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Massive Crowd Monitoring System

Technology Overview

A real-time decision support system that monitors signals from smart phones and devices in large area. This potentially gives new insights to crowd in large areas, providing analytics for retailers, advertisers, event organisers and Smart- and Safe Cities by counting the number of mobile phones in the area.

Using a variety of sensors based on software-defined radio, this location-based sensing technology is capable of:

  • measuring mobile signals transmitted over WiFi and cellular networks,
  • locate and track mobile phones, and
  • create statistical maps based on this data.

Technology Features & Specifications

The solution consists of a package of sensors and monitoring software tools. Variety of sensors includes a combination of long and short range, indoor and outdoor, mobile and WiFi detectors to be located in selected spots to detect anonymous data transmitted over cellular and WiFI networks. Gathered data is then relayed to cloud-based servers through secure communication channels. 

Crowd analytics is accessible through a password-protected web-based dashboard and made available for integration with 3rd party systems via APIs.

Analytics insights includes:

  • Footfall
  • Duration of stay
  • Returning visitors
  • Flow patterns and mobility
  • Tourist (nationality) rate analysis
  • Detection of approaching/exceeding limits on crowd size

Data analytics available in real-time and historical modes, providing trending analysis by the hour or customisable period of duration.

Potential Applications

This location-based sensing is suitable for wide range of use-cases, from:

  • patron footfall analysis in retails
  • monitoring of events and exhibitions for crowd control,
  • Smart Cities applications with fusion of environmental data sources, and
  • analysis of urban transportation

From marketing to safety and security purposes, this technology is capable of:

  • indicating footfall in the target area, 
  • calculating potential footfall in each existing location with analytical model, 
  • understanding of crowd behavioural patterns,
  • highlighting irregular events that are not common for specific location/date/time,
  • generating alerts whenever customisable threshold on crowd size are approached/exceeding according to the security plan,
  • etc.

Market Trends and Opportunities

According to research by Frost and Sullivan, amid increasing competition beween retailers to gain and keep customer loyalty, more retailers turn to use of Big Data analytics for actionable insight to customer behaviour. Customer purchase behavior and inventory optimization are popular applications of Big Data analysis in retail.

Realtime data and historical trends provides additional information to city planners and event organisers for more effective planning and event organization. 

Customer Benefits

Signals from personal mobile phone transmission is prevalent in urban areas. Counting each unique signals from each phone and aggregating them through data analytics gives new insights to crowd behavior with the potential to improve retail sales, advertising, event management and aids city planning and crowd control.

Data analytics allows business owners or event organisers to extract useful insights that explain how customers/attendees behave: whether they are returning to the shop/event or staying longer, how they move within the premises, if they are focusing on a specific area inside the exhibition hall, liking the food court more than electronic stands.

With these indications, it’s possible to monitor areas where crowd's buiding up, understand which ways are the most taken by commuters and hence that need bus stops, elaborate offers for tourists based on where they concentrate most, create promotions and marketing campaigns that are tailored upon customers’ habits. Then, if you could correlate these behavioural patterns with personal information of the customers, got from the event's app he/she downloaded opting in, the retailer’s relation with the client would be way more personal and eventually fruitful: clustering customers upon habits and presenting them deals related to their preferences would be a win-win situation for retailers and customers.

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