Automatic vehicle location (AVL) and automatic passenger counting (APC) systems are powerful tools for transit agencies to archive large, detailed data sets for transit operations. Ensuring data quality is an important first step to exploiting these data sets. An automated quality assurance methodology is presented to identify unreliable archived AVL-APC data for exclusion from further operational analyses. The approach is based on observed or expected pattern limitations of travel and passenger activity, which are derived from archived data. Stop-level tests identify suspect data, which are then flagged at the trip level. A methodology case study is presented for AVL-APC data from Grand River Transit in the region of Waterloo, Ontario, Canada.
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