research-article

How does coffee shop get crowded?: using WiFi footprints to deliver insights into the success of promotion

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UbiComp '17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers

Pages 421 - 426

https://doi.org/10.1145/3123024.3124418

Published: 11 September 2017 Publication History

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Abstract

Real time people density estimation is one of the biggest challenges of today research. This information can be applied to various urban applications such as advertising, traffic planning, and resource management. The recent researches have demonstrated on crowded estimation using various cutting-edge technologies to solve this problem. Since WiFi access points already exist in the major buildings and shops. They become a great tool to estimate the density of people. This research explores the opportunities to use existing access points in order to estimate density of people in the real world environment. WiFi probe request monitoring technique is used to identify the number of the customer's visit to a coffee shop. The result shows that during weekdays, the number of customers in promotion period is 30.43% greater than non-promotion period.

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Cited By

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Chen MYang XJin YZhou M(2021)Dynamic Time Warping Based Passive Crowd Counting Using WiFi Received Signal StrengthArtificial Intelligence and Security10.1007/978-3-030-78612-0_54(667-677)Online publication date: 9-Jul-2021
https://doi.org/10.1007/978-3-030-78612-0_54
Jahan FSun WNiyaz QAlam M(2019)Security Modeling of Autonomous SystemsACM Computing Surveys10.1145/333779152:5(1-34)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3337791
Winter STomko MVasardani MRichter KKhoshelham KKalantari M(2019)Infrastructure-Independent Indoor Localization and NavigationACM Computing Surveys10.1145/332151652:3(1-24)Online publication date: 18-Jun-2019
https://dl.acm.org/doi/10.1145/3321516
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Index Terms

How does coffee shop get crowded?: using WiFi footprints to deliver insights into the success of promotion
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

UbiComp '17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers

September 2017

1089 pages

ISBN:9781450351904

DOI:10.1145/3123024

General Chairs:
Seungyon "Claire" Lee
Google
,
Leila Takayama
UC Santa Cruz
,
Khai Truong
University of Toronto

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 September 2017

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Thammasat Young Researcher Fund 2016

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UbiComp '17

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UbiComp '17: The 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 11 - 15, 2017

Hawaii, Maui

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

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Chen MYang XJin YZhou M(2021)Dynamic Time Warping Based Passive Crowd Counting Using WiFi Received Signal StrengthArtificial Intelligence and Security10.1007/978-3-030-78612-0_54(667-677)Online publication date: 9-Jul-2021
https://doi.org/10.1007/978-3-030-78612-0_54
Jahan FSun WNiyaz QAlam M(2019)Security Modeling of Autonomous SystemsACM Computing Surveys10.1145/333779152:5(1-34)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3337791
Winter STomko MVasardani MRichter KKhoshelham KKalantari M(2019)Infrastructure-Independent Indoor Localization and NavigationACM Computing Surveys10.1145/332151652:3(1-24)Online publication date: 18-Jun-2019
https://dl.acm.org/doi/10.1145/3321516
Xiong HWu YLu Z(2019)A Survey of Group Key Agreement Protocols with Constant RoundsACM Computing Surveys10.1145/331846052:3(1-32)Online publication date: 18-Jun-2019
https://dl.acm.org/doi/10.1145/3318460
Zhang RXue RLiu L(2019)Security and Privacy on BlockchainACM Computing Surveys10.1145/331648152:3(1-34)Online publication date: 3-Jul-2019
https://dl.acm.org/doi/10.1145/3316481
Ma YZhou GWang S(2019)WiFi Sensing with Channel State InformationACM Computing Surveys10.1145/331019452:3(1-36)Online publication date: 18-Jun-2019
https://dl.acm.org/doi/10.1145/3310194
Shibata KYamamoto H(2019)People Crowd Density Estimation System using Deep Learning for Radio Wave Sensing of Cellular Communication2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)10.1109/ICAIIC.2019.8669071(143-148)Online publication date: Feb-2019
https://doi.org/10.1109/ICAIIC.2019.8669071
Depatla SMostofi Y(2018)Passive Crowd Speed Estimation and Head Counting Using WiFi2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)10.1109/SAHCN.2018.8397119(1-9)Online publication date: Jun-2018
https://doi.org/10.1109/SAHCN.2018.8397119
Tai KGoh VYap TNg H(2018)Residential Neighbourhood Security using WiFiComputational Science and Technology10.1007/978-981-13-2622-6_43(445-452)Online publication date: 28-Aug-2018
https://doi.org/10.1007/978-981-13-2622-6_43

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