Procedure for combining field measurements and machine learning to quantify impact of different track parameters on ride quality of railway tracks
Bibliographic record
Abstract
Ride quality in terms of vibration is a fundamental factor affecting passengers’ satisfaction. Every year, passenger carriers invest significantly in various aspects of their system, including track and infrastructure, to improve ride quality. The assessment of ride quality and understanding the extent of the impact of different parameters on its magnitude is essential for railway operators to make informed decisions regarding capital expenditures. This paper presents a methodology for using machine learning techniques to find the correlation between various parameters (including train speed, weather conditions, presence of track features, and composition of the track substructure) and ride quality (quantified using measurements from accelerometers mounted on a rail car). The statistical model was developed using field measurements collected over a 50 km section of VIA Rail’s track in Canada. This paper describes the collected field data, the development of the statistical model, and discusses the importance of each parameter on the accuracy of the model.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".