Abstract
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.
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Acknowledgment
This work is supported by National Natural Science Foundation of China (Grant No. 61471053).
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Wang, B., Hu, Y., Shou, G., Guo, Z. (2016). Trajectory Prediction in Campus Based on Markov Chains. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_13
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DOI: https://doi.org/10.1007/978-3-319-42553-5_13
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