OPUS: High-Performance Automated Fare Collection for Quebec's Public Transport
Bibliographic record
Abstract
In 2008, the Societe de Transport de Montreal (STM), in partnership with seven other Quebec transport organization authorities, launched a new smart card-based fare collection system called OPUS. This article describes the development of the system and highlights how it has modernized public transit in Quebec. The goals of the automatic ticketing system were to improve performance through control of expenses and diversification of revenues; to improve customer satisfaction; and to increase ridership. The project involved the introduction of new ticket media and the replacement of fare collection equipment in metro stations, suburban railway stations and on buses. Since the beginning of the deployment, more than 3.5 million OPUS cards have been distributed. Over one million OPUS card validations are made in Montreal each day, and customer satisfaction with the system has reached 90%. The OPUS card has increased the modal share of public transportation by making it easier to meet customers’ varied fare needs, such as combining different transportation tickets on a single card. The deployment process also has had the benefit of increasing synergy and cooperation among the partnering authorities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".