Xi Wang

TL;DR: An intelligent control method based on the Deep-Q-Network (DQN) algorithm for the heavy haul train running on the long and steep downhill section to enhance the train operation performance in regard to the operational safety, maintenance costs and running efficiency.

TL;DR: Numerical experiments illustrate that the composite control law is effective in inhibiting the external disturbances, and guaranteeing the safety, efficiency and comfort of high-speed trains’ movement.

TL;DR: Wang et al. as mentioned in this paper investigated the real-time train regulation and passenger load control problem with respect to frequent disturbances, and a predictive control strategy was designed to guarantee the actual train schedule and number of in-vehicle passengers track the nominal timetable and the expected passenger load with a given disturbance attenuation level.

TL;DR: The intelligent driving problem for heavy haul train is addressed based on the fusion of expert knowledge and machine learning methodologies and EasyEnsemble for the multi-class with a KNN based Denoising (EMKD) algorithm is introduced to determine the feasible Air Pressure Reduction (APR) and the exact time for exerting and releasing the air brake.

TL;DR: The robust automatic train regulation problem in high-frequency metro lines with fuzzy passenger arrival rate is addressed and the proposed approach is validated by a number of experiments under real running circumstances of Beijing Metro Yizhuang Line of China.