Using Wi-Fi probe requests from mobile phones to quantify the impact of pedestrian flows on retail turnover

Trasberg, Terje; Soundararaj, Balamurugan; Cheshire, James

doi:10.1016/j.compenvurbsys.2021.101601

Cited by 12 publications

(11 citation statements)

References 56 publications

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“…This involves the use of statistical tools such as correlation coefficients and regression analysis to analyse the relationship between different variables. For example, climatic conditions have a direct impact on the growth and availability of vegetables, which may be reflected in sales data [6]. By analysing this data, it is possible to reveal how specific factors affect the sales trends of vegetables.…”

Section: Related Workmentioning

confidence: 99%

Research on Seasonal Orderliness and Linear Correlation of Vegetable Sales Data

Wen,

Zhang,

Miao

2024

AJMSS

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The key to the study of seasonal orderliness and linear correlation of vegetable sales data is to understand how sales patterns are affected by seasonal factors and the linear relationship between these patterns and other variables. Seasonal ordering reflects cyclical changes in sales data over time that are influenced by factors such as climate, traditional festivals, and consumption habits. For example, certain vegetables may sell better in the winter while others in the summer. By analysing historical sales data, time series models such as Autoregressive Moving Average (ARMA) can be used to forecast future sales trends, which in turn support inventory management and pricing strategies. In addition, linear correlation analysis helps to identify the relationship between sales volume and factors such as price, cost and market demand, as well as the interactions between different vegetables, including complementary and substitution effects. This information is crucial for supply chain optimisation, promotional campaign planning and risk management. With the development of big data and analytics, researchers can more accurately model and predict market behaviours to support strategic decision-making for vegetable producers and retailers. Research that integrates seasonal ordering and linear correlation will provide insights into the complex dynamics of the vegetable market and provide data to support market participants in developing strategies to adapt to the changing environment.

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Section: Related Workmentioning

confidence: 99%

Research on Seasonal Orderliness and Linear Correlation of Vegetable Sales Data

Wen,

Zhang,

Miao

2024

AJMSS

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“…In outdoor WiFi networks, only a small percentage of clients nowadays choose to subscribe to the service, preferring to rely on cellular telephone connectivity to access the internet. Indeed, the authors of [5] report a fivefold increase in the percentage of randomized MAC clients, from 10% to 50%, between the years 2016 and 2018. In [1], fixed-MAC clients in 2020-2021 are found not only to represent less than 10% of all PRs, but also to have significantly different PR statistics compared to randomized MAC clients.…”

Section: Related Workmentioning

confidence: 99%

“…Localization is typically performed by leveraging the physical properties of a client's WiFi signals, such as the Received Signal Strength (RSS), at several network Access Points (APs). Most systems make use of the abundant Probe Request (PR) frames emitted regularly by client devices even when not connected to a network [1][2][3][4][5]. Sophisticated WiFi client localization interfaces have now become commonplace worldwide.…”

Section: Introductionmentioning

confidence: 99%

Tools for Ground-Truth-Free Passive Client Density Mapping in MAC-Randomized Outdoor WiFi Networks

Yang

Ahriz

Denby

2023

Sensors

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In the past few years, data privacy legislation has hampered the ability of WiFi network operators to count and map client activity for commercial and security purposes. Indeed, since client device MAC devices are now randomized at each transmission, aggregating client activity using management frames such as Probe Requests, as has been common practice in the past, becomes problematic. Recently, researchers have demonstrated that, statistically, client counts are roughly proportional to raw Probe Request counts, thus somewhat alleviating the client counting problem, even if, in most cases, ground truth measurements from alternate sensors such as cameras are necessary to establish this proportionality. Nevertheless, localizing randomized MAC clients at a network site is currently an unsolved problem. In this work, we propose a set of nine tools for extending the proportionality between client counts and Probe Requests to the mapping of client densities in real-world outdoor WiFi networks without the need for ground truth measurements. The purpose of the proposed toolkit is to transform raw, randomized MAC Probe Request counts into a density map calibrated to an estimated number of clients at each position.

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“…The ability to triangulate the physical location of users and objects within these clouds (with details of their position along retail high streets), using communications between wireless devices or between devices and base stations, also presents a significant by-product of wireless technologies [82]. Retailers have used communications triangulation to develop estimates of customer footfall past stores based on communications data that are freely available within Wi-Fi clouds [83,84], as well as to develop new schemes for the delivery of geographically-curated advertising and promotions [85]. A range of location-based services (LBS) have also been pioneered to piggyback on the localization capabilities of wireless technologies.…”

Section: Location-aware Technologiesmentioning

confidence: 99%

Smart and Sentient Retail High Streets

Torrens

2022

Smart Cities

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Here, we examine the extension of smart retailing from the indoor confines of stores, outward to high streets. We explore how several technologies at the union of retail intelligence and smart city monitoring could coalesce into retail high streets that are both smart and sentient. We examine the new vantages that smart and sentient retail high streets provide on the customer journey, and how they could transform retailers’ sway over customer experience with new reach to the public spaces around shops. In doing so, we pursue a three-way consideration of these issues, examining the technology that underpins smart retailing, new advances in artificial intelligence and machine learning that beget a level of street-side sentience, and opportunities for retailers to map the knowledge that those technologies provide to individual customer journeys in outdoor settings. Our exploration of these issues takes form as a review of the literature and the introduction of our own research to prototype smart and sentient retail systems for high streets. The topic of enhancing retailers’ acuity on high streets has significant currency, as many high street stores have recently been struggling to sustain custom. However, the production and application of smart and sentient technologies at hyper-local resolution of the streetscape conjures some sobering considerations about shoppers’ and pedestrians’ rights to privacy in public.

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Using Wi-Fi probe requests from mobile phones to quantify the impact of pedestrian flows on retail turnover

Cited by 12 publications

References 56 publications

Research on Seasonal Orderliness and Linear Correlation of Vegetable Sales Data

Research on Seasonal Orderliness and Linear Correlation of Vegetable Sales Data

Tools for Ground-Truth-Free Passive Client Density Mapping in MAC-Randomized Outdoor WiFi Networks

Smart and Sentient Retail High Streets

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