Abstract
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.
Highlights
Freight transportation plays an important role in economic development
Freight movement data is an indispensable input to transportation planning
It is an indispensable input to truck origin-destination matrix estimation, which is very useful for freight transportation planning
Summary
Freight transportation plays an important role in economic development. It is used to move raw materials and products from origins to destinations. It is an indispensable input to truck origin-destination matrix estimation, which is very useful for freight transportation planning It needs cohesive cooperation from respondents as the survey period is designed to cover all quarters in a year. It may be stated that, at present, freight survey data in Thailand need to be refined due to limited sampling coverage in the case of road side survey and non. Non-response is defined as data, of which respondent reports no shipment in all quarters. We opted to classify the data as non response due to the fact that it is very unlikely that an establishment having no shipment in any quarter throughout a year. Non response has been treated as zero shipment, resulting in considerably low estimates of shipment weight as will be discussed adjustment of the CFS data. Lack of reliable source of secondary data in Thailand makes it impossible to validate the survey
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