Extraction of Popular Tourist Routes from GPS Data

Understanding tourist travel patterns is essential for tourist management and planning. This study proposes a three-step framework to extract popular tourist routes from large-scale GPS data. In this paper, tourist trajectories are identified from original GPS data based on the distance between GPS trajectory points and the point of interest. We measure the spatial and temporal distance by dynamic time warping and earth mover’s distance, respectively. Then a similarity index is used to combine them and measure the spatial-temporal similarity between tourist trajectories. Finally, the popular tourist routes are obtained by clustering tourist trajectories with high similarity index. We validated the proposed framework with a real-world GPS data set collected in Beijing, China, and conducted the sensitivity analysis of important parameters. Results of the proposed three-step framework consistent with the reality, suggesting that our framework effectively extract meaningful tourist route information from large-scale GPS data.