Maintaining driver and worker safety on highways experiencing closures during construction, maintenance, and rehabilitation activities is crucial. Throughout the years, several studies have been conducted to identify the influences of various factors on the injury-severity level of the collisions occurring on highways. Developing statistical models can help identify the factors which significantly influence the injury-severity level. The clear and practical results of these models could be adopted by the contractors to improve the level of safety in the work zones. In recent years, several complicated and advanced statistical models were applied to injury-severity data; these models are often time inefficient, impractical, and hard to interpret by non-statistical experts. Also, these models are intended to have the issue of overfitting which limits the ability of the models to predict future events. This study collected historical data from four different US states (New York, Pennsylvania, Illinois, and Michigan), between 2013 and 2016, to develop an injury-severity level statistical model. Selected states have similar weather condition, pavement condition, and construction policies and regulations as Ontario, Canada. The authors believe due to the stated similarities between these US states and Ontario, the statistical models developed will be spatially transferable. Therefore, this paper aims to apply the random parameter concept to some of the well-known fixed parameter statistical models to overcome issues from both unnecessarily complicated models and statistically insufficient methodologies. Random parameter ordered Probit, random parameter ordered Logit, and random parameter ordered arctangent models will be developed to address stated issues. Then, a comparative assessment among all ordered models will be conducted to investigate which one of these models has statistical dominance. Finally, these models will be used to develop an Excel-based system which can be adapted by non-statistical experts to operate, understand, and plan. The Excel-based system will interpret the effect of each significant factor as well as predicting the possible severity level of future collisions in the work zones. This study intends to present a straightforward methodology, which addresses previous concerns in this field.