Big data could improve road safety says Microlise

Big improvements in the way huge amounts of data is analysed could soon lead to detailed risk ratings for specific roads at different times of the day, according to Matthew Hague, executive director – product strategy at Microlise.

Speaking at the IRTE conference, Hague predicted that fleets will harness the power of 'big data' in 2018 thanks to advanced data analysis techniques and growing demand for the capabilities they enable.

“We have spent a phenomenal amount time and resource over the last two years, running a number of successful projects involving government funding,” said Hague. “Big data is a bit of a buzz word, but it is one of those things that will really drive our product offering forward in the coming months and years.”

Microlise captures over data on over 7bn road miles each year, but gathering useful information from this ocean of data remains a challenge. "But new technologies and techniques are enabling us to scale up and process large data sets in a quick and agile way,” said Hague.

In 2014, with partner the University of Nottingham, Microlise was awarded funding of almost £360,000 from the Technology Strategy Board (now Innovate UK) to look at how to obtain value from the high volumes of complex data generated from real-time telematics.

“We completed our work with the University of Nottingham last year and will soon be in a position to productise new and innovative tools and solutions that will create new value propositions within the transport and logistics sector,” said Hague.

According to Microlise, the first uses of this new big data resource will be predictive analytics for vehicle health, improving hazard awareness and briefing drivers.

“By using the anonymous data we capture every day then overlaying the government’s annualised accident black spot and crime data, we can very accurately predict risk while taking time of day and weather conditions into account,” said Hague.

According to Hague, it will soon be possible to rank routes according to risk, and even to alert drivers to specific risks along their route as they approach them.