Traffic Strategy of Roundabout Based on User Portrait and Stackelberg Game

The existing research on roundabout traffic optimization is mostly based on lossless communication and combines fundamental data, such as vehicle speed, to design collaborative strategies.This approach overlooks data such as the external environment that affects traffic strategies and cannot meet the needs of practical applications.To solve the problem of practical communication constraints, a collaborative lane-change strategy for vehicles around the roundabout based on vehicle state prediction combined with user portraits is proposed.This strategy takes into account the characteristics of vehicle-road-environment collaboration of intelligent connected vehicle.A vehicle prediction method based on spatiotemporal features AP-LSTM, is designed to capture key spatiotemporal features of vehicles to achieve small sample trajectory prediction, effectively improving the accuracy and real-time performance of small sample vehicle trajectory prediction.A predictive mechanism-based vehicle collaboration algorithm PMC is also designed to compensate for the missing vehicle status information in vehicle collaboration decision-making under real-time communication constraints.The future state of vehicles is predicted based on historical data. On this basis, collaborative vehicle control at roundabouts is performed in combination with the Stackelberg game.Simulation experiments are conducted on the SUMO platform.The results show that the proposed AP-LSTM prediction algorithm has a lower Root Mean Square Error(RMSE) value than the Long Short-Term Memory (LSTM) algorithm.At the same time, compared to the SUMO algorithm, the proposed PMC collaborative algorithm has a 51.7% reduction in acceleration standard deviation and an average speed increase of 3.0%, effectively improving the traffic efficiency and driving stability of roundabout traffic.