Road congestion costs drivers, businesses and the economy billions each year.
With traffic levels set to increase by up to 51% by 2050, congestion is set to get worse. Alongside this, prioritising active travel, improving public transport reliability and creating more sustainable environments are also now all critical to transport policies.
As a result, there is huge pressure on highway authorities to improve the reliability of the road network and deliver decreased journey times, greener transport options and better air quality.
Real-time data insight and intelligent transportation systems are key to solving these urban mobility problems.
By providing live road usage data and AI-optimised signal control into highways operations, we enable operators to understand the current state of the network and implement traffic strategies at local and city level.
Comprehensive data inputs.
Our sensors provide anonymous real-time data feeds on counts, classifications, speeds, and vehicle journey times, along with information on queue build-up.
This wide range of data inputs is used to give algorithms a broad understanding of the current situation, that are able to adapt quickly to changing traffic conditions and efficiently implement both short-term traffic optimisations based on precise current vehicle positions, and longer-term optimisation on a local or regional level.
But this technology moves beyond just reducing congestion and journey times. Through AI and machine learning, we also have the potential to optimise signal control to prioritise for active travel modes, buses, or air quality. Contact us to find out more.