A Latent Class Pattern Recognition and Data Quality Assessment of Non-Commute Long-Distance Travel in California
Abstract
This study analyzes 8-week long-distance travel records from the California Household Travel Survey for completeness and identifies general types of non-commute long-distance tours using Latent Class Analysis. Likely due to the difficulty of gathering data of this kind, there has been relatively limited study of non-commute long-distance travel, despite the substantial contribution to many households’ greenhouse gas emissions and travel expenses. The California Household Travel Survey includes a valuable long-distance 8-week travel dataset, but this study identifies several possible shortcomings in the dataset. Of particular importance is a severe underreporting of shorter trips, which may result from a mix of respondent forgetfulness and survey fatigue. Despite the issues with the data, latent class cluster analysis was able to identify five distinct, informative patterns of long-distance travel. This analysis shows that long-distance tours for vacation, business travel, medical, and shopping are substantially distinct in terms of their travel characteristics and correspond to different combinations of other activities in the tour, and they are done by different types of households. The method used here to identify the typology of long-distance travel can be easily expanded to include a variety of other explanatory variables of this type of behavior in more focused data collection settings.
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Article first published online: July 13, 2018
Issue published: December 2018
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The authors confirm contribution to the paper as follows: study conception and design by Davis, McBride, Goulias; data collection and processing by Davis, McBride; analysis and interpretation of results by Davis, McBride, Goulias; draft manuscript preparation by Davis, McBride, Goulias. All authors reviewed the results and approved the final version of the manuscript.
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