Adaptive Headway Control Algorithm for Mixed-Traffic Stabilization and Optimization with Automated Cars and Trucks

Abstract

Automated vehicle (AV) technology provides an opportunity to increase capacity and reduce congestion while maintaining stability and flow rate by allowing AVs to control their movements using references such as vehicle headways. Studies have been conducted on advancing the control algorithm of automated cars, and recent research has focused on different AV market penetration ratios. However, a straightforward way to control AVs using model parameters considering different AV types (i.e., cars and trucks) has seldom been both investigated and implemented. A method is developed to show the feasibility of controlling AVs using the desired headway as a variable to ensure stability and satisfy demand. The proposed adaptive headway control method for automated vehicles can stabilize flow and optimize flow rate in a mixed-traffic environment with four vehicle types: (1) AV cars, (2) non-AV cars, (3) AV trucks, and (4) non-AV trucks. At various AV market penetration ratios and truck percentages, the relationship between AV headways and stability is investigated. The model is implemented and tested in a simulation environment based on real-time interactions of the External Driver Model and VISSIM. Numerical analyses demonstrate that if the truck percentage is 80% or less, traffic is stable with the proposed control algorithm. The proposed algorithm is evaluated in microscopic simulation. Compared with the baseline case with no headway control, the proposed adaptive headway control algorithm can lead to less oscillation in traffic flow and can also reduce the delay by 23.19% while increasing the average travel speed up to 9.09%.