Detecting Human Activity Profiles with Dirichlet Enhanced Inhomogeneous Poisson Processes

Shimosaka, Masamichi; Ishino, Takahito; Noguchi, Hiroshi; Sato, Tomomasa; Mori, Taketoshi

doi:10.1109/icpr.2010.1066

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Similar to the factorization method, some studies have focused on the fluctuation of human activity using mixture models [14,15,1,27,31,19,18]. Mixture models have high power of expression with non-linear distributions.…”

Section: Related Work and Their Limitationsmentioning

confidence: 99%

“…Mixture models have high power of expression with non-linear distributions. Therefore, the target data in previous research project are quite varied, e.g., people-count data at a building entrance and traffic-count data on a freeway [14,15], professional basket ball players' shot attempts [18], going-out behavior [27,31] and number of pedestrians in a specific area [19]. However, mixture models are difficult to use for predictive analysis since the load factor to switch the appropriate latent patterns are not determined in priori.…”

Section: Related Work and Their Limitationsmentioning

confidence: 99%

“…We also compared our proposed model with mixture models. Specifically, we used the Dirichlet-Poisson mixture model proposed by Shimosaka et al [27].…”

Section: Comparative Modelsmentioning

confidence: 99%

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Forecasting urban dynamics with mobility logs by bilinear Poisson regression

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Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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Understanding people flow in a city (urban dynamics) is of great importance in urban planning, emergency management, and commercial activity. With the spread of smart devices, many studies on urban dynamics modeling with mobility logs have been conducted. It is predictive analysis, not analysis of the past, that enables various applications contributing to a more prosperous society. To deal with the non-linear effects on urban dynamics from external factors, such as day of the week, national holiday, or weather, we propose a low-rank bilinear Poisson regression model, for a novel and flexible representation of urban dynamics predictive analysis. The results obtained from an experiment with one year's worth of mobility records suggest the high prediction accuracy of the proposed model. We also introduce the following applications: regional event detection via irregularities, visualization of urban dynamics corresponding to urban demographics, and extraction of urban demographics of unknown point of interests.

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