1. DARYL LLOYD Department for Transport

2. Moving Britain Ahead Using road safety data in a policy context Daryl Lloyd, Department for Transport

3. Moving Britain Ahead Overview 3  Is the Department for Transport’s need for data and analysis the same as everyone else?  Weather – why this is important  Modelling the weather effect  Drink driving  Supporting the THINK! campaigns  Young drivers  Absolute counts against rates  Use of dashboards to communicate detailed figures to non-expert users Using road safety data in a policy context

4. Moving Britain Ahead Similarities and differences in use of data 4  Lots of similarities with other organisations here:  Same underlying data (within some minor differences)  Sometimes similar strategic interest  E.g. Identifying the road users most at risk  Yet there are probably some key differences with at least some of you:  DfT has no operational responsibilities  Therefore little interest in understanding locations needing interventions  Never need to understand the cause and circumstances of specific accidents  Focus on strategic direction and setting overall road safety policies  Aim to show some real examples of work we have carried out over the last few months Using road safety data in a policy context

5. Moving Britain Ahead Weather – perhaps one of our biggest obsessions in recent times! 5  After a number years of falls, 2011 resulted in a sudden increase in fatalities Using road safety data in a policy context

6. Moving Britain Ahead Weather – perhaps one of our biggest obsessions in recent times! 6  After a number years of falls, 2011 resulted in a sudden increase in fatalities  We believed this to be as a result of supressed casualty numbers in 2010 owning to the snow  But we failed to highlight this in the 2010 publications  Therefore it looked a bit like an “excuse” when we suggested the explanation in the 2011 publication  Hypothesis only – no empirical evidence  New approach is to highlight what is happening in-year Using road safety data in a policy context

7. Moving Britain Ahead A model to explain how weather is affecting road safety  Since 2012 we have been working on modelling the weather-effect  This is a complex area with different influences pushing and pulling in opposite directions 7 Using road safety data in a policy context ‘Bad’ weather‘Good’ weather Risk (danger on the roads and behaviour) Increase in risk for driving (difficult road conditions, poorer visibility, etc) But possible decrease in risk in behaviour (drive more slowly, take more care) Decrease in risk (in general) But possible increase in risk in behaviour? Exposure (how much people travel by each mode) Decrease in exposure (especially for vulnerable road users) Increase in exposure (more travel in general, but especially for pedal and motor cyclists)

8. Moving Britain Ahead A model to explain how weather is affecting road safety  Since 2012 we have been working on modelling the weather-effect  This is a complex area with different influences pushing and pulling in opposite directions  We have worked with the Office for National Statistics over the last year or so to construct a model which can unpick this relationship  Produced a ‘RegARIMA’ model – this is a combination of a regression model with an autoregressive integrated moving average (ARIMA) model  ARIMA modelling is a standard time series analysis tool  Equation: (don’t worry…I’m not going to talk through it!) 8 Using road safety data in a policy context

9. Moving Britain Ahead Outputs from the model 9 Using road safety data in a policy context  Indication of which months have a statistically significant relationship between weather and casualties  Broken down by road user type  Split by precipitation and temperature  Based on deviation from the long term average for the relevant weather variable (1981-2010)  Different conditions affect different users  Above average temperature or precipitation leads to more casualties.  Above average temperature or rainfall leads to fewer casualties.

10. Moving Britain Ahead10 Using road safety data in a policy context  Above average temperature or precipitation leads to more fatalities. Similarly, below average temp or prec. leads to fewer fatalities.  Above average temperature or rainfall leads to fewer fatalities. Again, below average temp. or prec. leads to more fatalities. If temperatures are warmer than the long term average then we would expect there to be more pedal and motorcyclist fatalities than usual. Colder weather than usual would result in fewer fatalities. i.e. warmth is bad for road safety If there is more precipitation than the long term average then we would expect there to be fewer motorcyclist fatalities than usual i.e. lots of rain is good for road safety No statistically significant weather effects on car user fatalities Periods without a statistically significant effect. Casualties do not seem to be affected by weather strongly enough in these periods (at least, not at the 95% confidence level)

11. Moving Britain Ahead Just how much effect on casualties does the weather have? 11  Once we take out the weather effects the rise in fatalities is 2011 is removed  Provides evidence that the hypothesis that the increase was as a result of contrasting weather conditions was correct  Even after adjustment, there was a rise in fatalities in 2014  But this was in the order of 1% rather than the published 4% Using road safety data in a policy context

12. Moving Britain Ahead Published outputs  We now publish a new set of tables giving the weather-adjusted figures  Now can separate some ‘external’ influences from real changes in road safety  We have released a number of articles and technical documents about how the model works  The methodology can be reproduced using freely-available software  Full article was part of the Reported Road Casualties in Great Britain: annual report 2014  https://www.gov.uk/government/statistics/reported-road-casualties-great-britain-annual-report-2014 https://www.gov.uk/government/statistics/reported-road-casualties-great-britain-annual-report-2014  Technical note available on DfT website  https://www.gov.uk/government/publications/road-accidents-and-safety-statistics-guidance https://www.gov.uk/government/publications/road-accidents-and-safety-statistics-guidance  Article on the topic in the Royal Statistical Society’s magazine Significance released two weeks ago 12 Using road safety data in a policy context

