Modeling distributions of travel time variability for bus operations
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Bibliographic record
Abstract
Summary Bus travel time reliability performance influences service attractiveness, operating costs, and system efficiency. Better understanding of the distribution of travel time variability is a prerequisite for reliability analysis. A wide array of empirical studies has been conducted to model distribution of travel times in transport. However, depending on the data tested and approaches applied to examine the fitting performance, different conclusions have been reported. This paper aims to specify the most appropriate distribution model for the day‐to‐day travel time variability by using a novel evaluation approach and set of performance measures. Two important issues are explored using automatic vehicle location data collected on two typical bus routes over 6 months in Brisbane, namely, data aggregation influences on travel time distribution and comprehensive evaluation of performance of distribution models. The decrease of temporal aggregation of travel times tends to increase the normality of distributions. The spatial aggregation of link travel times would break up the link multimodality distributions for a busway route, but unlike for a non‐busway route. The Gaussian mixture models are evaluated as superior to its alternatives in terms of fitting accuracy, robustness, and explanatory power. The reported distribution model shows promise to fit travel times for other services with different operation environments considering its flexibility in fitting symmetric, asymmetric, and multimodal distributions. The improved statistic fitting can support more effective service reliability analysis. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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