An Innovative Spatiotemporal Trajectories Clustering Algorithm with Semantic Information Extraction

In contemporary urban management, the rapid advancement of technology and widespread use of location-based services provide critical insights, particularly in traffic planning. Analyzing urban spatiotemporal trajectory data reveals diverse perspectives that inform effective city management. This paper focuses on extracting insights from spatiotemporal trajectories and introduces a suite of urban data mining techniques applicable across scenarios, including points of interest diversification. The framework aids in understanding city dynamics, including the distribution and activities of individuals. The method begins by constructing spatiotemporal stay information through two processes: stay point and stay area extraction. Spatiotemporal semantic trajectory data is then integrated with Points of Interest (POI) semantic details. A novel clustering method based on network community detection is proposed. Our experiments show that the method not only achieves comparable or superior results but also ensures time efficiency, aligning with the demands of the big data era.