A method for processing the Confidential Carload Waybill Sample for railroad freight analysis

Freight transportation research is often constrained by the availability of useful data. In the context of freight rail research, traditional freight data sets do not provide sufficient resolution for detailed analysis of railroad freight flows. In addition to aggregated data, the complexity of railroad operations limits the usability of publicly available freight flow data. As part of their regulatory authority, the Surface Transportation has maintained the Confidential Carload Waybill Sample, a stratified sample representing 1%–3% of all railroad traffic in the United States for a year. Although rich with data, the Waybill Sample data requires pre-processing before it can be used for most analysis and visualization. Given this challenge, this paper introduces a tested method to extract, clean, and structure the Waybill Sample for subsequent visualization on a railroad network. The proposed method provides a reproducible approach for enhancing the Waybill Sample for use in railroad freight analysis.