Empirical Analysis of Long-Run Elasticities and Asymmetric Effects of Transit Demand Determinants
Bibliographic record
Abstract
The effects of transit ridership determinants can be quantified as demand elasticities which are often used to inform transit planning and policy making. This study seeks to determine the impacts of transit service supply, fare, and gas prices on ridership by quantifying the short-run and long-run demand elasticities, as well as test whether transit ridership exhibits an asymmetric response to the rise and fall of these factors using a panel data of 99 Canadian transit agencies over the period of 2002–2016. The results of the dynamic panel model show the effects of transit service and fare to be greater in the long run. The short-run fare elasticity was found to be –0.24 while the long-run elasticity was –1.1. Furthermore, the demand elasticity with respect to service levels was also found to be inelastic (0.28) in the short run but elastic (1.3) in the long run. The cross-elasticity of gas prices was estimated to be 0.17. The existence of asymmetry was analyzed using decomposition techniques to separately estimate the coefficients for the rise and fall in each of the determinants. The equality of these coefficients was tested against each other and it was found that ridership responded more to an increase in transit supply than a decrease. The importance of these results to policy making are then discussed.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".