Energy-Efficient Automatic Train Driving by Learning Driving Patterns.

Railway is regarded as the most sustainable means of modern transportation. With the fast-growing of fleet size and the railway mileage, the energy consumption of trains is becoming a serious concern globally. The nature of railway offers a unique opportunity to optimize the energy efficiency of locomotives by taking advantage of the undulating terrains along a route. The derivation of an energy-optimal train driving solution, however, proves to be a significant challenge due to the high dimension, nonlinearity, complex constraints, and time-varying characteristic of the problem. An optimized solution can only be attained by considering both the complex environmental conditions of a given route and the inherent characteristics of a locomotive. To tackle the problem, this paper employs a high-order correlation learning method for online generation of the energy optimized train driving solutions. Based on the driving data of experienced human drivers, a hypergraph model is used to learn the optimal embedding from the specified features for the decision of a driving operation. First, we design a feature set capturing the driving status. Next all the training data are formulated as a hypergraph and an inductive learning process is conducted to obtain the embedding matrix. The hypergraph model can be used for real-time generation of driving operation. We also proposed a reinforcement updating scheme, which offers the capability of sustainable enhancement on the hypergraph model in industrial applications. The learned model can be used to determine an optimized driving operation in real-time tested on the Hardware-in-Loop platform. Validation experiments proved that the energy consumption of the proposed solution is around 10% lower than that of average human drivers.