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 bicyclist fatalities from motor vehicle collisions in ,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 BackgroundMethods Results Results Continued VariableNMeanSD Age (years) Years with driver’s license Miles driven per year Lifetime N accidents Lifetime N moving violations Driving Discomfort Score Aggravation Score Risk Perception Score 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, 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 Figure 2: Information collected while driving Gaze DetectionVehicle speed Distance to cyclist Lane Position Road 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