Cited 5 time in webofscience Cited 6 time in scopus

Speed harmonisation and merge control using connected automated vehicles on a highway lane closure: a reinforcement learning approach

Title
Speed harmonisation and merge control using connected automated vehicles on a highway lane closure: a reinforcement learning approach
Authors
Ko, ByungjinRyu, SeunghanPark, Byungkyu BrianSon, Sang Hyuk
DGIST Authors
Ko, Byungjin; Ryu, Seunghan; Park, Byungkyu Brian; Son, Sang Hyuk
Issue Date
2020-08
Citation
IET Intelligent Transport Systems, 14(8), 947-958
Type
Article
Article Type
Article
Author Keywords
mixed traffic flowtrained Q-networkslearning (artificial intelligence)speed harmonisationconnected automated vehicleshighway lane closureCAV market penetration ratesreinforcement learning approachlane closure bottlenecktraffic congestionvehicle arrival ratesfuel consumptionhuman-driven vehiclestraffic controltraffic engineering computingroad vehiclesroad traffic controlroad traffictraffic controlhighway bottleneck areareinforcement learning algorithmQ-network
Keywords
IMPACTLIMIT
ISSN
1751-956X
Abstract
A lane closure bottleneck usually leads to traffic congestion and a waste of fuel consumption on highways. In mixed traffic that consists of human-driven vehicles and connected automated vehicles (CAVs), the CAVs can be used for traffic control to improve the traffic flow. The authors propose speed harmonisation and merge control, taking advantage of CAVs to alleviate traffic congestion at a highway bottleneck area. To this end, they apply a reinforcement learning algorithm called deep Q network to train behaviours of CAVs. By training the merge control Q-network, CAVs learn a merge mechanism to improve the mixed traffic flow at the bottleneck area. Similarly, speed harmonisation Q-network learns speed harmonisation to reduce fuel consumption and alleviate traffic congestion by controlling the speed of following vehicles. After training two Q-networks of the merge mechanism and speed harmonisation, they evaluate the trained Q-networks under various conditions in terms of vehicle arrival rates and CAV market penetration rates. The simulation results indicate that the proposed approach improves the mixed traffic flow by increasing the throughput up to 30% and reducing the fuel consumption up to 20%, when compared to the late merge control without speed harmonisation. © 2020 The Institution of Engineering and Technology.
URI
http://hdl.handle.net/20.500.11750/12844
DOI
10.1049/iet-its.2019.0709
Publisher
Institution of Engineering and Technology
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