Imputing informal workers for transportation modeling in Latin America by the use of machine learning techniques
Bibliographic record
Abstract
The informal economy plays a critical role in the Global South, particularly in large urban areas such as Mexico City, where over 50 % of jobs belong to this segment. It is crucial to understand the travel behavior of informal workers and effectively integrate these patterns into advanced transportation models, such as activity-based models (ABMs). This study proposes a unique approach for identifying informal workers across distinct economic sectors in Mexico’s Monterrey metropolitan area, by utilizing an Origin-Destination survey and the National Occupation and Employment Survey (ENOE). Machine learning models, trained on the ENOE dataset and applied to the OD survey, first classified workers as formal or informal, and subsequently reassigned informal laborers who had initially been classified under “Other" to the construction or commerce categories. The use of the Gradient Boosted Trees (GBT) classifier emerged as the optimal method, yielding accuracies of 78.0 % and 70.7 % for the two stages. Differences between predicted results and observed values fall within an acceptable range, especially in sectors with high informal worker rates. The resulting estimate and characterization of informal workers can potentially be integrated into ABMs, thereby providing a foundation for assessing the responses of informal workers to infrastructure policy interventions. • Identifying informal workers allows the characterization of their travel patterns. • Machine learning techniques identify these workers from surveys in Monterrey, MX. • Gradient Boosted Trees demonstrated to be the best method for worker identification. • Informal workers can then be included in activity-based models.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".