Modelling Link-Level Shared Micromobility Demand: Regression and Neural Network Approaches
Bibliographic record
Abstract
Shared micromobility is a relatively new transportation mode that has the potential to replace short-distance motor vehicle trips. To encourage shared micromobility ridership, transportation practitioners must understand the determinants of micromobility demand. Previous research has examined micromobility demand on a zonal basis; however, the decision to build micromobility and active transportation infrastructure happens on a link basis. This study presents a linear regression model, a spatial lag model, and a neural network that are used to examine the impact of the built environment on micromobility demand in Calgary, Canada using micromobility data from 2019–2023. Point of interest density was found to be a more important predictor of micromobility demand than road classification or active transportation infrastructure. Urban boulevards are the road classification most associated with increased demand. Active transportation facilities, especially those that separate users from motor vehicles, are also associated with higher micromobility volumes. Of the three models, the spatial lag model had the most predictive power because it accounted for spatial relationships. This demonstrates the importance of accounting for spatial lag and error when evaluating micromobility demand at a link level. The results of this study may provide insight for future micromobility and active transportation infrastructure developments.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".