隨著行動裝置的普及，已經有大量GPS軌跡資料的產生。行動服務供應商通常會利用使用者行動裝置上所記錄的位置資訊來提供個人化服務。近幾年對於利用軌跡資料的研究議題中，大部分針對地點預測和地點推薦。然而在停留時間預測的議題較少被注意到。若利用行動裝置可以精準預測使用者目前位置所停留的時間，就可以更貼近使用者想要的服務。此篇論文我們整合地理、時間來了解使用者移動行為，提出停留時間預測機制。首先，為了評估停留時間以及過濾一些雜訊資料，會把軌跡資料標記出停留時間和速度。然後，我們基於最長共同子序列的方法整合地理、抵達時間兩種特徵來計算出軌跡相似度。最後，根據軌跡相似度納入K最近鄰居方法找出K個最相似的軌跡，再從每個軌跡中與目標位置最近的地方之停留時間當作預測值。

With the popularity of mobile devices, a large number of GPS data is generated. Mobile service providers and manufacturers continue to provide individual services that take advantage of the location information associated with devices for users. In recent years, most researches of GPS trajectories focus on location prediction and recommendation. However, the issue of stay time prediction draws less attention. The individual services of user can be dramatically improved if a mobile device can predict how long a mobile user will stay at the current location. Therefore, in this paper, we propose an approach to predict how long a mobile user will stay at the current location. First, we label stay time and speed of every GPS trajectory data for evaluation and filter some noise data. Then, we implement a trajectory similarity measure based on Longest Common Sequence (LCSS) algorithm which integrates the information of the geographical and time features. Finally, we adopt K Nearest Neighbors (KNN) algorithm according trajectory similarity to find K most similar trajectories and derive stay time prediction value of nearest points compared to target point according to every k trajectory.

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