A Tale of Four Canadian Cities: LRT Systems and the COVID-19 Pandemic
Bibliographic record
Abstract
Light rail transit (LRT) systems have been proposed and implemented in many jurisdictions in Canada and abroad to cope with long-standing problems associated with urban sprawl. LRT systems are presented as an alternative mobility option to automobiles, a solution to alleviate traffic congestion, attract new property developments in existing urban areas close to downtown and enable higher density developments. However, there has been limited discussion and research on evaluating whether the LRT systems achieved their original policy goals and objectives. Thus, this research aims to address the performance of Canadian light rail transit (LRT) systems with respect to ridership and land development, and investigate the impact of the COVID-19 pandemic on meeting the original policy goals and objectives of LRT systems. Four LRT systems in Waterloo, Calgary, Vancouver and Ottawa were chosen as case studies to facilitate the research. \n \nThe research identified multiple Key Performance Indicators (KPIs) and created two scenarios to evaluate the LRT system performance and the impact of the pandemic on daily public transit commuting ridership. This research finds that the four case study LRT systems generally had satisfactory performance and mostly achieved their original policy and goals. During the peak of the pandemic, it is estimated that daily public transit commuting ridership decreased by over 40%, while the “new normal” scenario estimated a 20% drop in commuting trips as work from home policies become permanent in some workplaces. However, changes in ridership vary greatly among census tracts (CTs) as some CTs are estimated to experience much larger declines in ridership due to a concentration of industries which has higher potential for telework. While the COVID-19 pandemic is expected to impact the achievement of some policy goals and objectives of LRT systems, the extent of the impact is uncertain due to the ongoing changes of the pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".