Using Artificial Intelligence to help improve Road Quality

Data to Transform Operation

Potholes are a major issue for local councils and road users across the UK and Ireland, causing damage vehicles, injuries to passengers and impacting on local economic performance. According to the Asphalt Industry Alliance (AIA) most recent Annual Local Authority Road Maintenance (ALARM) survey, there are 7,240 fewer miles of road reported to be in good condition, and 1,100 more miles of roads classed as poor across England and Wales. The survey also found that claims for road user compensation against local authorities in England cost a total of £22.5 million in 2018/2019, of which £14.7 million related to staff costs.

Developed using advanced Transfer Learning techniques, DATO is a hybrid data analytics solution, that utilises machine learning, image detection technologies and accelerometer data to identify potholes in real-time and predict road quality over time. The intelligence gathered is then presented to users via an intuitive, no-code dashboard.

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