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

TL;DR

This study numerically explores how traffic flow control and observation using connected autonomous vehicles can be optimized through state reconstruction, achieving near full-information control performance with reduced communication and sensor deployment.

Contribution

It introduces novel algorithms for traffic state reconstruction and vehicle selection, improving control efficiency and reducing communication load in connected vehicle systems.

Findings

Reconstructed state control approaches full full-information control when vehicle gaps are small.

Delay reduction exceeds 60% with reconstructed state, close to 80% with full info.

Adaptive vehicle selection further reduces communication and improves control performance.

Abstract

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. %It shows that the reconstruction is well achieved if the distance between two connected and autonomous vehicles is not too large (more than 1 vehicle per kilometer). %We also show that it is possible to further improve the control performance by employing a simple adaptive probe vehicle selection scheme. 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 %It also demonstrates that it is possible to reduce the communication burden using a triggering mechanism with 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.