles , average travel time , learning (artificial intelligence) , distributed control , road traffic control , traffic signals , predictive control , fuel consumption rate , predictive cruise control algorithm , traffic engineering computing , distributed predictive cruise control , microscopic traffic simulation results , Paramics microscopic traffic simulations , adjustable speed , PCC algorithm , control engineering computing
Metadata by IET is licensed under CC BY 3.0
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