Identifying crash-prone locations with quantile regression.
Identifying locations that exhibit the greatest potential for safety improvements is becoming more and more important because of competing needs and a tightening safety improvement budget. Current crash modeling practices mainly target changes at the mean level. However, crash data often have skewed distributions and exhibit substantial heterogeneity. Changes at mean level do not adequately represent patterns present in the data. This study employs a regression technique known as the quantile regression. Quantile regression offers the flexibility of estimating trends at different quantiles. It is particularly useful for summarizing data with heterogeneity. Here, we consider its application for identifying intersections with severe safety issues. Several classic approaches for determining risk-prone intersections are also compared. Our findings suggest that relative to other methods, quantile regression yields a sensible and much more refined subset of risk-prone locations.
Qin
Xiao
X
Department of Civil and Environmental Engineering, South Dakota State University, CEH 148, Box 2219, Brookings, SD 57007, United States. xiao.qin@sdstate.edu
Ng
Marie
M
Reyes
Perla E
PE
D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data / Q000639:trends; D002958:City Planning / Q000458:organization & administration / Q000639:trends; D004779:Environment Design / Q000639:trends*; D005544:Forecasting; D006801:Humans; D016012:Poisson Distribution; D011640:Public Policy / Q000639:trends; D012044:Regression Analysis; D018570:Risk Assessment / Q000458:organization & administration / Q000639:trends; D017751:Safety Management / Q000458:organization & administration* / Q000639:trends; D014481:United States