*Mobility prediction is one of hot topics using location history information. It is useful for not onlyuser-level applications such as people finder and recommendation sharing service but also for system-levelapplications such as hand-off management, resource allocation, and quality of service of wireless services. Most of current prediction techniques often use a set of significant locations without taking into accountpossible location information changes for prediction. Markov-based, LZ-based and Prediction by PatternMatching techniques consider interesting locations to enhance the prediction accuracy, but they do notconsider interesting location changes. In our paper, we propose an algorithm which integrates the changingor emerging new location information. This approach is based on Active LeZi algorithm, but both of newlocation and all possible location contexts will be updated in the tree with the fixed depth. Furthermore,the tree will also be updated even when there is no new location detected but the expected route is changed. We find that our algorithm is adaptive to predict next location. We evaluate our proposed system ona part of Dartmouth dataset consisting of 1026 users. An accuracy rate of more than 84% is achieved.