| Abstract |
This study focuses on the factors affecting the retroreflectivity (RL) of longitudinal pavement marking using the data collected from four different functional classes of roads in Wyoming. Two different approaches were considered: the whole dataset was used in one approach, while in another, the dataset was classified based on functional classification. In both approaches, a binary logistic regression model was developed to investigate the factors affecting the probability of RL above the WYDOT’s marginal retroreflectivity level (125 and 100 mcd/m2/lux for white and yellow marking, respectively). A total of six categorical variables and eight continuous variables were incorporated into the models. The marking age, color, material, line type, highway location, and degree of curvature impact the pavement marking retroreflectivity above the marginal level. Regarding the horizontal curve, the increase in the degree of curvature of the horizontal curve decreases the likelihood of having the RL exceeding the marginal level. |
, Uttara Roy 
, Khaled Ksaibati 
