Research on congestion pricing to improve connected autonomous vehicles penetration rate

Connected autonomous vehicles (CAVs) are an important development direction of future traffic, and they play a positive role in reducing pollution, easing congestion and improving driving safety. Therefore, it is very necessary to take measures to encourage the popularization of CAVs. This paper studies the impact of congestion pricing on users' choice of travel mode, establishes a bi-level programming model, and discusses how to induce people to choose a more environmentally friendly travel mode and a more reasonable travel path through economic means in the presence of a hybrid traffic network of CAVs and human-driven vehicles (HDVs). The interaction between CAVs and HDVs is considered in the lower-level model, and the mode selection of individual traveler is described by using variational inequality (VI). The upper-level model constructs a mathematical programming model with the goal of minimizing the total travel time of the system. Diagonalization algorithm and genetic algorithm are used to solve the numerical example. The results show the influence and improvement of congestion pricing on the index.