The accurate measurement of human activities and their spatial and temporal distribution is a fundamental first step towards their understanding and management. Such distributions are highly granular and accurate estimation is crucial for decision-making processes across numerous fields, including urban management, retail, transport planning, and emergency services. In the retail sector, in this case, the inference of footfall aims to identify the number of individuals passing by the shop during a given period and can provide a crucial indicator of store performance.
In partnership with the Local Data Company (LDC) this research presents the Smart Street Sensor project, a novel case of producing footfall data using Wi-Fi signals from mobile devices. The data presented is being derived from Wi-Fi sensors that have been installed in a number of retail establishments across the UK, using proprietary hardware and software developed by the LDC.
Karlo Lugomer, Balamurugan Soundararaj and Roberto Murcio