###### Abstract
[23] proposed a trajectory clustering method based on adaptive distance and hierarchical clustering based on the optimal cluster number to analyze similarities and anomalies in taxi GPS trajectory. [PERSON] et al. [24] proposed combining fuzzy C-means clustering with quantitative spatial regression to comprehensively analyze GPS data and establish the relationship model for improving the traffic service level from the perspective of time and space. To extract the important information and mine the knowledge concealed in the data, a clustering algorithm is typically employed for preliminary data analysis [25; 26]. The clustering algorithm is a fundamental and effective unsupervised learning method widely used in many fields. Clustering algorithms divide data set into different clusters according to a fixed standard (distance, similarity, etc.) [27], making the similarity of objects in the same cluster as ample as possible and the distinction of objects in the different clusters as large as possible [28]. These unsupervised clustering algorithms can be broadly divided into the following six categories: partition-based clustering, density-based clustering, model-based clustering, hierarchical clustering, grid-based clustering, and graph-based clustering [29]. Among the above algorithms, the clustering based on partition represented by the K-means algorithm has the advantages of simple structure, high efficiency, easy convergence and muscular universality. As one of the mature clustering algorithms, the K-means algorithm has achieved significant application effects in many fields. But it has disadvantages such as sensitivity to noise and outliers, difficulty in selecting cluster numbers, sensitivity to the initial clustering center, etc. Numerous scholars have presented many ideas and approaches to locating the ideal cluster centers. The K-means++ algorithm was designed to weaken the K-means algorithm's sensitivity to the initial clustering centers; the algorithm's fundamental idea is to maximize the distance between the original clustering centers [30]. [PERSON] and [PERSON] [31] combined the genetic algorithm with the K-means algorithm to obtain the Genetic K-means algorithm (GAK) converged to the global optimum. The K-means algorithm execution is regarded as the K-means operator viewed as the search operator to replace crossover; Simultaneously, a kind of clustering-specific bias mutation operator is defined, called a distance-based mutation. [PERSON] et al. [32] proposed a new clustering algorithm called Fast Genetic K-means Algorithm (FGAK) inspired by GAK, always converging to the global optimum eventually and running much faster. [PERSON] et al. [33] advanced a previous genetic-searching approach called GenClust, with the intervention of fast hill-climbing of K-means and obtain an algorithm that is faster than its predecessor. [PERSON] and [PERSON] [34] adapt the structure of stochastic population-based Gravitational Search Algorithm for effectively solving the multivariate data clustering problem. Some scholars have proposed that the combination of nearest-better neighborhood and fuzzy PSO is statistically superior to multi-mode optimization, indicating that the hybrid algorithm has advantages [35]. These improved K-means clustering algorithms achieved good results but still have disadvantages, such as excessive dependence on hyperparameters [36], over-sensitivity to outliers, and sensitivity to cluster center initialization. Toward alleviating these problems, this paper proposes a novel K-means clustering algorithm based on hybrid \"fuzzy system-particle swarm-genetic\" to automatically obtain the optimal initial centers, namely the Novel FPSO-GAK clustering algorithm. This algorithm can enhance the processing effect of automatic clustering, avoid too much uncertainty of manual configuration parameters and clustering results falling into local optimum.