Application of Multiple Imputations to Freight Transportation Survey Data: A Case Study of Commodity Flow Survey
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Copyright: © 2020 Terdsak Rongviriyapanich and Akachut Suppiyatrakul. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: Freight transportation data was indispensable input to transportation planning. In Thailand, efforts had been put to collect freight movement data by conducting road side survey and commodity flow survey. The results of these surveys did not produce consistent volume of shipment due to limited sampling coverage and non-response. Nevertheless, freight distribution patterns, which were derived from these surveys, were favorably consistent with each other. Approach: The objective of this study was propose an approach to improving quality of the commodity flow survey data in terms of total shipment weight. Our scope of study was limited to consumer goods and food stuffs. Multiple imputations were performed to correct non-response. The shipment weight was again adjusted by taking into account of the probability of no shipment in a particular quarter. Results: Comparison between the adjusted weight and road side survey data showed that the discrepancies in total weight of significantly reduced. Conclusion: Total shipment weights of the CFS after the adjustments are compares to those of road side survey. Plausible result is obtained for the case of consumer goods, while that of food stuffs is still notably different.
- Commodity flow survey
- Multiple Imputation (MI)
- Missing Completely At Random (MCAR)
- shipment weight