PURPOSES : The reliability of traffic volume estimates based on location intelligence data (LID) is evaluated using various statistical techniques. There are several methods for determining statistical significance or relationships between different database sets. We propose a method that best represents the statistical difference between actual LID-based traffic volume estimates and the VDS values (i.e., true values) for the same road segment. METHODS : A total of 2,496 datasets aggregated for 1-h LID and VDS data were subjected to various statistical analyses to evaluate the consistency of the two datasets. The VDS data were defined as the true values for comparison. Four different statistical techniques (procrutes, 2-sample t-test, paired-sample t-test, and model performance rating scale) were applied. RESULTS : In cases where there is a specific pattern (e.g., traffic volume distribution considering peak and off-peak times), distribution tests such as Procrustes or Kolmogorov-Smirnov are useful because not only the prediction accuracy but also the similarity of the data distribution shape is important. CONCLUSIONS : The findings of this study provide important insight into the reliability of LID-based traffic volume estimation. To evaluate the reliability between the two groups, a paired-sample t-test was considered more appropriate than the performance evaluation measure of the machine-learning model. However, it is important to set the acceptance criteria necessary to statistically determine whether the difference between the two groups in the paired-sample t-test varies according to the given problem.