practical modeling of existing trains in the shiraz metro using neural networks and pid-fuzzy controller for ato system implementation

Transportation optimization is considered one of the main aspects of development in smart cities nowadays. urban rail transport systems, as one of the key elements of passenger movement in large cities, have witnessed significant growth and advancement in utilizing modern knowledge to provide services to passengers over the years. the existence of sufficient infrastructure and the importance of better service provision to passengers have led these systems to always stay at the forefront of recent advancements in data analytics and artificial intelligence. automatic train operation (ato) is one of the infrastructures currently being implemented in urban trains worldwide, and its quality heavily relies on the accurate analysis of train status and signaling systems. this article aims to extract practical data from the movement of trains in the shiraz metro, model it using a neural network, and then propose a new methodology for simulating and implementing the ato system in urban trains through the design of a fuzzy-pid controller.