## 2 Literature Review  
Many papers have already explored related data cleaning and clustering analysis methods. For instance, [PERSON] et al. (2011) proposed the TOD-SS (Trajectory Outlier Detection algorithm based on Structural Similarity) algorithm, which detects outliers using an angle threshold based on the structural similarity of trajectory segments [3]. [PERSON] et al. (2016) evaluated trajectory outliers using speed and time thresholds [4]. [PERSON] et al. (2020) focused on calculating the drift of ocean buoys, using interpolation to smooth the actual GPS trajectories [5]. They also used multiple moving interpolation smoothing methods and distance thresholds for fast calculation. The methods for noise elimination include the Kalman filter, a highly efficient recursive filter. It calculates the current state estimate based on the previous state estimate and the current observation and can be used to assess noise outliers and predict future trajectories. The Gaussian Mixture Model (GMM) [6], as discussed by [PERSON] et al. (2014), is commonly used to filter out background noise. Several trajectory compression or simplification methods exist, which reduce sampling points based on the maximum distance threshold epsilon between points and line segments. A classic method is the Douglas-Peucker algorithm ([PERSON] et al., 1973) [7]. In recent years, with significant advancements in computing power, many studies have discussed and improved the DP method [8,9]. [PERSON] et al. (2016) proposed a measurement of the DP threshold, verified through plotting [10]. [PERSON] et al. (2019) determined the appropriate DP threshold based on changes in trajectory shape and used it to compress trajectories before calculating the Dynamic Time Warping (DTW) distance matrix [11]. [PERSON] et al. (2017) proposed the DOTS (Directed acyclic graph-based Online Trajectory Simplification) method [12], which uses Synchronized Euclidean Distance (SED) as a substitute distance calculation to improve computational convenience, enabling real-time operations. In the same year, [PERSON] et al. (2017) proposed the OPW-TR (open-window time ratio) method to reduce errors in SED distance calculations [13]. Other literature compares the speed of DP and various distance calculations [14] ([PERSON] et al. (2010)). The Mahalanobis distance (DM) calculation method, involving vector calculations of the covariance matrix, was proposed as early as 1936 by Indian statistician [PERSON]. Additionally, during navigation, the Dead Reckoning (DR) method, which utilizes previously determined positions to calculate the current location of a moving object, is often employed. The Monte Carlo Localization (MCL) method, a statistical analogy method, estimates and resamples particle distributions in space based on Recursive Bayesian estimation theory. The most common risk function is Mean Squared Error (MSE). [PERSON] (2022) introduced the SQUISH method [15], a real-time algorithm for trajectory compression. It calculates the SED distance of trajectory segments. It removes the maximum value, adds new points to fill the gaps, maintains a fixed data count, and repeats the process to achieve the goal of data compression. Clustering algorithms are among the most valuable methods in the field of computational intelligence, frequently seen in AI applications. [PERSON] et al. (1996) proposed DBSCAN, a density-based algorithm for spatial clustering. In DBSCAN, two critical pa rameters are epsilon, the distance threshold, and MiniPts, the minimum number of points required to form a cluster. Many subsequent scholars have proposed comparative or improved versions, such as the comparison between KNN and DBSCAN [16] ([PERSON] et al. (2005)); dynamically computed DMDBSCAN [17] ([PERSON] et al. (2013)); DSets-DBSCAN [18] ([PERSON] et al. (2016); image segmentation using pixels as sets); Soft DBSCAN [19] ([PERSON] et al. (2013); combining DBSCAN with fuzzy set theory); and GMDBSCAN [20] ([PERSON] et al. (2006)) and HDBSCAN [21] ([PERSON] et al.