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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Foursquare Dataset. https://sites.google.com/site/yangdingqi/home/foursquare-dataset/. Accessed 22 August 2016.
- 2.
Actually, we stored hash values to the database instead of MAC addresses because of privacy issues.
References
Bro, R.: PARAFAC. Tutorial and applications. Chemometr. Intell. Lab. Syst. 38, 149–171 (1997)
Choi, J., Hwang, H., Hong, W.: Predicting the probability of evacuation congestion occurrence relating to elapsed time and vertical section in a high-rise building. In: Peacock, R., Kuligowski, E., Averill, J. (eds.) Pedestrian and Evacuation Dynamics, pp. 37–46. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-9725-8_4
Fukuzaki, Y., Nishio, N., Mochizuki, M., Murao, K.: A pedestrian flow analysis system using Wi-Fi packet sensors to a real environment. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing (2014)
Fukuzaki, Y., Mochizuki, M., Murao, K., Nishio, N.: 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, pp. 721–730. UbiComp 2014. Adjunct, ACM, New York (2014). https://doi.org/10.1145/2638728.2641312
Heikkilä, J., Silvén, O.: A real-time system for monitoring of cyclists and pedestrians. Image Vis. Comput. 22, 563–570 (2004)
Hsieh, H.P., Li, C.T., Lin, S.D.: Exploiting large-scale check-in data to recommend time-sensitive routes. In: ACM SIGKDD International Workshop on Urban Computing, UrbComp 2012 (2012)
Sahebi, S., Lin, Y.-R., Brusilovsky, P.: Tensor factorization for student modeling and performance prediction in unstructured domain. In: 9th International Conference on Educational Data Mining, EDM 2012 (2016)
Igarashi, M., Shimada, A., Oka, K., Taniguchi, R.: Analysis of Wi-Fi-based and perceptual congestion. In: 6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017, February 2017
Jung, E.J., Lee, J.H., Yi, B.J., Park, J.Y., Yuta, S., Noh, S.T.: Development of a laser-range-finder-based human tracking and control algorithm for a marathoner service robot. IEEE/ASME Trans. Mech. 19, 1963–1976 (2014)
Oka, K., Igarashi, M., Shimada, A., Taniguchi, R.: Extracting latent behavior patterns of people from probe request data: a non-negative tensor factorization approach. In: 6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017, February 2017
Schauer, L., Werner, M., Marcus, P.: Estimating crowd densities and pedestrian flows using Wi-Fi and Bluetooth. In: 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MOBIQUITOUS 2014 (2014)
Weppner, J., Lukowicz, P.: Bluetooth based collaborative crowd density estimation with mobile phones. In: 2013 IEEE International Conference on Pervasive Computing and Communications, PerCom, pp. 193–200, March 2013
Xi, W., Zhao, J., Li, X.Y., Zhao, K., Tang, S., Liu, X., Jiang, Z.: Electronic frog eye: counting crowd using WiFi. In: IEEE Conference on Computer Communications, INFOCOM 2014, pp. 361–369. IEEE, April 2014
Yaik, O.B., Wai, K.Z., Tan, I.K., Sheng, O.B.: Measuring the accuracy of crowd counting using Wi-Fi probe-request-frame counting technique. J. Telecommun. Electron. Comput. Eng. (JTEC) 8(2), 79–81 (2016)
Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Towards mobile intelligence: learning from GPS history data for collaborative recommendation. Artif. Intell. 184, 17–37 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Shimada, A., Oka, K., Igarashi, M., Taniguchi, Ri. (2018). Congestion Analysis Across Locations Based on Wi-Fi Signal Sensing. In: De Marsico, M., di Baja, G., Fred, A. (eds) Pattern Recognition Applications and Methods. ICPRAM 2017. Lecture Notes in Computer Science(), vol 10857. Springer, Cham. https://doi.org/10.1007/978-3-319-93647-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-93647-5_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93646-8
Online ISBN: 978-3-319-93647-5
eBook Packages: Computer ScienceComputer Science (R0)