Exploring pavement friction variability factors using ensemble trees and causal inference
Bibliographic record
Abstract
Understanding pavement friction measurement data is necessary to predict future road conditions and determine intervention strategies. Although considerable friction measurement data are collected for these purposes, it is not yet entirely clear how to interpret it. More specifically, there is often unexplained variability associated with these data, which inhibits their use. In this study, we have focused on enhancing the understandability of the data by exploring the causes of the unexplained variability. We constructed a dataset from two decades of friction data on Swiss national roads to explore the influence of different factors, including systematic testing conditions and external factors, on the observed data variations. We used average difference to quantify the degree of variability between consecutive measurements. Explainable ensemble trees and the SHapley Additive exPlanations methods are applied to assess the factors’ contribution to the data variability. Furthermore, a structural causal framework is employed to unravel the factors’ causal effects. Our findings indicate that much of the unexplained variability is related to maintenance interventions, temperature differences, and the speed at which the measurements were taken. These findings demonstrate how the data mining methods confirm the patterns observed in measurements conducted in controlled experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".