Trajectory Prediction in Campus Based on Markov Chains

• Published in: Big Data Computing and Communications Vol. 9784; pp. 145 - 154
• Main Authors: Wang, Bonan, Hu, Yihong, Shou, Guochu, Guo, Zhigang
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Activity regularity; Markov chain; Network hardware; Trajectory prediction; Wireless data analysis system
• ISBN: 9783319425528; 3319425528
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-42553-5_13

In this paper, we present a model of predicting the next location of a student in campus based on Markov chains. Since the activity of a student in campus is closely related to the time at which the activity occurs, we consider the notion of time in the prediction algorithm that we coined as Trajectory Prediction Algorithm (TPA). In order to evaluate the efficiency of our prediction model, we use our wireless data analysis system to collect real spatio-temporal trajectory data in campus for more than seven months. Experimental results show that our TPA has increased the accuracy of prediction for over 30 % than the original Markov chain.