UNET and UNETR Based Frameworks for Predicting the Short-Term Spatiotemporal Demand of E-Scooter Sharing Services
Bibliographic record
Abstract
Several factors have contributed to the emergence of shared on-demand mobility services in recent years, including urbanization, technological advancements, environmental concerns, changing consumer behavior, regulatory changes, and cost savings. Among these services, shared electric micro-mobility services represent the latest entrant to the market. One significant challenge for shared mobility services is that demand for these services can vary significantly throughout the day and across different locations, leading to imbalances in vehicle availability. This can result in long wait times for users, negatively impacting user experience and discouraging future service usage. In this study, we propose the use of the state-of-the-art deep learning models, such as the UNET and UNETR, for short-term spatiotemporal micromobility demand prediction. Our study reveals that UNETR surpasses the baseline model in predicting demand for the entire region of interest. For the next-hour pick-up and drop-off demand prediction, UNETR achieves mean absolute errors of 0.0163 and 0.0158, respectively, while for the next 24-hour prediction, the errors are 0.0166 and 0.0158, respectively. Additionally, UNET outperforms the baseline model and UNETR at the nonzero demand level, with mean absolute errors of 1.4886 and 1.4430 for the next-hour prediction, and 1.5607 and 1.5339 for the next 24-hour prediction, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".