Reducing gasoline consumption in mixed connected automated vehicles environment: A joint optimization framework for traffic signals and vehicle trajectory

Abstract

The application of connected automated vehicles can significantly reduce traffic congestion, gasoline consumption, and transportation emissions. Most of the existing research on the connected automated vehicles focuses on the improvement of the performance of the transportation system, the impact on gasoline consumption and transportation emissions is not concerned. This paper aims to study the impact on gasoline consumption and transportation emissions of mixed traffic flow with connected automated vehicles at an isolated intersection. Therefore, a joint optimization framework for traffic signals and vehicle trajectory of mixed connected automated vehicles and human-driven vehicles at an isolated intersection is proposed to reduce gasoline consumption and transportation emissions. The joint optimization framework is a two-level optimization model. Firstly, a proposed dynamic programming at the upper level is applied to optimize traffic signal timing based on predicted vehicle arrival, which can be calculated by the lower level. Secondly, a model predictive control method is proposed at the lower level to optimize vehicle trajectories considering gasoline consumption. Finally, the joint optimization framework is verified in a simulated connected automated vehicle environment and a case study conducted based on an isolated intersection. Numerical results indicate that the joint optimization method can reduce both gasoline consumption and transportation emissions. The reduced vehicle delay, gasoline consumption, and CO2 emissions can be as much as 57.21%, 22.36%, and 18.61%, respectively, for the 100% penetration rate of connected automated vehicles.