The future of road safety analysis Department for Transport

Where are we now? Rich data landscape Arguably one of the best collection of road safety statistics in the world (STATS19 - more on this later) Lots of intelligent and passionate analysts working to make our roads safer – you! But…. We tend to look at lots of single factors in isolation We focus on trying to understand historic trends, not predict the future

Data Science? Unicorn? Unicorn = 90% horse (what we have already) Mythical creature rarely seen and understood by very few? + 10% horn (doing something new)

“Fail Fast, fail cheap” A key principle of a data science approach is “fail fast, fail cheap”. In other words test concepts and feasibility, iterate, don’t look to do big, formal projects. This means looking for the minimum viable product to show the concept, technique, approach etc. will work. If it doesn’t, or requires considerable effort to work, review before proceeding, then stop!

Where can we innovate to add new insight ? Link more datasets together (using STATS19 as an anchor) to get a richer picture and gain more insight on behaviour and motivation. Look to get more timely information Analyse new data sources and approaches to gain new understanding Bring together the research and statistics in a more coherent narrative Understand the drivers of past trends to see if they have any predictive power for the future STATS19 review – better quality data with less burden Disseminate data in a more open and transparent way so others can innovate too! Note: All of these things will be looked at as feasibility studies, not full projects, this will help us determine where to best put resources going forward!

Data linking Linking to STATS19 depends on information on the time, location, injury and vehicle of the collision – as no personal data (names etc.) is currently passed to DfT. MOTs – MOT results prior to collisions – are there any patterns? Weather information via APIs Historic DVLA data (we have a 1% sample with birth, postcode, test passes, licence entitlements, driving offences and penalties, and medical restrictions) but could do more

Diagram

Making more of existing data sources Narrative verdicts from Coroners – what more can they tell us about complex road traffic collisions? Making more of RAIDS data Collecting new data from the Road Casualty Investigation Project Looking at MIDAS data (thanks Bruce!) More on road quality through OS variables The DVLA archive is an approx 1% sample of DVLA driver records in GB with information going back to  1985. The database currently contains 894,000 driver records. Of these records, 847,000 are in the latest generation of data, meaning there are 847,000 in the current database. The database includes data on date of birth, postcode, test passes, licence entitlements, driving offences and penalties, and medical restrictions. The database is sampled based on drivers being part of a name range (CHAME to CHEND and SWEET to TAYLL). Information is only held about a driver when they were part of the provided name range. Therefore, if a driver changes their name and is no longer part of the chosen name range, there will be no more updated information. The database can be used to look at driver offences throughout the years, this is more in depth than the DVLA records which only contains current offences, with offences being removed after 4 or 11 years.

Data from media reporting Sadly “bad news sells” So fatalities get reported online in the local and national media We can locate these media reports with a web-browser and get the data before we get STATS19 information Act as an ‘early warning system’ on trends But, it takes a lot to clean the data, which may be disproportionate effort for what we get…

New approaches: Road Casualty Investigation Project – With the RAC Foundation By taking a ‘safe systems’ approach to look at wider range of factors and players related to road collisions we can gain new insights on how they should be investigated and what types of interventions might reduce them in future. The local intelligence and detailed context gathered by analysts embedded in forces will test a new methodology for road safety analysis in Great Britain. The findings will be shared at a local level and nationally to allow everyone to benefit from the project.

Predictive modelling Using STATS19 we can explore which factors are most associated with a serious collision. Taking these for different demographic groups we can estimate what risk profiles might look like in the future. The seasonality in STATS19 is well known, but we can refine this modelling to look at risk profiles for different modes and demographics over time

Open data Improving our open data, transparency and accessibility are key parts of the framework for Official Statistics. All Government departments are working to make the information they release in a more open way. Look at releasing data via new formats in line with wider government agenda i.e. APIs, JSON files or through the ONS Secure Research Service This will allow all of you to innovate too!

CRASH 7 and beyond Half of forces as now using CRASH 7 – working towards one version of the truth – and consistent data from all forces Continuing to develop the product on an agile basis by evolving the work- plan to reflect feedback from users New PowerBI functionality and reports to allow local authorities to explore STATS19 data further.

Finally, and very importantly, this is not done in isolation Links to other organisations will help us enrich our evidence base and given new insights and ideas. These include: Road Casualty Investigation project with RAC Foundation Analysis of fatal cases on the SRN with Highways England and RAIDS with TRL Working with Road Safety Partnerships Building contacts with academics using STATS19 data and other sources to reflect their findings into our developments. And very importantly you! Let us know about your projects, challenges and success to keep up a conversation on how we learn together and work to save lives on our roads!

Questions?