Service and Cost Comparisons of Bus Rapid Transit and Light Rail Transit
Bibliographic record
Abstract
For medium size urban areas, bus rapid transit (BRT) and light rail transit (LRT) are candidates with the potential to serve hourly volumes of 10,000 to 25,000 passengers. Over the years, there has been much controversy about the relative service level and cost-effectiveness between BRT and LRT for this range of demand. Among other reasons for lack of definitive answers, absence of experience with an extensive BRT has been a major one. However, due to the implementation of the transitway system in Ottawa (Canada), and a number of LRT systems operating in Canada, service and cost data have improved. As a result, analyses of service and cost factors can be carried out with confidence. This paper reports research on comparisons of BRT and LRT under identical conditions. The Ottawa region is used as the basis for service (i.e., travel time, frequency, transfers) and cost comparisons. Service and cost models were developed to enable sensitivity analyses wherein these results are presented and discussed. Conclusions suggest that on the basis of the factors included in the models, and for the demand level studied under identical operating conditions, the BRT system offers superior service and cost-effectiveness as compared to the LRT. However, this conclusion should not discourage the implementation of LRT as a complement to the Transitway system as part of an integrated rapid transit network.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".