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.　(C)　2015　Elsevier　B.V.　All　rights　reserved.