Traditional　control　methods　in　automatic　train　operation　(ATO)　models　have　some　disadvantages,　such　as　high　energy　consumption　and　low　riding　comfort.　To　alleviate　these　shortcomings　of　the　ATO　models,　this　paper　presents　three　data-driven　train　operation　(DTO)　models　from　a　new　perspective　that　combines　data　mining　methods　with　expert　knowledge,　since　the　manual　driving　by　experienced　drivers　can　achieve　better　performance　than　ATO　model　in　some　degree.　Based　on　the　experts　knowledge　that　are　summarized　from　experienced　train　drivers,　three　DTO　models　are　developed　by　employing　K-nearest　neighbor　(KNN)　and　ensemble　learning　methods,　i.e.,　Bagging-CART　(B-CART)　and　Adaboost.M1-CART　(A-CART),　into　experts　systems　for　train　operation.　Furthermore,　the　DTO　models　are　improved　via　a　heuristic　train　parking　algorithm　(HPA)　to　ensure　the　parking　accuracy.　With　the　field　data　in　Chinese　Dalian　Rapid　Rail　Line　3　(DRRL3),　the　effectiveness　of　the　DTO　models　are　evaluated　on　a　simulation　platform,　and　the　performance　of　the　proposed　DTO　models　are　compared　with　both　ATO　and　manual　driving　strategies.　The　results　indicate　that　the　developed　DTO　models　obtain　all　the　merits　of　the　ATO　models　and　the　manual　driving.　That　is,　they　are　better　than　the　ATO　models　in　energy　consumption　and　riding　comfort,　and　also　outperform　the　manual　driving　in　stopping　accuracy　and　punctuality.　Additionally,　the　robustness　of　the　proposed　model　is　verified　by　a　number　of　experiments　with　some　steep　gradients　and　complex　speed　limits.　(C)　2016　Elsevier　Ltd.　All　rights　reserved.