Abstract
Freight transportation research is often constrained by the availability of appropriate data. Within the context of freight rail research, use of traditional freight datasets present some critical challenges. Aggregating freight data to geographic areas diminishes the ability to perform nuanced analysis. More challenging, the complexity of railroad operations limits the usability of publicly available freight flow data which do not account for which rail company is economically and legally responsible for the movement of the goods or for the contractual arrangements between infrastructure owners and carriers. Finally, detailed data are usually privately owned and not readily-accessible for analysis outside the individual rail companies. As part of their regulatory authority, the Surface Transportation Board has developed the Confidential Carload Waybill Sample, a stratified sample representing 1% to 3% of all rail traffic in the United States for a particular year. While the sample is rich with data, it is limited in spatial information as well as in its implementation in geospatial analysis. This paper introduces a methodology for geospatially enabling the Confidential Carload Waybill Sample which can then be used to generate rail routes based on a topological representation of the U.S. railroad network. This methodology is applied using two case studies, a freight diversion analysis and transporting hazardous materials.
