Sketch Model for Estimating Station Level Ridership for LRT
Bibliographic record
Abstract
The City of Edmonton aims to undertake an evidence-based approach to prioritize its 2047 unfunded LRT network for implementation. The Regional Travel Model (RTM) developed by the City does not explicitly model LRT as a separate mode and in general, like other regional-scaled models, is limited in its ability to accurately reflect station-level patterns. On the other hand, a Direct Ridership Model (DRM) estimates ridership as a function of the station’s environment and transit service attributes. A DRM can be used as complementary to the traditional four-step models like the RTM to provide a better understanding at a local level, which a system-wide analysis cannot provide. It also provides significantly greater flexibility than a RTM for evaluating different land use and service scenarios, and with minimal calibration, is transferable to other major urban areas. In this study, we developed a DRM of LRT stations using data collected for 15 existing stations in the City of Edmonton straddling over two horizon years. Individual DRMs were developed for the a.m. and p.m. peak periods after an assessment of the available data. The results indicate that population, employment, and post-secondary enrollment within a 1000m buffer around each station; along with, bus frequency, distance to CBD, parking spaces, and CBD stations were statistically significant variables. Five methods to expand the peak period station level boarding to daily estimates were also developed to understand the potential range of daily forecasts the City could expect to experience in the future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".