Travel mode choice prediction: developing new techniques to prioritize variables and interpret black-box machine learning techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Travel Mode Choice (TMC) prediction is vital for forecasting travel demand and transportation planning. To be helpful for those purposes, one needs to know with high accuracy what influences choices and how. For accuracy, Machine Learning (ML) classification techniques often produce results with higher accuracy than traditional methods. However, many ML techniques are black-box tools, making them less useful for planning. To this end, two new approaches were proposed to interpret the results of ML techniques and investigate the influence of different variables on TMC. The results suggested that ensemble learning techniques outperform other prediction methods. Adding accessibility, geographic, and land-use variables to the conventional TMC prediction models could improve their performance. The most important parameters for TMC were found to be: trip distance, availability of a transit pass and availability of a driver’s license. Their respective influences on the different modes are demonstrated using the novel method mentioned above.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it