Research on intelligent connected mixed traffic flow allocation model

The root cause of traffic congestion and reduced capacity is uneven traffic distribution. To study the operation mode of mixed traffic flow composed of CAV and manual driving vehicles, an allocation model for mixed traffic of CAV and manual driving vehicles is established, and an algorithm is provided to solve the model. With the goal of optimizing system travel costs, using Sioux Falls classic network as example, the impact of CAV on system travel costs with or without guidance is analyzed. The research results indicate that the addition of CAV has a positive impact on urban road traffic, especially when the demand is high. Under the penetration rate of medium to low CAV (0-0.6), when guided, CAV can significantly reduce travel costs and traffic congestion. With the continuous increase in the penetration rate of intelligent connected vehicles, the traffic capacity of the transportation system has become better. Even if intelligent connected vehicles choose a balanced path selection method for users, the transportation system will still improve and approach the optimal system.