Pedestrian-movement prediction based on mixed Markov-chain model
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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In this paper, we discuss statistical analysis of human trajectories measured by GPS-like positioning devices. Our goal is to develop a system of trajectory analysis that distributes information optimized for each user. For such a system, we need a method to estimate a user's status from his/her trajectories. First, a trajectory needs to be divided into short temporal segments, which will be matched to action model patterns, to estimate a user's status. Second, we tried dividing actual human trajectories using a conventional trajectory-clustering method. Moreover, we adjusted parameters of the trajectory clustering by using information criteria experimentally. After the experiment, we confirmed that only a criterion in which noise data are counted worked well. However, we also confirmed that the number of clusters generated by the method is too small. Therefore, we conclude that an improvement in deciding which data are noise in trajectory clustering is necessary for estimating the status of users.