In data mining of trains' actual timetable, data cleaning is a key step that helps to improve data quality and data utilization. This study presented a data cleaning method based on data mining technology to preprocess train actual timetable data. Using the outlier detection method, first abnormal data was detected, then the missing data were classified as missing timetable data and missing outbound and inbound train information. Finally, an interpolation method was proposed based on the planned timetable to process two types of missing data. This methodology was examined a month of actual timetable for a rail transit line. Experimental results demonstrated the efficiency of the proposed method.
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