An Exploration of the Decline in E-Scooter Ridership after the Introduction of Mandatory E-Scooter Parking Corrals on Virginia Tech’s Campus in Blacksburg, VA
Bibliographic record
Abstract
We report shared e-scooter ridership and rider perceptions on Virginia Tech’s Blacksburg campus before and after introduction of mandatory e-scooter parking corrals in January 2022. The analysis relies on a panel of 131 e-scooter riders surveyed in Fall 2021 and Spring 2022. Although parking corrals were perceived favorably prior to implementation, perceptions became more negative afterwards. Respondents said corrals were not located where needed, difficult to find, fully occupied, and took too much extra time to use. After parking corrals were introduced, ridership declined 72% overall and also fell for all socio-economic subgroups. The heaviest user groups, like undergraduate males, were most likely to quit. The first study identifying desired and actual egress times for e-scooters, we found that roughly two-thirds of riders desired egress times under 2 min and one quarter under 1 min. Prior to the introduction of parking corrals, 82% of riders reported actual egress times under 2 min, and 43% under 1 min. Those who kept riding after the introduction of e-scooter corrals reported longer actual egress times and a stronger stated desire for egress times under 2 min. Communities should be careful when imposing e-scooter parking restrictions to ensure that e-scooter egress time is sufficiently low—ideally within an easy 2 min walk of popular origins and destinations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".