TANYA FOSDICK and GLEN DENNIS USING DATA TO INFORM INTELLIGENCE-LED MOTORCYCLE CAMPAIGNS

Transcription

1 TANYA FOSDICK and GLEN DENNIS USING DATA TO INFORM INTELLIGENCE-LED MOTORCYCLE CAMPAIGNS

2 Who crashes? Who are the types of riders involved in injury collisions in Great Britain between 2010 and 2014?

3 Born-again riders Sports bike riders Inexperienced riders Weekend warriors Speeders

4 Answers Riders in KSI Collisions Large Bikes Young Moped Riders Commuters/ At-Work 50% are on bikes with engines over 500cc 21% are years old & on machines up to 125cc 20% are reported as driving for work or commuting Different types of crash-involved rider = different types of solution

5 How do they crash? What led to injury collisions involving motorcyclists in Great Britain between 2010 and 2014?

6 SMIDSY Poor manoeuvres by cars Bends too fast Pot holes/drains Speed

7 How do they crash? Over 500cc 45% not at junctions & 29% at T-junctions 52% on 50mph or above roads 28% single vehicle collisions 51% travelling straight ahead & 22% on bends 20% on Sundays 27% 3-6pm

8 How do they crash? Young scooter & mopeds 24% not at junctions & 40% at T-junctions 70% on 30mph roads 23% single vehicle collisions 62% travelling straight ahead & 12% overtaking 16% on Tuesdays 21% 6-9pm

9 How do they crash? Commuters/At-Work 26% not at junctions & 35% at T-junctions 55% on 30mph roads 24% single vehicle collisions 58% travelling straight ahead & 16% overtaking 19% on Wednesdays 34% 6-9am

10 Contributory Factors Over 500cc Control Errors (30%) Observation Errors (21%) Speed Choices (18%) Unsafe Behaviour (14%) Manoeuvre Errors (13%) Young scooter & mopeds Observation Errors (25%) Nervous behaviour (24%) Control Errors (22%) Unsafe Behaviour (15%) Speed Choices (13%) Commuters/ At-work Observation Errors (22%) Control Errors (21%) Road Surface (14%) Speed Choices (11%) Manoeuvre Errors (10%)

11 Case Study: Humberside 2004 Operation Achilles founded to address motorcycle KSIs and anti-social riding 2006 New data-led approach Profiled routes Profiled riders Speed data Times/dates

12 What did the data say? years old 54% local to East Riding of Yorkshire (30% out of force) From smaller rural communities. Reasonably affluent Larger bikers (over 500cc) High powered Not commuting/riding for work Sundays/Summer months/good weather Single carriageways/60mph limits

13 What did the data say? Failed to look properly Failed to judge other person s path or speed Careless, reckless or in a hurry Same Mosaic Types also over-represented on BikeSafe & RIDE (receptive to education) Collisions occurring on specific routes

14 Interventions What would you do?

15 High visibility Covert enforcement enforcement Engagement Engineering Media Better signs

16 Interventions What would you hope to achieve? Reduce motorcycle casualties

17 Interventions Difficult to prove links between interventions & casualty reduction but can measure other linked outputs: RIDE scheme referrals Improvements in rider attitude & behaviour Educating road users Reducing criminality KSIs on specific routes (before & after)

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19 Enforcement

20 Enforcement Toolkit

21 Results

22 Marketing & Media

23 Partnership work

24 Engineering Info boards Shiny Side Up Unmarked vehicles in operation Shell grip/lines and signs Partnership with local authority Frangible roadside furniture Police enforcement observation ramps

25 RIDE For those who NEED to be educated 828 referrals in Humberside since 2008 Purpose of course attitudinal change

26 RIDE Evaluation Mixed methods quantitative & qualitative RIDE attendees & Control Positive responses Qualitative: Course atmosphere Attitudes & motivations, not skills training Alternative to points Reported changed behaviour

27 BikeSafe For those who WANT to be educated Bridging the Gap Aims: Encourage participation in accredited post-test training Encourage positive rider attitudes & behaviours

28 Challenges for BikeSafe Long term funding Robust and tangible evidence of effectiveness What does it achieve?

29 Evaluation of BikeSafe Long-term evaluation One of the largest insights into motorcycle riders & their attitudes & behaviour in the UK Independent robust study, involving TRL Pre then post study (12 months) with 24 month follow up Completed Autumn 2017

30 Initial Findings (Pre responses) 1,786 respondents from April April 2015 BEFORE attending BikeSafe 46% response rate Profiling attendees Understanding: Attitudes to safety Riding preferences Post-test training rates Barriers to post-test training

31 Percentage of BikeSafe Respondents Age Getting older 35% 30% 25% % 15% 10% 5% 0% under plus Age Band

32 Mosaic Profiles Over-represented Groups

33 Mosaic Profiles Under-represented Groups

34 One-Size doesn t fit all Over 500cc Mosaic Group KSI Casualties BikeSafe Pro-Police Affluent Educated A D E G - H K L M

35 Mosaic Groups Motorcycle KSI Riders Attending BikeSafe Not Attending BikeSafe

36 Number of BikeSafe Respondents 100-Based Index Engine Size 600 Bikesafe respondents GB Licensed Motorcycle index cc cc cc cc cc 801-1,000cc 1,000cc + Unknown Engine Size 0

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39 Next Steps

40 Conclusions Humberside National Linear (Humberside)

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42 BikeSafe Using evaluation to steer future of BikeSafe Prove effectiveness Refine if necessary Refrain if necessary Highlight strengths & weaknesses Barriers to post-test training Perceptions of post-test training Working with providers to improve post-test training Future justification funding/support

43 ANY QUESTIONS?