Understanding and Estimating In-Service Axle Weights of Transit Buses
Bibliographic record
Abstract
Despite evidence that certain transit buses exceed axle weight limits (sometimes without any passengers on board) and mostly anecdotal indications of the concomitant pavement impacts, relatively little empirical evidence substantiates these impacts. The number of transit buses exceeding weight limits has been exacerbated by regulatory changes directed at improving emissions and accessibility. These changes have required manufacturers to include heavy auxiliary equipment, such as emissions reduction components and hydraulic systems, on transit buses and have been introduced without commensurate increases in the weight limits of transit buses. Public agencies have limited knowledge about transit bus weights and their pavement impacts. Further, estimating in-service weights of transit buses is difficult. To help improve knowledge about issues of transit bus weights, this paper describes the legal and regulatory factors surrounding transit bus weight, basic estimates of in-service bus weights, contributing factors to transit bus weights, challenges for reducing bus weights, pavement impacts of transit buses, and challenges for estimating in-service weights of transit buses. The paper also develops and applies a methodology for estimating in-service weights of transit buses by using Winnipeg, Manitoba, Canada, as a case study. Application of this methodology ( a) demonstrates the need for reliable local data when in-service weights of transit buses are estimated and ( b) empirically corroborates regulatory compliance and pavement-related concerns by using data collected from in-service transit buses.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".