Online Learning Algorithms for Train Automatic Stop Control Using Precise Location Data of Balises

Abstract

For urban metro systems with platform screen doors, train automatic stop control (TASC) has recently attracted significant attention from both industry and academia. Existing solutions to TASC are challenged by uncertain stopping errors and the fast decrease in service life of braking systems. In this paper, we try to solve the TASC problem using a new machine learning technique and propose a novel online learning control strategy with the help of the precise location data of balises installed at stations. By modeling and analysis, we find that the learning-based TASC is a challenging problem, having characteristics of small sample sizes and online learning. We then propose three algorithms for TASC by referring to heuristics, gradient descent, and reinforcement learning (RL), which are called heuristic online learning algorithm (HOA), gradient-descent-based online learning algorithm (GOA), and RL-based online learning algorithm (RLA), respectively. We also perform an extensive comparison study on a real-world data set collected in the Beijing subway. Our experimental results show that our approaches control all stopping errors in the range of ±0.30 m under various disturbances. In addition, our approaches can greatly increase the service life of braking systems by only changing the deceleration rate a few times, which is similar to experienced drivers. Among the three algorithms, RLA achieves the best results, and GOA is a little better than HOA. As online learning algorithms can dynamically reduce stopping errors by using the precise location data from balises, it is a promising technique in solving real-world problems.