Indoor Trajectory Prediction for Shopping Mall via Sequential Similarity

Abstract

With the prevalence of smartphones and the maturation of indoor positioning techniques, predicting the movement of a large number of customers in indoor environments has become a promising and challenging line of research in recent years. While most of the current predicting approaches that take advantage of mathematical methods perform well in outdoor settings, they exhibit poor performance in indoor environments. To solve this problem, in this study, a sequential similarity-based prediction approach which combines the spatial and semantic contexts into a unified framework is proposed. We first present a revised Longest Common Sub-Sequence (LCSS) algorithm to compute the spatial similarity of the indoor trajectories, and then a novel algorithm considering the indoor semantic R-tree is proposed to compute the semantic similarities; after this, a unified algorithm is considered to group the trajectories, and then the clustered trajectories are used to train the prediction models. Extensive performance evaluations were carried out on a real-world dataset collected from a large shopping mall to validate the performance of our proposed method. The results show that our approach markedly outperforms the baseline methods and can be used in real-world scenarios.