Open AccessDistributed predictive cruise control based on reinforcement learning and validation on microscopic traffic simulation
Author(s) -
Mynuddin Mohammed,
Gao Weinan
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0404
Subject(s) - cruise control , reinforcement learning , fuel efficiency , model predictive control , traffic simulation , cruise , idle , computer science , real time computing , control (management) , simulation , automotive engineering , engineering , artificial intelligence , transport engineering , microsimulation , aerospace engineering , operating system
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%.