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Early Change Detection Based on SpotRank

Published: 08 October 2018 Publication History

Abstract

This paper proposes a new method of early change detection for people flow analysis. Some conventional methods often focus on a single location (spot) to demonstrate how the number of people changes over time. In contrast, our proposed method takes into account the links between the spots to grasp a foretaste of congestion of a specific spot as early as possible. The main advantage of the proposed method is that it not only describes the characteristics of each spot, but also the relationships among spots, i.e., whether the connectivities are strong/weak. We introduce an idea of PageRank, which is based on a centrality of graph theory and extend that idea to represent the amount of people flow among spots. We call the extended method "SpotRank". SpotRank assigns an importance score to each spot. The score of a particular spot is calculated by the number of paths and the amount of people flow from other spots. Therefore, the more paths and people flow, the importance score (ranking) increases. The proposed method begins with the calculation of SpotRank every 10 min, followed by change detection, i.e., how much the ranking changes over time. In our experiments, we measured people flow using Wi-Fi packet sensors for a period of over 16 weeks. We confirmed the effectiveness of the proposed method, which successfully grasped a foretaste of congestion at a restaurant in our university.

References

[1]
Ahmed El-Geneidy and David Levinson. 2011. Place rank: valuing spatial interactions. Networks and Spatial Economics 11, 4 (2011), 643--659.
[2]
Yuki Fukuzaki, Masahiro Mochizuki, Kazuya Murao, and Nobuhiko Nishio. 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. ACM, 721--730.
[3]
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank citation ranking: Bringing order to the web. Technical Report. Stanford InfoLab.
[4]
Atsushi Shimada, Kaito Oka, Masaki Igarashi, and Rin-ichiro Taniguchi. 2018. Congestion Analysis Across Locations Based on Wi-Fi Signal Sensing. Pattern Recognition Applications and Methods (6 2018), 204--221.
[5]
Kenji Yamanishi and Jun-ichi Takeuchi. 2002. A unifying framework for detecting outliers and change points from non-stationary time series data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 676--681.

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cover image ACM Conferences
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
October 2018
1881 pages
ISBN:9781450359665
DOI:10.1145/3267305
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 08 October 2018

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