###### Abstract  
With rapid economic and demographic growth, traffic conditions in medium and large cities are becoming extremely congested. Numerous metropolitan management organizations hope to promote the coordination of traffic and urban development by formulating and improving traffic development strategies. The effectiveness of these solutions depends largely on an accurate assessment of the distribution of urban hotspots (centers of traffic activity). In recent years, many scholars have employed the K-Means clustering technique to identify urban hotspots, believing it to be efficient. K-means clustering is a sort of iterative clustering analysis. When the data dimensionality is large and the sample size is enormous, the K-Means clustering algorithm is sensitive to the initial clustering centers. To mitigate the problem, a hybrid heuristic \"fuzzy system-particle swarm-genetic\" algorithm, named FPSO-GAK, is employed to obtain better initial clustering centers for the K-Means clustering algorithm. The clustering results are evaluated and analyzed using three-cluster evaluation indexes (SC, SP and SSE) and two-cluster similarity indexes (CI and CSI). A taxi GPS dataset and a multi-source dataset were employed to test and validate the effectiveness of the proposed algorithm in comparison to the Random Swap clustering algorithm (RS), Genetic K-means algorithm (GAK), Particle Swarm Optimization (PSO) based K-Means, PSO based constraint K-Means, PSO based Weighted K-Means, PSO-GA based K-Means and K-Means++ algorithms. The comparison findings demonstrate that the proposed algorithm can achieve better clustering results, as well as successfully acquire urban hotspots. url  
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network's complexity and surrounding space environment make it challenging to obtain urban hotspots. The widespread application of information technologies in the cities, such as GPS navigation, provides high-resolution Spatio-temporal perception [7]. In particular, GPS navigation services are widely used in vehicles, making it possible to accurately perceive the movement of many vehicles simultaneously in time and space [8]. The emergence of GPS trajectory data provides a new approach and method to solve the problem of urban traffic optimization [9]. As ordinary day-to-day GPS trajectory data, taxi track data not only reflects urban traffic conditions but also records people's daily travel information [10]. How to mine hidden knowledge from massive trajectory data has become an important research topic [11; 12]. Many researchers have analyzed the GPS data of vehicle trajectory and drilled the connotative information behind these data, straining to find the spatial-temporal variation models reflecting vehicles distribution [13]. These models are applied to public transportation optimization analysis [14], urban population spatial and temporal dynamic distribution analysis [15], private car navigation real-time optimal path analysis [16], municipal road planning [17], urban hotspots searching [18; 19], etc. Many research results have been applied to the practice of smart city construction, achieving good results in the balance of urban traffic flow and human flow load, and playing a pivotal role in emergency treatment. These researchers use many machine learning methods based on GPS data sets, such as clustering analysis algorithm, time series algorithm, deep learning algorithm, reinforcement learning algorithm, and so on. [PERSON] and [PERSON] [20] proposed a grid-based method for GPS route analysis for retrieval that achieves efficient route search under noisy and variable sampling rate conditions. [PERSON] et al. [21] proposed a hybrid method for incremental extraction of urban road network from GPS data set, which can mine cumulative change patterns of road network. [PERSON] et al. [22] proposed a spatial kernel density calculation method based on GPS data to provide decision support for the allocation of medical emergency resources. [PERSON] et al.