Recommending most popular travel path within a region of interest from historical trajectory data

Advancement in mobile and GPS technologies have enabled users to record and publish their route activities or trajectories through location based social networking sites. Existing research mainly focus on finding popular routes and recommending suitable routes for the users based on the historical movements of users between different Point of Interests (POIs). However, users often spend most of their time around different POIs (e.g., Colosseo) and less time traveling between POIs. Thus, existing methods fail to capture the detailed movement of users around a POI, which we call Region of Interest (ROI). A major challenge of identifying patterns of routes inside an ROI comes from the inaccurate and incomplete data of user trajectories. In this paper we propose a novel technique to find the most popular path within an ROI from historical trajectory data by rephrasing trajectories into smaller part and eliminating noisy points from trajectories. We then devise an algorithm to produce the most popular path inside each ROI. We perform experiments on a real dataset extracted from Flickr to show the effectiveness of our approach.