Bicycle Naturalistic Data Collection
Rakha, Hesham Ahmed
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Recently, bicycling has drawn more attention as a sustainable and eco-friendly mode of transportation. Between 2000 and 2011, bicycle commuting rates in the United States rose by 80% in large bicycle friendly cities (BFCs), by 32% in non-BFCs, and overall by 47%. On the other hand, about 700 cyclists are killed and nearly 50,000 are injured annually in bicycle–motor vehicle crashes in recent years in the United States. More than 30% of cyclist fatalities in the United States from 2008 to 2012 occurred at intersections, and up to 16% of bicycle-related crashes were due to cyclists’ violations at intersections. In light of these statistics, this project focused on investigating factors that affect cyclist behavior and predicting cyclist violations at intersections. Naturalistic cycling data were used to assess the feasibility of developing cyclist violation prediction models. Mixed-effects generalized regression model is used to analyze the data and identify the significant factor affecting the probability of violations by cyclists. At signalized intersections, right turn, side traffic and opposing traffic are statistically significant factors affecting the probability of red light violation. At stop-controlled intersections, the presence of other road users, left turn, right turn and warm weather are statistically significant factors affecting the probability of violations. Violation prediction models were developed for stop-controlled intersections based on kinetic data measured as cyclists approached the intersection. Prediction error rates were 0% to 10%, depending on how far from the intersection the prediction task was conducted. An error rate of 6% was obtained when the violating cyclist was at a time-to-intersection of about 2 seconds, which is sufficient for most motor vehicle drivers to respond.