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Although people flow analysis is widely studied because of its importance, there are some difficulties with previous methods, such as the cost of sensors, person re-identification, and the spread of smartphone applications for collecting data. Today, Probe Request sensing for people flow analysis is gathering attention because it conquers many of the difficulties of previous methods. We propose a framework for Probe Request data analysis for extracting the latent behavior patterns of people. To make the extracted patterns understandable, we apply a Non-negative Tensor Factorization with a sparsity constraint and initialization with prior knowledge to the analysis. Experimental result showed that our framework helps the interpretation of Probe Request data.

Extracting Latent Behavior Patterns of People from Probe Request Data: A Non-negative Tensor Factorization Approach