Large-Scale Trajectory Prediction Model Based on Prefix Projection Technique

被引：0
|
作者：
Qiao S.-J. [1 ]
Han N. [2 ]
Li T.-R. [3 ]
Li R.-H. [4 ]
Li B.-Y. [1 ]
Wang X.-T. [3 ]
Gutierrez L.A. [5 ]
机构：
[1] School of Cybersecurity, Chengdu University of Information Technology, Chengdu
[2] School of Management, Chengdu University of Information Technology, Chengdu
[3] School of Information Science and Technology, Southwest Jiaotong University, Chengdu
[4] College of Computer Science and Software Engineering, Shenzhen University, Shenzhen
[5] Department of Computer Science, Rensselaer Polytechnic Institute, New York
来源：
Han, Nan (hannan@cuit.edu.cn) | 1600年 / Chinese Academy of Sciences卷 / 28期
基金：
中国国家自然科学基金;
关键词：
Frequent sequential patterns; Markov chain; Prefix projection; Trajectory matching; Trajectory prediction;
D O I：
10.13328/j.cnki.jos.005340
中图分类号：
学科分类号：
摘要：
Smart phones, GPS equipped vehicles and wearable devices can generate a large number of trajectory data. These data can not only describe the historical trajectory of moving objects, but also accurately reflect the characteristics of moving objects. The existing trajectory prediction approaches have the following drawbacks: both prediction accuracy and efficiency cannot be guaranteed together, effective trajectory prediction is limited to road-network constrained local spatial areas, and complex and large-scale location data are difficult to process. Aiming to cope with the aforementioned problems, a prefix projection based trajectory prediction model targeting massive trajectory data of moving objects is proposed by employing the basic idea of frequent sequential patterns discovery. The new model, called PPTP (prefix projection based trajectory prediction model), includes two essential steps: (1) Discovering frequent trajectory patterns by creating projected databases and iteratively mining frequent prefix trajectory patterns from projected databases; (2) Trajectory matching by incrementally extending the postfix trajectory based on each frequent sequential pattern and outputting the longest continuous trajectory that is greater than the threshold of minimum support count. The advantages of the proposed algorithm are that it can generate long-term trajectory patterns via short frequent sequential patterns in an incremental manner, and it will not generate useless candidate trajectory sequences in order to overcome the drawback of time-intensive in discovering frequent sequential patterns. Extensive experiments are conducted on real large-scale trajectory data from multiple aspects, and the results show that PPTP algorithm has very high trajectory prediction accuracy when comparing to 1st-order Markov chain prediction algorithm and the average improvement of accuracy can reach to 39.8%. A generic trajectory prediction system is developed based on the proposed trajectory prediction model, and the complete prediction trajectories are visualized in order to provide assistance for users in path planning. © Copyright 2017, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
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页码：3043 / 3057
页数：14
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