Annual pedestrian and bicycle fatalities remained steady while motor vehicle fatalities declined in the United States during the past decade; this balance underscores the need for better methods of pedestrian and bicycle safety analysis. This study presents a new method for classifying pedestrian and bicycle crashes called the location–movement classification method (LMCM) and shows that the LMCM provides useful information that is not captured by a well-established NHTSA crash typology. Both typologies were applied to a sample of 296 pedestrian and 229 bicycle crashes reported in Wisconsin between 2011 and 2013. The LMCM revealed that pedestrian crashes of all injury severity levels were significantly more likely to be on the farside than the nearside of intersections. Pedestrian crashes were significantly more likely to be fatal than nonsevere when they involved motorists traveling straight, were along roadways between intersections, and involved pedestrians approaching from the motorist’s left. Bicycle crashes were significantly more likely to be fatal than nonsevere when they involved motorists traveling straight, were along roadways between intersections, and involved motorists traveling in the same direction as the bicyclist. The LMCM can be used to support engineering, education, and enforcement treatments to reduce pedestrian and bicycle, crashes, injuries, and fatalities.

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This study examined American Community Survey journey-to-work data from 2008 to 2012 to identify the characteristics of neighborhoods with the highest levels of bicycle commuting in the United States. The 100 census tracts with the highest bicycle commute mode shares (top 100 census tracts) were identified and paired with 100 other randomly selected census tracts from the same county (100 comparison census tracts). As a whole, the top 100 census tracts had a bicycle commute mode share of 21%. Seventy of the top 100 census tracts were in locations that had fewer than 10 days per year with high temperatures below 32°F (0°C), and 68 were within 2 mi (3.2 km) of a college or university campus. Seventeen had relatively low college populations and were in high-density neighborhoods close to large city central business districts. Conditional logistic regression was used to estimate the likelihood of a paired tract being in the top 100 rather than the comparison tract. After climate and topography were controlled for, being a top 100 census tract was associated with several socioeconomic and local-environment characteristics, including being located closer to a university and having more households without automobiles, more people born in other states and countries, higher population density, more housing constructed before 1940, and greater bicycle facility density. The results suggest that policies to model employment centers after university campuses; design neighborhoods that support routine, multimodal travel; and reduce barriers to bicycling in bad weather may help create more local areas with high rates of bicycle commuting.

This paper analyzes the relationship between detailed neighborhood environment variables and commute mode share using a dataset drawn from across the United States and includes model validation results. Representing one of the first studies of its kind, we use United States journey-to-work data to explore the following questions: 1) Which detailed environment variables have significant associations with the proportion of people in a neighborhood who take public transit, walk, or bicycle to work? 2) Does adding detailed environment variables to existing, nationally available neighborhood variables improve the predictive accuracy of work commute mode share models? We use a set of 120 randomly selected census tracts to estimate fractional multinomial logit models that predict walk, bicycle, transit, and automobile commute mode shares. The Base Model includes a set of nine significant, nationally available variables identified from a previous analysis of 5,000 tracts. We test 18 additional detailed neighborhood environment variables and identify five variables that have significant associations with commute mode share: sidewalk coverage (positive association with transit and walk), proximity to a rail station (positive association with transit), bicycle facility density (positive association with bicycle), freeway presence (negative association with walk), and mixed land use (positive association with transit, walk, and bicycle). While these detailed environment variables add clarity to our understanding of factors that influence travel behavior, our validation analysis using 50 separate census tracts does not provide conclusive evidence that these variables improve model accuracy. Further studies with larger sample sizes are needed to determine the optimal set of variables to include to predict automobile, transit, pedestrian, and bicycle commute mode shares.