With the development of intelligent transportation, a large amount of vehicle trajectory data is collected and stored. However, the trajectory data always has anomalous trajectory point data, seriously affecting the accuracy and effectiveness of subsequent trajectory data analysis. This study finds a class of implicit positional anomaly trajectory data that is difficult to be detected by traditional detection methods based on movement feature thresholds but plays a vital role in trajectory data analysis. To this end, this study proposes a method to detect the implicit anomalous trajectory data based on floating grid and clustering method. The parallelization method of data is realized by taking the trajectory data of some cabs in Xi’an as an example. The experimental results show that the data recall and accuracy of the proposed method to detect the hidden location anomaly could reach 0.90, and the F1-score is in the range of 0.88–0.91. The detection of such implicit anomalous trajectory data is beneficial to subsequent analysis and application of spatio-temporal trajectory data.