A Comparative Analysis of Micro-Mobility Services in Atlanta, GA and Washington, D.C.
Bibliographic record
Abstract
Micro-mobility vehicles, or self-driven, lightweight transportation devices such as bicycles and scooters, are becoming increasingly popular modes of transport in urban areas.Since 2017, several private companies have introduced dockless micro-mobility sharing services to various American cities.In this thesis, I compare the usage patterns of shared dockless electric scooters in Atlanta, Georgia, and Washington, D.C., in the summer of 2019 to identify which populations use micro-mobility services in each city.I incorporate descriptive statistics, spatial lag & negative binomial regression models, and the results of a participant survey to demonstrate that there are notable differences in scooter usage characteristics between the cities.Simultaneously, I show that the clustering patterns and specific scooter usage predictors are similar in Atlanta and Washington.These findings will help urban planners assess the impact of shared micro-mobility services on existing transportation systems and evaluate their role in serving different populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".