Snow can cause dangerous driving conditions by reducing pavement friction and covering road surface markings. Salt is widely used by highway maintenance managers in the United States to reduce the impact of snow or ice on traffic. For the development of long-term plans, especially for the next winter season, it is essential to know what factors affect salt usage and to determine the sufficient amount of salt needed in each depot location. This determination can be accomplished by estimating statistically robust models for salt usage prediction. In this study, historical data regarding storm characteristics and salt usage on the New Jersey Turnpike and Garden State Parkway were used to estimate those models. Linear models, hierarchical linear models, and hierarchical linear models with varying dispersion (HLVDs) were developed to predict the salt usage on the two highways. Results show that districts with higher average snow depth, longer storm duration, and lower average temperature were associated with greater salt usage. HLVD models were found to have the best predictive performance by including random parameters to account for unobserved spatial heterogeneity and by including fixed effects in the dispersion term. In addition, with the estimation of case-specific dispersion on the basis of storm characteristics, HLVD models could be used appropriately to estimate the upper bounds of salt usage, bounds that are not extremely large and could satisfy the salt demand in most cases. The findings of this study can provide highway authorities with valuable insights into the use of statistical models for more efficient inventory management of salt and other maintenance materials.
ASJC Scopus subject areas
- Civil and Structural Engineering
- Mechanical Engineering