Most　existing　automatic　train　operation　(ATO)　models　are　based　on　different　train　control　algorithms　and　aim　to　closely　track　the　target　velocity　curve　optimized　offline.　This　kind　of　model　easily　leads　to　some　problems,　such　as　frequent　changes　of　the　control　outputs,　inflexibility　of　real-time　adjustments,　reduced　riding　comfort　and　increased　energy　consumption.　A　new　data-driven　train　operation　(DTO)　model　is　proposed　in　this　paper　to　conduct　the　train　control　by　employing　expert　knowledge　learned　from　experienced　drivers,　online　optimization　approach　based　on　gradient　descent,　and　a　heuristic　parking　method.　Rather　than　directly　to　model　the　target　velocity　curve,　the　DTO　model　alternatively　uses　the　online　and　offline　operation　data　to　infer　the　basic　control　output　according　to　the　domain　expert　knowledge.　The　online　adjustment　is　performed　over　the　basic　output　to　achieve　stability.　The　proposed　train　operation　model　is　evaluated　in　a　simulation　platform　using　the　field　data　collected　in　YiZhuang　Line　of　Beijing　Subway.　Compared　with　the　curve　tracking　approaches,　the　proposed　DTO　model　achieves　significant　improvements　in　energy　consumption　and　riding　comfort.　Furthermore,　the　DTO　model　has　more　advantages　including　the　flexibility　of　the　timetable　adjustments　and　the　less　operation　mode　conversions,　that　are　beneficial　to　the　service　life　of　train　operation　systems.　The　DTO　model　also　shows　velocity　trajectories　and　operation　mode　conversions　similar　to　the　one　of　experienced　drivers,　while　achieving　less　energy　consumption　and　smaller　parking　error.　The　robustness　of　the　proposed　algorithm　is　verified　through　numerical　simulations　with　different　system　parameters,　complicated　velocity　restrictions,　diverse　running　times　and　steep　gradients.