Location and Motion Prediction of Consumers in a Large Shopping Mall
Zeyu Zhang, Weiping Zhu · 2017
It is important to predict consumers' location and motion in a large shopping mall to provide them better service. When a consumer passes regions of a shopping mall, his/her moving trace can be recorded for prediction. Existing approaches cannot be directly used to fulfill such task because handling the ordered region sequences is quite challenging. In this paper, we propose an improved Apriori algorithm called AprioriOS (Apriori for Ordered Sequences) to solve this problem. Using this method, association rules are mined out from ordered region sequences and then used to predict future locations of consumers. We can predict more than one regions that a consumer may pass in future. We also design an association rule querying method and a tree storage structure for location prediction. And we propose a motion prediction method based on 3-axis accelerometer or RFID to predict motions of consumers. Based on the proposed we develop an location and motion prediction system for shopping malls. Our simulation results show that the system is effective in terms of the accuracy of prediction.