Extract Human Mobility Patterns Powered by City Semantic Diagram


The widespread use of global positioning system (GPS) devices has made it easier to access massive spatiotemporal trajectory data. This flourishing trend provided researchers with the opportunity to discover the regularities or patterns of human mobility, which was made possible by the solid data ground it laid. However, there are still three obstacles to overcome in the process of extracting semantic patterns. These obstacles are semantic absence, semantic bias, and semantic complexity. In order to address the aforementioned issues, the authors of this paper devise and implement a novel data structure they call the City Semantic Diagram. First, our method eliminates the problem of semantic absence by precisely determining semantic behaviors based on raw trajectories. Second, the design of semantic purification allows us to better identify semantic complexity derived from human mobility. Third, we eliminate the possibility of semantic bias by making use of objective data sources, such as GPS trajectories that are widely available. Experiments that were exhaustive and extensive in scale were carried out using real taxi routes and points of interest from Shanghai as the data source. When compared to other methods, City Semantic Diagram is in a position to discover fine-grained semantic patterns in an efficient and accurate manner.

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