Dylan Antoniazzi, Sacha Dubois, Rupert Klein, Michel Bédard

Dylan Antoniazzi, Sacha Dubois, Rupert Klein, Michel Bédard
Motorcyclist Crash Responsibility: The Effect of Driver Age and Motorcycle Displacement Dylan Antoniazzi, Sacha Dubois, Rupert Klein, Michel Bédard

Acknowledgments A quick thank-you to the project co-authors for their collaborative efforts Andrew Wheeler for his blog posts detailing the Graphics Processing Language in SPSS The organizations below for their support

Introduction In the USA, between 1980 to 2013:
Fatalities of riders under 29 have fallen from 73% of all fatalities to 27% Fatalities of riders over 50 have risen from 3% to 34% (NHTSA, 2015) This shift in fatality proportions is partly due to the aging population

Introduction A motorcycle’s power can be measured by its displacement
Typically specified in cubic centimeters (CCs) The higher the engine CCs the more powerful the motorcycle resulting in greater acceleration and speed Motorcycle Type Typical Displacement (CCs) Standard 250 Sport Touring 1000+

Research Question To examine the effect of displacement on fatal crash responsibility while considering motorcyclists’ age

Data Source Fatality Analysis Reporting System
Information on ALL fatal crashes in the USA since 1975 Contains detailed information on environmental, vehicular and motorcyclist-related factors

Design Employed a case-control design
Cases had committed one or more Unsafe Motorcyclist Action (UMA), our proxy measure of crash responsibility Examples of UMAs include: Speeding, Weaving Controls did not commit an UMA

Inclusion Criteria Not Impaired by alcohol or drugs: Sex:
Alcohol and drug data first captured in 1987 To rule out alcohol and drugs, we used data from 1987 through 2009 Sex: Given ~97% of motorcyclists involved in fatal crashes were male, we excluded females

Analyses Employed binary logistic regression to examine crash responsibility by motorcycle displacement and motorcyclist's age

Logistic Regression: Independent crash contributors
Displacement Measured in CCs Examined displacement in 250 CC increments up to 1500 CCs Age Measured in years Examined age in 10 year increments up to age 70

Logistic Regression: Design
Dependent variable: Responsibility Either any UMA or one of the top three UMAs Independent variable: Both linear and quadratic terms for Age and Displacement (e.g., Age and Age2) Interaction between Age and Displacement

RESULTS

CONSORT FLOW DIAGRAM MC riders involved in a fatal crash between (n=78,006) Confirmed BAC of Zero (n=27,777) Confirmed drug negative (n=13,813) Male Riders (n=13,293)

Top Unsafe Motorcycle Actions
35% (4,669) Speeding 22% (2,957) Weaving 7% (910) Erratic Behavior 61% (8,064) Any UMA 395 (5,229) No UMAs

35% (4,669) Speeding 22% (2,957) Weaving 7% (910) Erratic Behavior 61% (8,064) Any UMA 39% (5,229) No UMAs

SPEEDING LOGISTIC REGRESSION

Speeding 35% (4,669) 22% (2,957) 7% (910) 61% (8,064) 39% (5,229)
Weaving 7% (910) Erratic Behavior 61% (8,064) Any UMA 39% (5,229) No UMAs

Speeding For Speeding we see that displacement has an inverted J-Shape for ages That is the highest odds ratios of any UMA by age are typically seen at CCs And the lowest odds ratios are seen at the 1500 CC level Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.43 ( ) 1.10 ( ) 1.21 ( ) 0.99 (0.99–0.99) 1.02 ( ) ( ) 1.00 ( )

Speeding Riders aged had increased odds of committing a Speeding UMA for CCs compared to equivalent aged riders of 250 CC motorcycles For these riders, increased odds are not present at 1500 CCs Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.43 ( ) 1.10 ( ) 1.21 ( ) 0.99 (0.99–0.99) 1.02 ( ) ( ) 1.00 ( )

Speeding However, by age 70 we see a more linear shape
Riders aged 70 had increased odds of committing a Speeding UMA for 1500 CCs motorcycles compared to equivalent aged riders of 250 CC motorcycles At lower CCs (≤ 1250), increased odds are not statistically significant Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.43 ( ) 1.10 ( ) 1.21 ( ) 0.99 (0.99–0.99) 1.02 ( ) ( ) 1.00 ( )

Speeding Age 20 CCs Odds Ratio (95% CI) 250 1.00 (1.00; 1.00) 500
1.68 (1.48; 1.92) 750 2.15 (1.76; 2.63) 1000 2.08 (1.65; 2.62) 1250 1.53 (1.17; 2.01) 1500 0.85 (0.57; 1.27) Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.43 ( ) 1.10 ( ) 1.21 ( ) 0.99 (0.99–0.99) 1.02 ( ) ( ) 1.00 ( )

WEAVING LOGISTIC REGRESSION

Weaving 35% (4,669) 22% (2,957) 7% (910) 61% (8,064) 39% (5,229)
Speeding 22% (2,957) Weaving 7% (910) Erratic Behavior 61% (8,064) Any UMA 39% (5,229) No UMAs

Weaving For Weaving we see that displacement has an inverted U-Shape
However the curve tends to become linear as age increases Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.62 ( ) 1.07 ( ) 1.13 ( ) 0.99 (0.99–0.99) 0.98 ( ) 1.00 ( ) ( )

Weaving Riders aged had increased odds of committing the Weaving UMA for motorcycles with 500 – 750 CCs compared to equivalent aged riders of 250 CC motorcycles Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.62 ( ) 1.07 ( ) 1.13 ( ) 0.99 (0.99–0.99) 0.98 ( ) 1.00 ( ) ( )

