Revenue Alterations of Shared Automated Mobility Services Integrated into Mobility as a Service
Bibliographic record
Abstract
The concept of mobility as a service (MaaS) is integrating mobility services and offering them to users via a single means. The introduction of shared autonomous vehicles (SAVs) and adaptation of revenue models must be tackled in the MaaS concept. As the scientific basis for this is incomplete, we forecasted the alterations in the revenue of SAVs after the integration into MaaS. We analyzed the existing revenue models used by shared mobility services and MaaS, and used a revenue model framework (RMF) to explore the possible sources of revenue. Our methodology contains the before-and-after analysis and considers the MaaS integration levels. The main findings indicate that MaaS takes over revenue sources from mobility service providers as the integration levels increase. Also, the commission paid to MaaS is a new cost for mobility service providers. Then, integrating SAVs into MaaS is beneficial if the revenue increase caused by number of trips outweighs the loss generated by the responsibilities taken by MaaS. Finally, the door-to-door SAV services exist in all integration levels, but feeder is only present at level 3. MaaS operators and SAV mobility service providers can use our findings during negotiations of contracts, and the integration process can be accelerated.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".