Data Archiving and Networked Services (DANS) · 2015 · 20 citations · 18 references
Open access
EngineeringAutomotive EngineeringSystems EngineeringAutonomous VehiclesBrake SystemsTransportationIntelligent Transportation SystemsTraffic EngineeringTraffic Flow TheoryTraffic OperationsVehicle ControlVehicle Control SystemsAcceleration ControlFreeway Traffic ManagementStop-and-go WavesTraffic ControlTraffic Management ApplicationsTraffic Management SystemsVariable Speed LimitsVehicle-to-infrastructure Communication
The vision of intelligent vehicles traveling in road networks has prompted numerous concepts to control future traffic flow, one of which is the in-vehicle actuation of traffic control signals. The key of this concept is using intelligent vehicles as actuators for traffic control systems, replacing the traditional road-side systems. Under this concept, the authors design and test a control system that connects a traffic controller with in-vehicle controllers with Vehicle-to-Infrastructure communications. The link-level traffic controller regulates traffic speeds through variable speed limits (VSL) gantries to resolve stop-and-go waves, while intelligent vehicles control accelerations through vehicle propulsion and brake systems to optimize their local situations. It is assumed that each intelligent vehicle receives VSL commands from the traffic controller and uses them as variable parameters for the local vehicle controller. Feasibility and effectiveness of the connected control paradigm are tested in simulation on a two-lane freeway stretch with intelligent vehicles randomly distributed among human-driven vehicles. Simulation shows that the connected VSL and vehicle control system improves traffic efficiency and sustainability, i.e. total time spent in the network and average fuel consumption rate are reduced compared to (uncontrolled and controlled) scenarios with 100% human drivers and to uncontrolled scenarios with the same intelligent vehicle penetration rates.
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Meng Wang, Winnie Daamen, Serge Hoogendoorn et al. · Transportation Research Part C Emerging Technologies · 2014 · 215 citations