hypothesis 1: narrow roadways and roadways with higher speed limits will increase risk of...
TRANSCRIPT
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