Case Study: Guildford

In Guildford, we have built the world’s largest multi-asset parking and retail monitoring system. Sensors in car parks and key shopping streets are delivering live information on the parking and retail environments, 24 hours a day.


Recognising the global trend for urbanisation, GEOmii looked to building a City Scale Technology and Business Model Demonstrator for novel urban technologies in the UK. Following over a year of negotiations with several local authorities, Guildford was chosen as our first location.

A large market town in Surrey with a population of 96,000, Guildford has a vibrant centre with 468 retailers and several large employers. Although not a city per se, it experiences many of the common problems of urban areas with road congestion, poor air quality and a less than ideal distribution of parking spaces.

The aim was to monitor car parking spaces around town and shopper movements on two main shopping streets: High Street and North Street. This data was to be used by a range of applications that allow shoppers to easily find available parking spaces, retailers to gain valuable insights and the local authorities to better understand Guildford’s needs and to develop and maintain a thriving town centre.

Parking Management

As a first stage in the development of the City Scale Demonstrator, GEOmii worked with Guildford Borough Council and Surrey County Council to explore the potential of our GEOmii Parking Platform (the map below shows our current live parking data feed from Guildford), which we developed as part of a £1m Innovate UK funded project. Following the agreement of the local authorities we have installed on street parking space sensors across the centre of the town, off street sensors in the town’s car parks and connected to existing car park systems. The system uses fixed parking space sensors for high frequency areas, off street car park counters and, for wide area coverage, our own vehicle based mobile parking space sensor system.

In total, we monitor over 5,500 spaces and provide live parking space availability data for the vast majority of on street, off street and park and ride parking spaces. Our fault tolerant prediction analytics technology allows us to reliably predict where parking spaces will be in the future, allowing drivers to better plan their journeys, save time and have a better experience when visiting Guildford.

Key facts:

  • The GEOmii Parking system is now operational in Guildford, funded with £2m from Ethos and Innovate UK.
  • The system provides real time and predicted future parking space availability covering 5,404 on street and off street parking spaces.
  • Working with Clearview Intelligence and Nwave, we have installed 352 parking space sensors on the main shopping streets and 9 car park counters in car parks in Guildford.
  • We have also installed occupancy counters for the 3 park and ride car parks outside the centre of Guildford.

Retail Footfall

In addition to the GEOmii Parking Platform development, GEOmii worked with Experience Guildford (Business Improvement District) to trial a system that would provide detailed footfall monitoring across the main retail areas. These trials used Bluetooth and WiFi sensors and proven data analytics to identify how shoppers move around the retail areas to help improve the management and long term development of the town centre offer.

Key facts:

  • In collaboration with Experience Guildford we have installed a retail footfall sensor system to help retailers better understand how shoppers move around Guildford.
  • This system provides live heat maps of how people are moving around the town centre’s retail areas.
  • Retailers now have the tools to understand what is happening directly outside their store front.
  • Our retail footfall system is anonymous and uses sensors and statistical techniques to calculate accurate footfall and movement.
  • Working with local company Flexeye we have designed a data dashboard to help town managers gain the insights they need from our network of sensors, predictive analysis and an increasing amount of data from the Internet of Things (IOT).