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

Marc R. Fialkoff; Kathleen L. Hancock; Steven K. Peterson

doi:10.1016/j.retrec.2018.10.004

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

Marc R. Fialkoff, Kathleen L. Hancock, Steven K. Peterson

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	34-43
Number of pages	10
Journal	Research in Transportation Economics
Volume	71
DOIs	https://doi.org/10.1016/j.retrec.2018.10.004
State	Published - Nov 2018

Funding

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Environmental Management, under contract number DE-AC05-00OR22725. This material is based, in part, upon work supported by the U.S. Department of Transportation, Office of Safety, under IAA DTFR5313x00122l/003. This work was also supported by Oak Ridge National Laboratory Higher Education Research Experience Dissertation/Thesis Program administered by Oak Ridge Associated Universities (ORAU). This material is based upon work supported by the U.S. Department of Energy, Office of Science , Office of Environmental Management, under contract number DE-AC05-00OR22725 . This material is based, in part, upon work supported by the U.S. Department of Transportation , Office of Safety, under IAA DTFR5313x00122l/003 . This work was also supported by Oak Ridge National Laboratory Higher Education Research Experience Dissertation/Thesis Program administered by Oak Ridge Associated Universities (ORAU).

Funders	Funder number
Oak Ridge National Laboratory
Office of Safety	IAA DTFR5313x00122l/003
U.S. Department of Energy
U.S. Department of Transportation	DTFR5313x00122l/003
Office of Science
Office of Environmental Management	DE-AC05-00OR22725
Oak Ridge Associated Universities
Oak Ridge National Laboratory

Keywords

Origin-destination matrix
Rail network
Railroads
Waybills

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10.1016/j.retrec.2018.10.004

Cite this

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title = "A method for processing the Confidential Carload Waybill Sample for railroad freight analysis",

abstract = "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.",

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AB - 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.

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

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