Identifying the key factors of intermodal travel using interpretative ensemble learning
Bibliographic record
Abstract
• Development of a novel interpretability-based ensemble learning model to identify key factors affecting intermodal travel • Differences in feature interpretability between the developed model and the logit model were investigated • The developed model was tested on multiple datasets Intermodal travel is considered an effective method for achieving sustainable urban transportation. Understanding the factors influencing intermodal travel is crucial. Due to the relatively small proportion of intermodal trips within cities, datasets are significantly imbalanced, leading to poor performance of traditional logit models. In this paper, we develop a novel interpretable ensemble learning (IEL) model to identify key factors through voting by five types of machine learning models. We test our model on two datasets with different numbers of features. The results show that travel duration, travel distance, vehicle ownership, and distance to the nearest metro station are the key factors influencing intermodal travel, cumulatively contributing nearly 70% in the JDS2021 dataset with 14 features and nearly 80% in the SHS2019 dataset with 8 features. Furthermore, we analyze the interpretability of our model and compare it with the logit model. Our model enriches the methodology for modeling intermodal travel behavior.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".