Although average effective vehicle length (AEVL) has been recognized as one of the most popular methods for detecting data errors, how to set proper thresholds so as to prevent false alarms and missed detections remains a challenging ongoing issue. This study proposed a sequential screening algorithm that employed multiple comparisons with the best statistics to compare concurrently the estimated AEVLs between lanes and stations for assessment of the data quality of a target detector. With both the temporal and spatial information, the proposed method can reliably generate a confidence interval and determine whether the target detector is malfunctioning or in need of calibration. The proposed algorithm was tested with 2 weeks of detector data from Ocean City, Maryland. The analysis results demonstrate the effectiveness of the proposed sequential screening algorithm and its potential for field applications.