Many studies related to congestion analysis focus on estimating quantitative values such as actual number of people, mobile devices, and crowd density. In contrast, we focus on perceptual congestion rather than quantitative congestion; however, we also analyze the relationship between quantitative and perceptual congestion. We construct a system for estimating and visualizing congestion and collecting user reports about congestion. We use the number of mobile devices as quantitative congestion measurements obtained from Wi-Fi packet sensors and a user report-based congestion as a perceptual congestion measurement collected via our Web system. In our experiments, we investigate the relationship between these values. In addition, we apply Non-negative Tensor Factorization to extract latent patterns between locations and congestion. These latent features help us to understand the relationship of the characteristics among the locations.