Automatic Metro Train Regulation Using Adaptive Headway for Large Passenger Flows

Time-variant passenger flow is the primary contributor to train delays and passengers' discomfort on high-frequency metro lines. For the service provider, it is important to develop timely and adaptive strategies to the changes in demand and prevent risks caused by overcrowding on plat-forms. To minimize train delays and passengers' discomfort, we propose a traffic control framework that consists of three modules: the passenger flow module (PFM), the train operation module (TOM), and the headway optimization module (HOM). In real-time, the PFM calculates the passenger flow data, while the TOM adopts the model predictive control (MPC) strategy to minimize train delays by adjusting the running time and dwell time, of each train. When the platform is overcrowded, the HOM optimizes the headway for different stations. In this way, the proposed method successfully minimizes the number of stranded passengers, their total waiting time, and train delays. To demonstrate the effectiveness of this approach, we provide numerical simulations based on actual operations.