Bibliographic record
Abstract
This paper evaluates the impact of adding a reserved bus lane on the running times and on-time performance of two parallel bus routes, one of them a limited-stop bus service and the other a regular bus service. By means of automatic vehicle location and automatic passenger count data, statistical models were built to estimate running time and on-time performance. The reserved bus lane yielded savings of 1.3% to 2.2% in total running time, and benefits were more significant for northbound afternoon peak trips than for southbound morning peak trips because of congestion levels northbound. The introduction of a reserved lane increased the odds of being on time by 65% for both routes. A decline in the variability of running time and delay at the end was noticed after implementation of the reserved lane; the decline indicated that the reliability of the service being offered along the corridor had improved. The analysis showed that the more affected a bus service was by adverse traffic conditions, the more it benefitted in running time from improvements introduced by reserved lanes while keeping schedules constant. Reserved lanes had a substantial effect on both service reliability and on-time performance, two key variables in customer satisfaction that justified such implementation. This study will help transit planners and schedulers to understand the effects of implementing reserved lanes on running time, on-time performance, and transit 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".