Assessing and Comparing Data Imputation Techniques for Item Nonresponse in Household Travel Surveys
Bibliographic record
Abstract
This research provides a comparative assessment of data imputation techniques for item nonresponse in household travel surveys. Using the Transportation Tomorrow Survey (TTS) data for the Region of Waterloo in Ontario, Canada, a series of synthetic datasets are generated with varying amounts of missing data, while preserving the respective proportions of missing items and missing item combinations in the original survey data. Then, the performances of six different imputation techniques are compared. The six different imputation techniques include two simple imputation techniques (mode and hot-deck), three discriminative models (logistic regression, multi-layered perceptron, support vector machines) and one generative model (autoencoder). This assessment compares these techniques, as well as the impact of the proportion of item nonresponse in the dataset through their repeated application to multiple synthetic datasets. Results show that the machine/deep learning techniques (both generative and discriminative) not previously applied to household travel survey data outperform their simple imputation counterparts. Overall, the accuracy of travel household survey data imputation is shown to depend on many factors, including the technique employed, the dimensionality of the missing item, and the hypertuning of the technique (if applicable), but not on the amount of missing data in these experiments. This research should prove beneficial to practitioners who often confront item nonresponse in their household travel survey data by providing evidence and recommendations to support the selection and implementation of a data imputation technique. The research methodology also provides a repeatable procedure for future researchers to test data imputation techniques on their own datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.085 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".