Predicting user behavior makes it possible to provide personalized services. Destination prediction (e.g. predicting a future location) can be applied to various practical applications. An example of destination prediction is personalized GIS services, which are expected to provide alternate routes to enable users to avoid congested roads. However, the destination prediction problem requires critical trade-offs between timing and accuracy. In this paper, we focus on early destination prediction as the central issue, as early recognition in destination prediction has not been fully explored. As an alternative to the traditional two basic approaches with trajectory tracking that narrow down the candidates with respect to the trip progress, and Next Place Prediction (NPP) that infers the future location of a user from user habits, we propose a new probabilistic model based on both conventional models. The advantage of our model is that it drastically narrows down the destination candidates efficiently at the early stage of a trip, owing to the staying information derived from the NPP approach. In other words, our approach achieves high prediction accuracy by considering both approaches at the same time. To implement our model, we employ SubSynE for state-of-the-art prediction based on trajectory tracking as well as a multi-class logistic regression based on user contexts. Despite the simplicity of our model, the proposed method provides improved performance compared to conventional approaches based on the experimental results using the GPS logs of 1,646 actual users from the commercial services.

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Thanks to the recent popularity of GPS-enabled mobile phones, modeling people flow or population dynamics is attracting a great deal of attention. Advances in methods where regular population patterns with respect to factors such as holidays or weekdays are extracted have provided successful results in irregularity detection. With large-scale crowded events such as fireworks, it is crucial that there be enough time to take countermeasures against the irregular congestion, i.e., irregularity prediction. It remains a tough challenge to predict population from GPS trace logs with existing methods. To tackle this problem, we focus here on route search logs, since aggregation of the location-oriented queries of individual plans serves as a mirror of short-term city-scale events, in contrast to GPS mobility logs. This paper presents a brand new framework for city-scale event prediction: CityProphet. By our observation of data where the route search logs related to a future event are in most cases repeatable and accumulated in proportion as the event draws near, we are able to leverage the divergence between the above two properties to predict city-scale irregular events. We demonstrate through experiments using the transit app logs of over 370 million queries that our approach can successfully predict city-scale crowded events one week in advance.

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