1. Paul Hillier, ARRB Group, Australia
‘Data Led, Experienced Tuned’ – Developing an Effective Local Skid Resistance Management Strategy 3rd International Road Surface Friction Conference 2011
Paul Hillier, ARRB Group, Australia

2. Presentation outline Introduction – the challenge faced
The value of data (e.g. Organisational Governance Model)
‘Data mining’
Austroads Guide (Jan 2005) – strategy framework
Where does data come in?
Limitations of data
Local experience as a fine tuning tool
Closing thoughts

3. The challenge faced ….. Identifying, collecting &
analysing available data
Local knowledge
and experience

4. Organisational Governance Model (OGM)

5. Organisational Governance Model (OGM)

6. Data mining …… a simplistic view
Available Data
Facts and Knowledge

7. Data mining …… a simplistic view
Available Data
Facts and Knowledge
Decisions

8. Data as our comfort blanket?
We are very comfortable as engineers making decisions on data
It makes sense…….
But what if we can’t afford to collect it?
Or fail to recognise that we do not have the full / current picture?
How do we integrate practical experience?
We’ll come back to this…….

9. Focus on Skid Resistance Strategies

10. Austroads Guide – Jan 2005

11. Austroads Guide – Jan 2005

12. Austroads 2011 (AT 1131) – data usage
Determining zones of skid resistance demand
Determining testing regime
Setting ILs from first principles (crash rates)
Periodic review and update of ILs
Identifying and prioritising sites for further investigation

13. Limitations of data
Generic
Specific -in managing skid resistance

14. Generic limitations of data
Can include:
is not available (or limited availability)
‘ageing’ – network changes have occurred
collected by a device out of calibration / maintenance
collected by a device that is now superseded
data appears (or is obviously) erroneous, skewed or unrepresentative
data has processing / collation and/or display errors
is too limited to permit patterns and trends to be identified
gaps in data are significant
data has been previously partially processed or filtered
‘raw’ data can no longer be processed as software etc not available
And what if we don’t recognise any shortfalls?
Danger of ‘rubbish in, rubbish out’?

15. Specific limitations of data – Skid Resistance
Author’s position - use data wherever possible (and applicable)!
But understand / accept limitations:
historical rationale (comparing road sections)
‘snap shot in time’ concept
crash data: no. of incidents low + v.difficult establishing causation

16. Importance of local experience as a fine tuning tool
Will help ensure that the strategy:
is achievable within current resources
meets local conditions and challenges
will make a difference operationally
meets corporate & operational objectives
‘will fly’ (is implemented)
is ‘owned’ locally
will be continually improved

17. Closing Thoughts

18. Author’s experience (skid resistance)
Use data in forming strategy and making policy decisions
But, understand its possible limitations
Ignore local practical (delivery) experience at your peril
Involve a number of people – explain the corporate position and requirements, but also understand their local position
Get many views - collate them / analyse them
Vital for credibility, ownership and achievability
Don’t forget the roll out and training regime