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New analysis finds speed cameras may create bad driving behaviour

Using more than one billion miles of driving behaviour data, collected over three years (2011-2014) and including 8,809 separate journeys in 5,353 vehicles, Wunelli, a LexisNexis company, has revealed the most frequent braking black spots across the UK created by speed cameras, based on motorists braking excessively just before speed cameras to avoid being caught. Eighty per cent of all the UK speed cameras investigated had hard braking activity, with braking increasing six fold on average at these loca
October 28, 2015 Read time: 4 mins
Using more than one billion miles of driving behaviour data, collected over three years (2011-2014) and including 8,809 separate journeys in 5,353 vehicles, Wunelli, a LexisNexis company, has revealed the most frequent braking black spots across the UK created by speed cameras, based on motorists braking excessively just before speed cameras to avoid being caught.

Eighty per cent of all the UK speed cameras investigated had hard braking activity, with braking increasing six fold on average at these locations. Wunelli defines a hard braking event as a change in speed of 6.5+ mph over a one-second time period, which is enough to propel a bag on the passenger seat into the foot-well.

Wunelli's key findings include: Eighty per cent of the UK speed cameras investigated are creating braking black spots; motorists hard braking activity increases on average by 689 per cent at these locations. The analysis also found that women exceed the speed limit 12 per cent less than men and hard brake 11 per cent less. In addition, motorists are most likely to speed at 5:59 am and least likely to speed at 5:16 pm, while motorists driving in 30 mph zones are found to be speeding 12 per cent of the time and at least 18 per cent over the speed limit.

Motorists in Caithness, Scotland, speed 36 per cent of the time, whilst motorists in Greater London only speed 8eight per cent of the time. A 30 per cent reduction in speeding is achieved by those provided with feedback via personal dashboards or smartphone devices

The Wunelli analysis also identified that drivers of four-wheel drive gold estate cars are typically the safest drivers as determined by fewest speeding, braking and claims events.

Paul Stacy, founding director, Wunelli said: "These findings question whether speed cameras are serving their purpose as a road safety tool or whether they are instead encouraging poor driving behaviour.

"The breadth and depth of data Wunelli can now aggregate and study means driving behaviour analysis is now possible on a range of vehicle factors, if you wanted to identify which car driver is least likely to be involved in an accident based on the driving behaviour we have recorded, they would be the owner of an estate car, gold colour, four-wheel drive and about £10k in value. Of course, that's not to say gold-coloured 4WD estate owners are all safe drivers."

The analysis also uncovered that residential roads (under 40mph) have significantly more accidents per mile than roads with higher speed limits. This type of information is not only hugely valuable to insurers but immensely important for motor manufacturers and the designers of the cars and the road networks of the future.

Ash Hassib, SVP and GM, Auto and Home Insurance, LexisNexis Risk Solutions, adds, "We have collected over a billion miles of driving behaviour data, and our analysis has provided some extremely important insights. We are building upon these insights to show the potentially dangerous effects of certain speed deterrents on driving behaviour as speeding drivers take erratic measures, such as braking harshly, to avoid being penalised. This supports the theory of accidents being 'wake up calls' to drivers to take more care and proof that a carrot rather than a stick approach works in improving driving behaviour."

The analysis was based on speed camera location data of 2012.The results are of over 5500 cameras analysed by Wunelli, the 10 speed cameras were selected on the following criteria: Camera was not a red light camera nor at a railway level crossing; Braking events near the camera had a low propensity to be caused by other local features, such as side streets, intersections; There was a significantly higher proportion of braking events within 0-50m of the camera compared to 50-100m of the camera (braking event cluster); More than 25 braking events were recorded within 50m of the camera.

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