  In this paper we adopted state-of-the-art machine learning algorithms,
namely: random forest (RF) and least squares boosting, to model crash data and
identify the optimum model to study the impact of narrow lanes on the safety of
arterial roads. Using a ten-year crash dataset in four cities in Nebraska, two
machine learning models were assessed based on the prediction error. The RF
model was identified as the best model. The RF was used to compute the
importance of the lane width predictors in our regression model based on two
different measures. Subsequently, the RF model was used to simulate the crash
rate for different lane widths. The Kruskal-Wallis test, was then conducted to
determine if simulated values from the four lane width groups have equal means.
The test null hypothesis of equal means for simulated values from the four lane
width groups was rejected. Consequently, it was concluded that the crash rates
from at least one lane width group was statistically different from the others.
Finally, the results from the pairwise comparisons using the Tukey and Kramer
test showed that the changes in crash rates between any two lane width
conditions were statistically significant.
