How to measure distracted driving risk?

Driver distraction is lately becoming a key critical road accident risk factor, equivalent or even more important than the well-known “four big road killers”: speed, drink-and-drive, non-use of seat belt and helmet. Some argue that mobile phone use (talking, texting and browsing) is a key reason why road fatalities do not decrease anymore in developed countries, despite all other efforts to improve road safety.

However, monitoring driver distraction is not that easy and its impact on accident probability is not always neither straightforward nor easy to quantify, despite the increasing attention in research during the last decade. Research has demonstrated that mobile phone use in general leads to lower speeds and longer headways representing a potential positive impact on safety, which is however well outweighed by the very slow driver reaction time during an incident, increasing thus seriously the accident probability.

It is clear that mobile phone use has a direct impact on driver behaviour, however what needs to be further specified and quantified is the impact of the distracted driver behaviour on accident probability. Naturalistic driving studies (recording driver and vehicle behaviour in instrumented vehicles) might one of the most appropriate methodologies to capture the effect of driver distraction and inattention on driver behaviour and safety. However, naturalistic driving studies and the respective big data analyses are highly time and resources consuming and ways to optimise the effort should be further explored.

So far, research findings on driver distraction risks are mostly converging across the globe (at least at the OECD countries), however, there is a clear need to address common solutions to common problems, involving a combination of driver education, rules enforcement and technology solutions.

Contribution at SWOV Conference on Naturalistic Driving Research, Hague, June 2017

By | 2017-11-25T16:20:22+00:00 June 7th, 2017|Categories: General|Tags: , , , , |

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