Mobility Vitality in Active and Micro-Mobility Modes: Measuring Urban Vitality Through Spatiotemporal Similarity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. Urban vitality captures the dynamic and interactive nature of city environments by highlighting how residents engage with public spaces, making it essential for differentiating neighborhoods. Traditional indicators focused on static measures, such as density, land-use diversity, and built environment design. Most of these measures fail to capture the dynamic nature of vitality. This paper introduces the concept of Mobility Vitality, a novel measure that captures the dynamic and vibrant nature of human activities through the analysis of active and micro-mobility modes, including biking, e-scootering, and recreational running. Taking Washington, D.C. as a case study, we analyze the spatiotemporal patterns of mobility across different modes and time periods, revealing significant variations in mobility patterns between the downtown core and peripheral areas. The results also indicate that the most unique time series of the three micro-mobility modes are weekend mornings and weekday nights, and fluctuations are more pronounced within a day than between weekdays and weekends. The proposed analysis framework may guide infrastructure investments, optimize urban transport networks, and advance more equitable and sustainable cities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it