13. Moving Britain Ahead Drink drive accidents  Always a high priority area for the Government and other organisations  Highlighted recently by the PACTS / RAC Foundation report on Saving lives by lowering the legal drink- drive limit (authored by Prof Richard Allsop)  Also in the media recently owing the difference between the limits in Scotland and the rest of the United Kingdom  (though I should note that Northern Ireland are expecting to follow Scotland) Disclaimer  I will not discuss the rights and wrongs of lowering the limit, but will talk through some of the evidence that our road safety data holds on the matter  The current limit and any future plans to change / remain unchanged in a purely political decision and, as with all political decisions, rest with ministers 13 Using road safety data in a policy context

14. Moving Britain Ahead Gaps in the evidence  The key gap in the evidence lies in Stats19  Officers provide information on whether a driver passed or failed a breath test  However, they cannot breath test drivers who:  Die at the scene  Are too badly injured to have a breath test at the scene  Have already left the scene (e.g. in an ambulance) before an officer arrives  The data collected are binary: pass / fail  This means that Stats19 does not have any information about drivers who have blood alcohol content over 0 but under 80 mg/100ml  We do have some digital breath test data  Incomplete (around 50% of forces and E&W provide it) and not tied to Stats19 14 Using road safety data in a policy context Only a problem in Stats19: they can and do follow up where necessary for prosecution purposes, but this information does not always make it into Stats19

15. Moving Britain Ahead Digital breath tests following accidents vs other tests 15 Using road safety data in a policy context Breath alcohol content level following an accident Breath alcohol content level for breath tests for other reasons A fairly flat distribution before falling off Falling distribution

16. Moving Britain Ahead Drink drive accidents – used to inform THINK! campaigns  THINK! drink drive campaigns have had a fewer target areas recently, all of which were supported by evidence:  Morning after 16 Using road safety data in a policy context Breath test failures by hour These hours are not over- represented, but still account for 11% of failures

17. Moving Britain Ahead Drink drive accidents – used to inform THINK! campaigns  THINK! drink drive campaigns have had a fewer target areas recently, all of which were supported by evidence:  Morning after  Women drinking 17 Using road safety data in a policy context Breath test results by sex Increasing proportion of failures are women

18. Moving Britain Ahead Drink drive accidents – used to inform THINK! campaigns  THINK! drink drive campaigns have had a fewer target areas recently, all of which were supported by evidence:  Morning after  Women drinking  ‘A second drink can double your chance of being of a fatal collision’ 18 Using road safety data in a policy context

19. Moving Britain Ahead Young drivers – accidents by time of day 19 Using road safety data in a policy context  KSI accidents involving at least one young car driver  Unsurprisingly follows traffic distributions throughout the day  Most accidents at peak times  Especially evening peak  Peak periods extend on Friday and Saturday

20. Moving Britain Ahead But this becomes more interesting when you look at how over- represented some hours are (in comparison with traffic volumes) 20 Using road safety data in a policy context  The ‘peak’ travel times start to disappear – especially in the morning  Night time has a much higher risk  Early hours of the morning are often 10 times the risk rate as a ‘typical’ hour Key Hours highlighted in blue have scores below a hundred which indicates fewer accidents than expected Hours highlighted in and red have scored above a hundred which indicates more accidents than expected

21. Moving Britain Ahead Young driver casualties by severity and road type 21 Using road safety data in a policy context RuralUrbanMotorway 16,151 120 Total Casualties: Killed: 1,167 Serious: 14,864 Slight: The majority of young driver fatalities (83%) occur on rural roads. Young car drivers are also more likely to be seriously injured on rural roads compared to urban roads. Lower severity accidents are more likely to occur on urban roads. Casualties occurring on rural roads accounted for the same proportion of casualties as those occurring on urban roads. Increasing proportion of casualties on rural roads from more severe accidents: probably a function of speed

22. Moving Britain Ahead Young vs old – absolute figures 22 Using road safety data in a policy context There are more younger (17-24) driver fatalities and KSI casualties than older drivers (75+)

23. Moving Britain Ahead Young vs old – risk per mile driven 23 Using road safety data in a policy context Rapidly decreasing gap between young (17-24 year old) and older (75+ year old) driver KSI casualty rate And the older driver fatality rate is now worse than for younger drivers This leaves a question about which road user group we need to focus on in the future. Younger drivers make up a large proportion of casualties, but the gap is much smaller (or even reversed) when view as a risk rate.

24. Moving Britain Ahead Use of dashboards* to help non-expert users get into the data 24 Using road safety data in a policy context * Credit to TfL which produced the original dashboard we based this on

25. Moving Britain Ahead Summary  Although there are many functions carried out by organisations represented here today that DfT does not have to deal with itself, there are also many similarities  All the data we collect and use are imperfect  The key to good evidence is making the best use of the data we have – especially in innovative ways  There are a new set of weather-adjusted casualty tables available for users  Similarly, we have been making much better use of statistical significance tests in recent publications, which can help users know what to look at in more detail  The answer to the question can often depend on whether you are interested in absolute figures (e.g. which group represents the MOST casualties) or rates (e.g. which group is most over-represented) 25 Using road safety data in a policy context