Travel destination prediction using frequent crossing pattern from driving history

The modeling of user behavior patterns for personalized information services in mobile environments has recently become a popular research theme. Most of the research aims at predicting the user's future behavior (and/or location) by extracting frequent patterns from the history of location data sequences. However, sometimes user behavior changes according to the external information such as date, time, weather, etc., and we cannot accurately predict it based on the location data sequences alone. In this paper, we propose a new travel destination prediction method including day and time as external information. First, the user's travel history information including the location, date and time is stored. Then, from the external information, time/day categories that have correlation to the user's destination based on entropy are determined. Finally, using the categories, a destination that depends on the external information can be successfully predicted. An application of the method to data collected from a car navigation system showed possibility for an improved performance comparing to the conventional methods. Higher destination prediction accuracy during the first several minutes after user's departure was reported.