Hypothesis 1: Narrow roadways and roadways with higher speed limits will increase risk of vehicle/bicycle crash

Hypothesis 2: Bicycle lanes and signage will result in greater vehicle-bicycle separation distance

• >51,000 bicyclist fatalities from US motor vehicle collisions since 1932

• 726 bicyclist fatalities from motor vehicle collisions in 2012

• 49,000 bicyclists were injured in motor vehicle collisions in 2012

• Injuries and deaths among bicyclists from motor vehicle collisions cost an estimated $8 billion annually

• Bicycling injury and fatality rates in the US appear to be increasing

• Need for a safe, efficient, and effective method to evaluate driver behaviors and traffic infrastructure which may increase bicyclist crash and injury risk

• Results demonstrate the potential utility of automobile simulators for evaluating the risk of vehicle-bicycle crashes

• Bicycle lanes and 4 lane roads increased driver-bicyclist separation, supporting hypotheses 1 and 2

• Curbs and 2 lane roads reduced driver-bicyclist separation, supporting hypothesis 1

• Roads with bicycle signs had lower normalized driver speeds, supporting hypothesis 2

• Roads with 35 MPH speed limits had higher normalized driver speeds, not supporting hypothesis 1

• Greater driving aggravation was associated with smaller driver-bicyclist separation

• Simulator-based research shows promise in evaluating infrastructure- and behavior-based bicycle safety strategies

Hypotheses

Background Methods

Results

Results Continued

2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.805

10152025303540

Mean Distance in Meters

Aggr

evati

on S

core

Variable N Mean SD

Age (years)30 39.6 6.6

Years with driver’s license30 22.1 7.3

Miles driven per year30 15483 9532.6

Lifetime N accidents30 2.2 1.7

Lifetime N moving violations30 2.6 1.8

Driving Discomfort Score30 16.4 6.1

Aggravation Score30 25.5 5.9

Risk Perception Score30 71.9 14.0

Table 2: Participant Statistics • Drivers were experienced and had few lifetime crashes/moving violations (Table 2). Females had significantly higher perceived risk scores (data not shown)

• Lowest normalized driver speeds in conditions with bicycle signs; highest normalized driver speeds in conditions with 35 MPH speed limits (Figure 3)

• Greatest driver-bicycle separation in conditions with 4 lanes and bicycle lanes; smallest driver-bicycle separation in conditions with curbs and 2 lanes (Figure 3)

• Driver-bicyclist speed differentials reduced with increasing age (r=-0.559, p=0.001)

• Driver aggravation and mean driver-bicyclist distance significantly correlated (Figure 4)

This study was funded by the University of Michigan Injury Center.

Acknowledgements

Conclusions

Fifteen male (M=37.7 ± 5.8 years old) and 15 female (M=41.4 ± 7.0 years old) subjects, 30-50 years old

Survey

• Driving history (length of experience, types of roads primarily driven, usual trip length, miles driven per year, crash history)

• Aggravation Scale (0-60 score on driving aggravation factors)

• Risk Perception Scale (0-150 score on perceived driving risk)

• Higher scores indicated greater aggravation, perceived risk

Virtual Drive in Simulator

• 2 min practice course, 10 min experimental course

• 9000-m long experimental course, 27 virtual bicyclists

• 12 experimental conditions (Table 1, Figure 1)

• Measured driver speed, gaze, location in virtual world, distance from virtual bicyclists (Figure 2)

Figure 3: Normalized driver speed and mean and minimum driver-bicyclist distance by experimental condition (KEY – BL: bicycle lane, BS: bicycle sign, PL: parking lane, SH: sharrows, C: Curb, 35: 35 MPH, 50: 50 MPH, 2: 2 road lanes, 4: 4 road lanes)

Figure 4: Distance from Cyclist by Aggravation

Goals

Goal 1: Demonstrate use of automobile simulator to observe and measure vehicle/bicycle interactions

Goal 2: Evaluate risk factors for vehicle/bicycle crashes

References

All statistics were obtained from the National Highway Traffic Safety Administration website at www.nhtsa.org

Figure 2: Information collected while driving

Gaze Detection Vehicle speed

Distance to cyclistLane PositionRoad Position

Figure 1: Examples of experimental conditions

Suburban Bike Lane

Rural Narrow Shoulder

Use of a driving simulator to assess risk of bicycle-motor vehicle crashes Rick Neitzel 1, Ph.D., CIH, Stephanie Sayler 1, C. Ray Bingham 2, Ph.D., & Kenneth Guire 3

1 University of Michigan Department of Environmental Health Sciences; 2 University of Michigan Transportation Research Institute; 3 University of Michigan Department of Biostatistics

ANALYSIS Correlations, ANOVA, and generalized linear models used to analyze three crash risk outcomes: mean and minimum driver-bicyclist separation and driver’s speed normalized to the posted speed limit

Table 1: Experimental conditions

r=-.411, p=0.024