The future of emerging technologies in public transit in Greater Toronto Area
Bibliographic record
Abstract
In communities throughout the world, strong and convenient public transportation makes valuable contributions to economic development, increased safety, energy conservation, a cleaner environment, less traffic congestion, and an improved quality of life. Whether it's a disabled person on her way to a doctor appointment, a child on the way to the library, or an elderly person going to buy groceries, rails, buses and vans connect people to their community. While transit serves many purposes, one of the most important of which is to provide critical access and mobility for transit-dependent and lower-income residents country wide, it also reduces the pressure on critical commute corridors by offering a convenient alternative to driving alone. People who are dependent on public transit, the young or the old, the disabled or the low-income, deserve a first-class system. A survey was conducted by City Pulse Toronto (CP 24) and the question they put to the viewers was "Would improved public transit convinces you to give up your car?" The result was amazing that 96% of the people using cars opted for Public transit. In the last decade statistics depict that the cities that have adopted emerging technologies in public transit are reaping the benefits of their increased rider ship by almost three fold. It is disappointing to see that the transit-using trend in Greater Toronto Area (GTA) has decreased in the past five years except in the regions where transit agencies are updating their systems. Throughout the North America and other parts of the world, transit agencies are deploying automatic vehicle location and control fleet management systems, electronic and interactive customer information systems, and contact/contactless fare collection systems to save costs, improve operations and management efficiency and provide better service to customers. In this project an effort is made to depict the extent of adoption of advanced technology in the provision of public transportation service in Greater Toronto Area. The focus is on some of the most innovative or comprehensive implementations, categorized under two types of services/technologies, Automatic Passenger Counting and Electronic Fare Payment. Another objective of this study is to assemble the knowledge on successful applications of advanced technologies, the issues in their implementation, the goals and benefits of Intelligent Transportation System's integration. The study focuses on institutional, operational and technical barriers with the expectation that this will lead to more widespread adoption of ITS systems and techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".