A hierarchical ensemble learning framework for energy-efficient automatic train driving

Abstract

Railway transportation plays an important role in modern society. As China's massive railway transportation network continues to grow in total mileage and operation density, the energy consumption of trains becomes a serious concern. For any given route, the geographic characteristics are known a priori, but the parameters (e.g., loading and marshaling) of trains vary from one trip to another. An extensive analysis of the train operation data suggests that the control gear operation of trains is the most important factor that affects the energy consumption. Such an observation determines that the problem of energy-efficient train driving has to be addressed by considering both the geographic information and the trip parameters. However, the problem is difficult to solve due to its high dimension, nonlinearity, complex constraints, and time-varying characteristics. Faced with these difficulties, we propose an energy-efficient train control framework based on a hierarchical ensemble learning approach. Through hierarchical refinement, we learn prediction models of speed and gear. The learned models can be used to derive optimized driving operations under real-time requirements. This study uses random forest and bagging -REPTree as classification algorithm and regression algorithm, respectively. We conduct an extensive study on the potential of bagging, decision trees, random forest, and feature selection to design an effective hierarchical ensemble learning framework. The proposed framework was testified through simulation. The average energy consumption of the proposed method is over 7% lower than that of human drivers.

Intelligent train operation based on deep learning from excellent driver manipulation patterns

“…Huang et al [24] combined two traditional machine learning methods, random forest and support vector regression (SVM), to achieve ATO. Many researchers adopted integrated random forest or classification and regression tree (CART) algorithms to mine excellent driver data [25][26][27][28], while a rule-guidance method was proposed [29]. The traditional machine learning methods in the above literature have limited learning ability, and they cannot excavate train driving data deeply and comprehensively, which results in the low precision of train manipulation.…”