In subway systems, the automatic train operation (ATO) is gradually replacing manual driving for its high punctuality and parking accuracy. But the existing ATO systems have some drawbacks in riding comfort and energy-consumption compared with the manual driving by experienced drivers. To combine the advantages of ATO and manual driving, this paper proposes a Smart Train Operation (STO) approach based on the fusion of expert knowledge and data mining algorithms. First, we summarize the domain expert knowledge rules to ensure the safety and riding comfort. Then, we apply a regression algorithm named as CART (Classification And Regression Tree) and ensemble learning methods (i.e. Bagging and LSBoost) to obtain the valuable information from historical driving data, which are collected in the Beijing subway Yizhuang line. Besides, a heuristic train station parking algorithm (HSA) by using the positioning data storage in balises is proposed to realize precisely parking. By combing the expert knowledge, data mining algorithms and HSA, two comprehensive STO algorithms, i.e., STOB and STOL are developed for subway train operations. The proposed STO algorithms are tested by comparing both ATO and manual driving on a real-world case of the Beijing subway Yizhuang line. The results indicate that the developed STO approach is better than ATO in energy consumption and riding comfort, and it also outperforms manual driving in punctuality and parking accuracy. Finally, the flexibility of STOL and STOB is verified with extensive experiments by considering different kinds of disturbances in real-world applications. 
Smart train operation algorithms based on expert knowledge and ensemble CART for the electric locomotive