Abstract:
Average daily volume fluctuates intensely based on the day of week in the intermodal freight transportation. Shippers tend to peak around Thursdays and receivers tend to peak around Mondays. These fluctuations bring challenges to the industry in terms of capacity management and getting reliable service from the railroad companies. The purpose of this study is to forecast J. B. Hunt Transport Services, Inc.'s load volume on railroads. Load is meant to be the number of containers that will arrive at a rail ramp during a 24hrs time window. The end in mind is to have better service from the railroad companies and to manage the company owned equipment better. The forecasting model applied to tackle this problem is a multiple linear regression model and is based on the historical in-gate numbers. It uses the previous two year's data and day of week information as independent variables, and current year's data as the response variable. The results indicate better accuracy levels for the model when compared to the two week moving average.