Performance Metrics and Data Mining for Assessing Schedule Qualities in Paratransit
Bibliographic record
Abstract
The simple productivity measures and hard constraints used in many paratransit vehicle scheduling software programs do not fully capture the interest of all the stakeholders in a typical paratransit organization (e.g., passengers, drivers, municipal government). As a result, many paratransit agencies still retain a human scheduler to look through all of the schedules to manually pick out impractical, unacceptable runs. (A run is considered one vehicle's schedule for one day.) The goal of this research was to develop a systematic tool that can compute all the relevant performance metrics of a run, predict its overall quality, and identify bad runs automatically. This paper presents a methodology that includes a number of performance metrics reflecting the key interests of the stakeholders (e.g., number of passengers per vehicle per hour, deadheading time, passenger wait time, passenger ride time, and degree of zigzagging) and a data-mining tool to fit the metrics to the ratings provided by experienced schedulers. The encouraging preliminary results suggest that the proposed methodology can be easily extended to and implemented in other paratransit organizations to improve efficiency by effectively detecting poor schedules.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".