An Evaluation of the Impacts of Introducing a New Transit System on Commuting Mode Choice and Transit Ridership: A Case Study of the VIVA BRT-Lite System in Toronto
Bibliographic record
Abstract
The Regional Municipality of York, north of the City of Toronto, implemented a new bus service known as VIVA in 2005. This distinctly branded system operates primarily in two highly-traveled corridors and features high operating speeds, offline fare payment, advanced traveler information systems, and other intelligent transportation system (ITS) technologies. Although this new service has been deemed a success by many, it remains to be seen to what degree transit use was affected by its introduction. To evaluate this, home-based work and post-secondary school generalized extreme value (GEV)-class discrete models are estimated. In the work trip model, two mode choice nests were identified: Auto (comprising auto driver and auto passenger) and Root (comprising all other modes). It was found that auto trips were more easily predictable than transit trips and that there is an appreciable difference in the heteroskedasticity of choice between occupation groups. No nesting structure for post-secondary trips was statistically identifiable. Improvements in transit service were found to have a greater impact on transit mode share than increases in congestion for both work and post-secondary school trips. It is also concluded that transit improvements played a relatively small role in the considerable shift to transit amongst post-secondary students. It is posited that VIVA attributes such as improved branding, advertising, and communications may have caused this change in preferences.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".