Location Prediction Based on Behavior Semantic Mining

• Main Authors: Huei-Yu Lung, 龍徽猷
• Other Authors: Bi-Ru Dai
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/55113247802342149609

碩士 === 國立臺灣科技大學 === 資訊工程系 === 101 === Predicting movements of mobile users has become more and more popular because trajectory data collecting is easy nowadays. Most of those prediction techniques need geographic pattern matching of users’ trajectory data, so it is possible that those techniques cannot work in a place where the user has never been before. In this paper, we propose an approach based on transportation mode and behavior semantic features to predict the next location of the users’ movement. First, we identify the users’ transportation mode to get a sequential data of the users’ motion mode. Then, we get the semantic meaning as behavior semantic features from the places where users have stopped and visited for a while. We find the relationship between the transportation mode and behavior semantic features to predict the next location based on the Hidden Markov model. We use real world data for our experiment to demonstrate the effectiveness of our approach.