Cooperative intelligent control and optimization of personal rapid transportation

Traditional personal rapid transportation(PRT) vehicles have limited number of single vehicle seats, and have the challenge of complex and changeable driving environment, therefore are difficult to meet actual traffic demand of large volume of urban transportation．A new intelligent cooperative control operation scheme is proposed to solve this problem．Operation rules of PRT vehicles and self-organization relationship among vehicles are used to establish dynamic knowledge maps in a bottom-up manner to assist decision-making of vehicle operation．The leader-follower strategy is used to form virtual fleet operation during actual vehicle driving．Rolling optimization strategy and particle swarm optimization (PSO) algorithm are combined to adjust rolling acceleration of vehicles, resulting in shortened running time of PRT vehicles forming a virtual formation, and further improved transport capacity of PRT system．Intelligent cooperative control operation scheme is found to reduce the time spent by PRT vehicles to form a virtual formation by 38%, passenger flow per hour is increased 1.5 times．This operation scheme ensures high efficiency and safety of PRT system operation, and meets the needs of low-carbon travel and green development．