Modelling and simulation of autonomous train operation based on a car-following model

Abstract The growing demand for capacity has prompted the rail industry to explore next-generation train control systems, such as train autonomous operation control systems, which transmit real-time information between trains with the help of train-to-train communication. The communication delay affects the operation of the system. In addition, the train monitors real-time traffic information through on-board sensors. However, no measurement can be perfect, including sensors, which are affected by factors such as railway geometry and weather conditions. The sensor detection error is uncertain, resulting in multiple information uncertainties. Therefore, this paper proposes a train-following model based on the full velocity difference model by considering multiple information uncertainties and communication delay time to describe the autonomous operation of the train under a train autonomous operation control system. Based on this train-following model, a stability analysis and numerical simulation of train traffic flow are carried out. The results show that when the velocity measured by the sensor is smaller than the real velocity or the headway monitored by the sensor is greater than the real headway, the delay will increase and continue to propagate and accumulate backward, resulting in blockage. Otherwise, the opposite occurs. These findings suggest that the effects of multiple information uncertainties are two-sided, depending on the degree of uncertainty of velocity information and headway information. In addition, communication delay time has little effect on train flow and delay.