Real-time destination prediction for mobile users

Abstract

Abstract. The number of GPS trajectories recorded daily has been continuously growing in the recent years and new methods to analyse such big data are surfacing all the time. In this paper, we focus on destination prediction, which is useful in various applications like hazard detection and advertisement. We proposed a real-time method for destination prediction of moving users. It uses the current movement trajectory of the user together with historical and regional information to make an accurate prediction. The method is efficient because we can rapidly compute features with the help of spatial and non-spatial indexing methods. We tested the method with real trajectories collected by Mopsi users. The success rate of the method is up to 65 % depending on the length of the recorded trajectory so far, i.e. how long the user has been on move. To our knowledge, this is the first real-time system capable of such success.

Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities

“…Waga et al considered all these sources, including user interactions by incorporating POIs determined from photos taken by users [49]. It is also possible to predict POIs based on a user's trajectory and proximity, as proposed by Mariescu-Istodor et al [50]. Twitter does not provide information on the POIs visited by users, so it has to be inferred from the available data.…”