An analysis of power peaks in stochastic models of railway traffic

Abstract

Railway traffic flow can be modeled by a string of consecutive trains, each subject to random speed variations that are described by a stochastic process. Despite analogies with car-follower models, railways include specific features and a safety system that forces vehicles to decelerate towards a fixed lower speed if an absolute safety distance with the vehicle ahead is not respected. By simulating this dynamic system, we compute performance indicators focusing on energy consumption and the power peaks arising when multiple trains accelerate simultaneously. We study how different conditions of the system and assumptions on the stochastic processes, e.g., describing human drivers vs automated train operations (ATO), affect energy consumption and power peaks. Our results show that an ATO controller aware of precise distance and speed information can be effective at reducing energy consumption and smoothing the peaks, which are a major concern of operators.