The design and development of an algorithm for automatically creating transit networks in transcad to measure regional accessibility
Bibliographic record
Abstract
The Metrolinx Economic Analysis team commissioned Arup Canada to create a tool to measure regional transit accessibility called WithinReach. It allows transportation professionals to quickly evaluate and compare existing services versus potential projects, using availability metrics. The calculation requires the summarization of all transit route operations across a geographic area into performance standards for one service day. In the Greater Toronto-Hamilton Area (GTHA), detailed timetables are available in the General Transit Feed Specification (GTFS) format, however manually preparing this data for WithinReach is labour-intensive. Due to the frequency of changes to vehicle and route schedules, and the number of regional agencies, maintaining an up-to-date summary of operations is cumbersome. This report covers the design, development, testing, procedure, and validation of a Transit Network Builder (TNB) to automate this effort. The TNB abstracts GTFS schedule data into performance benchmarks for use with the WithinReach Tool and the TransCAD transportation planning environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".