Understanding Factors Associated with Commuter Rail Ridership: A Demand Elasticity Study of the GO Transit Rail Network
Bibliographic record
Abstract
Planners have theorized that transitioning commuter rail systems to regional rail networks will increase ridership, balance mode share, and reduce automobile use in North American cities. This process is currently underway in Ontario, Canada, as service is being expanded throughout the GO Transit commuter rail network. Calculating elasticities is a common approach used to identify factors that, if adapted, may significantly influence transit demand. However, few studies have focused on identifying demand elasticities specific to the current case of upgrading commuter rail systems in the North American context. The purpose of this study is to fill this gap. Station-level ridership data were compiled for the GO system from January 2016 to December 2019. Smartcard data were used to estimate station catchment areas for which land use, socioeconomic, and demographic datasets were developed. Data about additional factors related to station access, service quantity, fare price, and availability of substitute transport modes were also compiled. Controlling for trip type (e.g., a.m. peak and evening off-peak), demand models were estimated using a random effect panel data estimator. This study finds that service quantity, population density, fuel price, and unemployment rate were significantly associated with commuter rail ridership, regardless of trip type examined. Employment density and seasonal variation were also significant, although different signs were shown between models. The results suggest that plans for this kind of transition should include other considerations in addition to service quantity improvements. Those directed toward the implementation of transit-oriented developments, transport pricing schemes, and competitive fare price strategies are outlined.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".