Analysis of Wi-Fi-based and Perceptual Congestion

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

2017

Abstract

Conventional works for congestion estimates focus on estimating quantitative congestion (e.g., actual number of people, mobile devices, and crowd density). Meanwhile, we focus on perceptual congestion rather than quantitative congestion toward providing perceptual congestion information. We analyze the relationship between quantitative and perceptual congestion. For this analysis, 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 user-report-based congestion as a perceptual congestion measurement collected via our Web service. Base on the obtained quantitative and perceptual congestion, we investigate the relationship between these values.

References

  1. Choi, J., Hwang, H., and Hong, W. (2011). Predicting the Probability of Evacuation Congestion Occurrence Relating to Elapsed Time and Vertical Section in a Highrise Building, pages 37-46. Springer US, Boston, MA.
  2. Fukuzaki, Y., Mochizuki, M., Murao, K., and Nishio, N. (2014). A pedestrian flow analysis system using wi-fi packet sensors to a real environment. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, UbiComp 7814 Adjunct, pages 721-730, New York, NY, USA. ACM.
  3. Schauer, L., Werner, M., and Marcus, P. (2014). Estimating crowd densities and pedestrian flows using wi-fi and bluetooth. In Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MOBIQUITOUS 7814, pages 171-177, ICST, Brussels, Belgium, Belgium. ICST (Institute for Computer Sciences, SocialInformatics and Telecommunications Engineering).
  4. Weppner, J. and Lukowicz, P. (2013). Bluetooth based collaborative crowd density estimation with mobile phones. In Pervasive Computing and Communications (PerCom), 2013 IEEE International Conference on, pages 193-200.
  5. Xi, W., Zhao, J., Li, X. Y., Zhao, K., Tang, S., Liu, X., and Jiang, Z. (2014). Electronic frog eye: Counting crowd using wifi. In IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, pages 361-369.
  6. Yaik, O. B., Wai, K. Z., Tan, I. K., and Sheng, O. B. (2016). Measuring the accuracy of crowd counting using wifi probe-request-frame counting technique. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(2):79-81.
Download


Paper Citation


in Harvard Style

Igarashi M., Shimada A., Oka K. and Taniguchi R. (2017). Analysis of Wi-Fi-based and Perceptual Congestion . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 225-232. DOI: 10.5220/0006206102250232


in Bibtex Style

@conference{icpram17,
author={Masaki Igarashi and Atsushi Shimada and Kaito Oka and Rin-ichiro Taniguchi},
title={Analysis of Wi-Fi-based and Perceptual Congestion},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={225-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006206102250232},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Analysis of Wi-Fi-based and Perceptual Congestion
SN - 978-989-758-222-6
AU - Igarashi M.
AU - Shimada A.
AU - Oka K.
AU - Taniguchi R.
PY - 2017
SP - 225
EP - 232
DO - 10.5220/0006206102250232