Tsinghua Science and Technology  2019, Vol. 24 Issue (2): 226-237    doi: 10.26599/TST.2018.9010114
A Hierarchical Ensemble Learning Framework for Energy-Efficient Automatic Train Driving
Guohua Xi, Xibin Zhao, Yan Liu, Jin Huang*, Yangdong Deng
∙ Guohua Xi is with the CRRC Corporation Limited, Beijing 100078, China.
∙ Xibin Zhao, Yan Liu, Jin Huang, and Yangdong Deng are with the School of Software and Key Laboratory for Information System Security, Ministry of Education (KLISS)/Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China.

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.

Received: 20 April 2018      Published: 29 April 2019
Corresponding Authors: Jin Huang