This study analyzed the statistical significance of continuous lighting and/or clearing and grubbing of roadway corridors as measures taken to reduce moose-vehicle collisions (MVCs). Construction improvements, moose population, and weather data were collected for ten project corridors. Data were gathered for a 10-year period for each project, spanning from 5 years before to 5 years after the construction completion date. To determine the statistical significance of the variables, regression analysis was performed for every possible combination of variables.

The results of the regression analyses showed different levels of variation in crashes being explained by the data set. The results for four of the projects were inconclusive; therefore, a correlation between the number of reported MVCs and the independent variables could not be determined. The results from the other projects show between 15.0% and 85.9% of the variation in collisions being explained by the independent variables, with differing results on which variables contributed to the number of MVCs. Among the projects, differences in whether the variable contributes to an increase or decrease in the number of MVCs are apparent. These results show that the variables do not capture all the contributing factors related to MVCs.

A combined set of all project corridors was evaluated. The combined set regression analysis resulted in only 12.0% of the variation in collisions being explained by the independent variables. This result also shows that more factors contribute to MVCs than were included as variables in this study.

Although this study resulted in low statistical significance, there is evidence of positive results for the mitigation measures, continuous lighting and clearing and grubbing. Continued monitoring of post construction conditions, Maintenance and Operations events, and data collection for continued improvements will increase the accuracy of the data for future re-analysis and the development of crash modification factors.