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Contributory Factors in MAST

Contributory Factors in MAST. Bruce Walton Tanya Fosdick. What are Contributory Factors?. “the key actions and failures that led directly to the actual impact” “largely subjective and depend on the skill and experience of the investigating officer”

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Contributory Factors in MAST

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  1. Contributory Factors in MAST Bruce Walton Tanya Fosdick

  2. What are Contributory Factors? • “the key actions and failures that led directly to the actual impact” • “largely subjective and depend on the skill and experience of the investigating officer” • “can come from various sources [which] can be of variable quality … When there is conflicting evidence … the reporting officer should decide on the most plausible account” • “not necessarily the result of extensive investigation … This is not a problem” [emphasis mine]

  3. What Contributory Factors are NOT • Causation • Complex events often do not have single ‘cause’ • Criminal offences • e.g. “Impaired by alcohol” ≠ over the legal limit • Objective facts • Subjective judgement is encouraged • Definitive explanations • Very few crashes are exhaustively investigated

  4. Why use CFs at all? • How else can statistics be related to specific road safety issues? • Differentiate between behavioural and environmental issues • Relate behaviours to most relevant attendant circumstances and road user groups • Very large sample of actual events • On average, about 350k factors in 150k crashes pa • A largely untapped resource

  5. Credibility - “It’s only a sample” • Reported injury crashes only • Basic STATS19 principle can always be repeated • Officer attended crashes only • Since 2006 only • New factors introduced in 2005 • First year was a ‘bedding in’ period • Crashes with CFs are a discrete population • Not directly comparable with overall figures

  6. Reality, reporting bias, or both? • Stating the obvious • Most drivers probably “Fail to look”, for many reasons • Same factor expressed in different ways • Is “Swerved” ≈ “Loss of control”? • Reporting bias: truth, distortion, or does it cancel out? • Example: CF505 Driver Unfit Ill • 17% aged 75 to 84, but only 6.9% of adult population • 9% aged 25 to 34, but 16.5% of adult population • CF601 Aggressive v. CF 603 Nervous or in a panic • The ‘elephant in the room’

  7. Are affluent people more “uncertain” and less “aggressive”?

  8. CF Analysis in practice • Demonstration

  9. Future refinements? • Qualitative research to help data interpretation • Additional fields relating vehicles to casualties • Related Vehicle Attributed CF 306 DriverSpeedLimit • CF grouping may allow better targeting of issues • Classic example: excessive or inappropriate speed (CFs 306 and/or 307) • Other groupings possible e.g. Poor manoeuvre or signalling (CFs 403, 404 and/or 407) • Best practise consensus would be required

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