Optimizing Station Placement for Integrated Shared Micromobility and Public Transit Networks
Bibliographic record
Abstract
Conventional transit network planning relies on assumptions about passenger behaviors that are becoming challenging to predict due to the growing popularity of smart mobilities, such as shared micromobility as an access mode. This has created a notable knowledge gap in multimodal transport planning, and the current study aims to address this gap by proposing a model to optimize station locations in an integrated transport approach. The study jointly optimizes the density of transit stations and micromobility docking stations, introducing a new approach to multimodal transport planning. The fixed and variable costs of the two modes, as well as the user costs, are evaluated. The findings show that an increase in the density of micromobility stations balances the reduction in transit station density as transit station costs rise. This highlights the supplementary relations between these two modes in an integrated transport network. The results also showed that travel demand increases transit station density while micromobility station density is less affected. However, micromobility station density is more sensitive to their market penetration levels. The findings provide insights for decision-makers to understand the synergies between shared mobility and public transit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".