Numerical Investigation of Traffic State Reconstruction and Control Using Connected Automated Vehicles

In this paper we present a numerical study on control and observation of traffic flow using Lagrangian measurements and actuators. We investigate the effect of some basic control and observation schemes using probe and actuated vehicles within the flow. The aim is to show the effect of the state reconstruction on the efficiency of the control, compared to the case using full information about the traffic. The effectiveness of the proposed state reconstruction and control algorithms is demonstrated in simulations. They show that control using the reconstructed state approaches the full-information control when the gap between the connected vehicles is not too large, reducing the delay by more than 60% when the gap between the sensor vehicles is 1.25 km on average, compared to a delay reduction of almost 80% in the full-information control case. Moreover, we propose a simple scheme for selecting which vehicles to use as sensors, in order to reduce the communication burden. Numerical simulations demonstrate that with this triggering mechanism, the delay is reduced by around 65%, compared to a reduction of 72% if all connected vehicles are communicating at all times.