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Traffic Speed Data Investigation with Hierarchical Modeling

  • Tomonari MasadaEmail author
  • Atsuhiro Takasu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9446)

Abstract

This paper presents a novel topic model for traffic speed analysis in the urban environment. Our topic model is special in that the parameters for encoding the following two domain-specific aspects of traffic speeds are introduced. First, traffic speeds are measured by the sensors each having a fixed location. Therefore, it is likely that similar measurements will be given by the sensors located close to each other. Second, traffic speeds show a 24-hour periodicity. Therefore, it is likely that similar measurements will be given at the same time point on different days. We model these two aspects with Gaussian process priors and make topic probabilities location- and time-dependent. In this manner, our model utilizes the metadata of the traffic speed data. We offer a slice sampling to achieve less approximation than variational Bayesian inferences. We present an experimental result where we use the traffic speed data provided by New York City.

Keywords

Gaussian Process Latent Dirichlet Allocation Full Conditional Distribution Topic Probability Traffic Speed 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Nagasaki UniversityNagasakiJapan
  2. 2.National Institute of InformaticsChiyoda-ku, TokyoJapan

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