A Personal Location Prediction Method Based on Individual Trajectory and Group Trajectory

• Main Authors: Fan Li, Qingquan Li, Zhen Li, Zhao Huang, Xiaomeng Chang, Jizhe Xia
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Location prediction; individual trajectory; group trajectory; variable order markov model; prediction by partial match (PPM); probabilistic suffix tree (PST)
• Online Access: https://ieeexplore.ieee.org/document/8758816/

With the rapid development of communication technology, a large amount of spatiotemporal trajectory data has been produced. One of the critical applications of trajectory data is location prediction that is important for urban traffic planning and location-based services. Although many methods for personal location prediction have been proposed, to the best of our knowledge, some users always have sparse historical trajectory data in practical applications, resulting in poor location prediction precision. Targeting on this challenge, we propose an Individual Trajectory-Group Trajectory (ITGT) location prediction model by utilizing the pattern of group travels. First, the model performs the stay point extraction and conducts the spatial clustering to construct the clustering link. Second, Fano's inequality and clustering link are used to evaluate the predictability of location. Third, two variable order Markov models, named prediction by partial match (PPM) and probabilistic suffix tree (PST), are adopted to predict the clustering link. Finally, our approach is evaluated by using 608 712 points from 5000 volunteers at Shenzhen, China. The results show that: 1) when using individual trajectory, the PPM individual model is superior to the conventional N-order Markov model and PST individual model; 2) when all group trajectories are used, the PPM group model is not as accurate as the PPM individual model; and 3) when it classifies the group trajectory into different traffic zones, the PPM zone model is better than the PPM group model and the PPM individual model. The prediction precision of 1-3 order PPM zone model is 83.21%, 86.75%, and 87.35%, respectively, which introduces approximately 3% performance gains by utilizing the characters of traffic zone groups.