Train Driving Data Learning with S-CNN Model for Gear Prediction and Optimal Driving

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 engines by the aid of various landforms along railway lines. However, because of the feature of non-linearity, high-dimension, complicated-constraint and time-variant, it is difficult to get the optimal train-driving scheme that saves the most energy. This paper proposes a dexterously augmented CNN model called sequential CNN(S-CNN) to generate automatic train-driving schemes. Forward and backward features are defined to depict the context of the running train and they are processed separately in the input layer. Operations of experienced train drivers are used for the CNN model training. The S-CNN model takes both the time-series and space-series into consideration and pays attention to their ordinal attributes. The proposed techniques are validated in a Hardware-in-Loop platform using realistic data. Experimental results proved that the driving operations obtained by S-CNN model are very similar to experienced human drivers and it saves more than 10% energy when compared with the average performance of human drivers.