Weaving Further, riders aged had reduced odds of the Weaving UMA with 1500 CC motorcycles compared to equivalent aged riders of 250 CC motorcycles Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.62 ( ) 1.07 ( ) 1.13 ( ) 0.99 (0.99–0.99) 0.98 ( ) 1.00 ( ) ( )

Weaving By age 60, the displacement curve starts to take on a more linear shape However odds of committing a Weaving UMA were not significantly increased at all CC levels compared to equivalent aged riders of 250 CC motorcycles Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.62 ( ) 1.07 ( ) 1.13 ( ) 0.99 (0.99–0.99) 0.98 ( ) 1.00 ( ) ( )

Weaving Age 20 CCs Odds Ratio (95% CI) 250 1.00 (1.00; 1.00) 500
1.32 (1.14; 1.53) 750 1.41 (1.13; 1.75) 1000 1.20 (0.93; 1.55) 1250 0.83 (0.61; 1.13) 1500 0.46 (0.29; 0.73) Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.62 ( ) 1.07 ( ) 1.13 ( ) 0.99 (0.99–0.99) 0.98 ( ) 1.00 ( ) ( )

ERRACTIC OR RECKLESS RIDING
LOGISTIC REGRESSION

Erratic Riding 35% (4,669) 22% (2,957) 7% (910) 61% (8,064)

Erratic Riding For Erratic Riding we see that displacement has a curvilinear shape Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.20 ( ) 1.34 ( ) 1.13 ( ) 0.99 (0.99–0.99) 1.12 ( ) 0.97 ( ) ( ) 1.00 ( )

Erratic Riding Riders aged had increased odds of committing the Weaving UMA for CCs 500 – 1250 compared to equivalent aged riders of 250 CC motorcycles Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.20 ( ) 1.34 ( ) 1.13 ( ) 0.99 (0.99–0.99) 1.12 ( ) 0.97 ( ) ( ) 1.00 ( )

Erratic Riding Riders aged had increased odds of committing the Weaving UMA for CCs 500 – 1250 compared to equivalent aged riders of 250 CC motorcycles Riders age 50 have a similar pattern Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.20 ( ) 1.34 ( ) 1.13 ( ) 0.99 (0.99–0.99) 1.12 ( ) 0.97 ( ) ( ) 1.00 ( )

Erratic Riding By age 60 the effect of displacement is lost Age Age2
CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.20 ( ) 1.34 ( ) 1.13 ( ) 0.99 (0.99–0.99) 1.12 ( ) 0.97 ( ) ( ) 1.00 ( )

Erratic Riding Age 20 CCs Odds Ratio (95% CI) 250 1.00 (1.00; 1.00)
500 1.45 (1.19; 1.78) 750 1.82 (1.34; 2.47) 1000 1.96 (1.38; 2.77) 1250 1.81 (1.18; 2.77) 1500 1.44 (0.76; 2.74) Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.20 ( ) 1.34 ( ) 1.13 ( ) 0.99 (0.99–0.99) 1.12 ( ) 0.97 ( ) ( ) 1.00 ( )

ANY UMA LOGISTIC REGRESSION

Any UMA 35% (4,669) 22% (2,957) 7% (910) 61% (8,064) 39% (5,229)

Any UMA For Any UMA we see the now familiar curvilinear effect of displacement, especially for lower aged riders Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.48 ( ) 1.12 ( ) 1.07 ( ) 0.99 (0.99–0.99) 1.02 ( ) ( ) ( ) 1.00 ( )

Any UMA Riders aged had increased odds of committing any UMA for motorcycles with 500 – 1000 CCs compared to equivalent aged riders of 250 CC motorcycles Further, riders age had reduced odds of committing any UMA for 1500 CC motorcycles compared to equivalent aged riders of 250 CC motorcycles Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.48 ( ) 1.12 ( ) 1.07 ( ) 0.99 (0.99–0.99) 1.02 ( ) ( ) ( ) 1.00 ( )

Any UMA Riders aged 50 had similar but weaker increased odds at CCs For these riders, increased odds do not remain at 1000 CCs Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.48 ( ) 1.12 ( ) 1.07 ( ) 0.99 (0.99–0.99) 1.02 ( ) ( ) ( ) 1.00 ( )

Any UMA At age 70 displacement takes on a linear effect
While odds increase by displacement this is not statistically significant Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.48 ( ) 1.12 ( ) 1.07 ( ) 0.99 (0.99–0.99) 1.02 ( ) ( ) ( ) 1.00 ( )

Any UMA Age 20 CCs Odds Ratio (95% CI) 250 1.00 (1.00; 1.00) 500
1.19 (1.07; 1.33) 750 1.27 (1.08; 1.50) 1000 1.21 (1.00; 1.46) 1250 1.03 (0.81; 1.30) 1500 0.78 (0.55; 1.10) Age Age2 CC CC2 Age*CC Age2*CC Age*CC2 Age2*CC2 0.48 ( ) 1.12 ( ) 1.07 ( ) 0.99 (0.99–0.99) 1.02 ( ) ( ) ( ) 1.00 ( )

Implications Given these results education and legislative measures should be considered For example, develop training interventions focusing on control, stability, and breaking differences given the vehicle’s greater weight and power

Implications Legislatively, licensing tiers could be employed based on displacement and educational requirements Both education and legislative measures could curb the trend seen between higher levels of displacement and crash responsibility

Contact Info Mr Dylan Antoniazzi Mr Sacha Dubois Dr Rupert Klein Dr Michel Bédard

Dylan Antoniazzi, Sacha Dubois, Rupert Klein, Michel Bédard

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