A new car-following model is proposed based on recurrent neural network (RNN) to effectively describe the state change and road traffic congestion while the vehicle is moving. The model firstly gives a full velocity difference car-following model according to the driver’s reaction sensitivity and relative velocity, and then takes the vehicle position and velocity as the input parameters to optimize the safe distance between the front and rear vehicles in the car-following model based on RNN model. Finally, the effectiveness of the above model is validated by building a simulation experiment platform, and an in-depth analysis is conducted on the relationship among influencing factors, e.g., relative velocity, reaction sensitivity, headway, etc. The results reveal that, compared with traditional car-following models, the model can quickly analyze the relationship between initial position and velocity of the vehicle in a shorter time and thus obtain a smaller safe distance. In the case of small velocity difference between the front and rear vehicles, the running velocity of the front and rear vehicles is relatively stable, which is conducive to maintaining the headway.
Investigating the Impact of Connected and Automated Vehicles on Signalized and Unsignalized Intersections Safety in Mixed Traffic
