We are honoured and delighted to be chosen for a Queen’s Award for Enterprise in the Innovation category for our traffic insights Artificial Intelligence technology which supports government, regional bodies and local councils in traffic optimisation and transport infrastructure.
The Queen’s Award for Enterprise, now in its 55th year, is widely recognised as the highest official UK award for British businesses. The accolade is given to businesses and organisations who excel at innovation, international trade, sustainable development or promoting opportunity. We were recognised with this year’s Innovation Award for our AI technology which anonymously captures and classifies live transport usage, helping make cities smarter, safer and more sustainable.
Our sensors use proprietary machine learning algorithms to enable accurate detection, classification and analysis of different transport modes, traffic flow and travel patterns on how road users behave. This anonymous data helps to improve urban infrastructure by providing detailed insight into how roads are used, and is enabling the development of the next generation of future city-wide traffic control systems.
We have been using this anonymous data to help councils implement and facilitate ‘active travel’ schemes (such as walking and cycling). Our AI-based ‘Smart Junctions’ traffic signal system also reduces queuing, congestion and emissions and allows cities to prioritise sustainable modes of travel, including cycling and public transport.
Mark Nicholson, our CEO and Co-Founder commented, “It is an honour to receive the prestigious Queen’s Award for Enterprise and we are extremely proud to be recognised for our work in making cities smarter, safer and more sustainable. As we emerge from the pandemic, cities need data to evolve and adapt their transport networks to the new normal. We are excited to support transport authorities in this transition with privacy-centric datasets and tools.”
Our sensors have been deployed in over 40 towns and cities across the UK, including Manchester, Cambridge and Sunderland, and provide detailed and anonymous data 24/7.