What makes public transit demand management programmes successful? A systematic review of ex-post evidence
Bibliographic record
Abstract
Transit crowding results in negative experiences and mode change for transit riders and operational challenges for operators. The COVID-19 pandemic initiated an ongoing transformation of how, when, and where people travel, yet the challenge of balancing demand and supply in transportation remained topical. The pandemic has also exposed the traditional approach of infrastructure expansion for being too slow to respond to the challenges of crowding in a timely manner. As such, this paper provides a systematic literature review of the ex-post studies that evaluated the impact of transit demand management strategies. The paper synthesises the findings from 13 different programmes analysed in 20 studies. It is concluded that at least within the scope of the limited number of identified ex-post studies, the practice of alternative work schedules that allow employees greater freedom when to travel is the demand management approach that can bring the most significant crowding reduction. Once that flexibility is expanded, other strategies that appeal to riders’ preferences might have a larger effect as well. The findings of this review aim to encourage transit agencies to develop collaborations with large employers that can introduce alternative work schedules.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".