Congestion analysis across locations based on wi-fi signal sensing

Atsushi Shimada, Kaito Oka, Masaki Igarashi, Rin-Ichiro Taniguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationPattern Recognition Applications and Methods - 6th International Conference, ICPRAM 2017, Revised Selected Papers
EditorsAna Fred, Maria De Marsico, Gabriella Sanniti di Baja
PublisherSpringer Verlag
Pages204-221
Number of pages18
ISBN (Print)9783319936468
DOIs
Publication statusPublished - Jan 1 2018
Event6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017 - Porto, Portugal
Duration: Feb 24 2017Feb 26 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10857 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
CountryPortugal
CityPorto
Period2/24/172/26/17

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Shimada, A., Oka, K., Igarashi, M., & Taniguchi, R-I. (2018). Congestion analysis across locations based on wi-fi signal sensing. In A. Fred, M. De Marsico, & G. S. di Baja (Eds.), Pattern Recognition Applications and Methods - 6th International Conference, ICPRAM 2017, Revised Selected Papers (pp. 204-221). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10857 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93647-5_12