Evaluation of Proof-of-Payment and Multiple-Unit Operations in Different Streetcar Route Configurations
Bibliographic record
Abstract
This study investigates the performance effects of Proof-of-Payment (POP) and Multiple-Unit (MU) streetcar operations when applied individually and jointly to 2 streetcar routes with different configurations and characteristics. Using a microscopic traffic simulation model, the 504 King and the 512 St. Clair streetcar routes in Toronto are investigated. The 2 routes have different operational characteristics. Currently, both routes suffer from major reliability problems including streetcar bunching and gapping. The purpose of POP is to reduce the magnitude and variability of passenger service time. In MU, individual streetcars are coupled while doubling the service headway. To estimate the impact of these measures, a state-of-the-art modeling tool was applied to replicate the existing and proposed scenarios. In general, the Proof-Of-Payment system shows better performance than the Pay-On-Entry system, and Multiple-Unit operation shows better performance than Single-Unit operation. In all cases, POP results in lower headway variability, shorter route travel times and reduced magnitude and variability of passenger service times. On both routes, MU offers the benefits of lower headway variability, fewer extreme headways, shorter route and section travel times, fewer passengers left behind and lower crowding at the peak load stop. In addition, the results point to the benefits of implementing a bundle of strategies since the scenarios with both Proof-Of-Payment and Multiple-Unit operation consistently perform best.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".