Unlocking the Maze: Exploring Nested Ecosystem of Mobility as a Service through Systematic Literature Review
Bibliographic record
Abstract
Technological advancements in the transportation sector have enabled new mobility solutions. Mobility as a Service (MaaS) is one such example that represents the integration of information technology‐enabled apps with transport modes to provide door‐to‐door and affordable transport options to substitute private cars. Research in transportation is growing in focus on MaaS, and so are commercial MaaS products in various developed countries across the world. This study employs the systematic quantitative literature review approach to select scientific research articles on MaaS published to date and proposes a nested ecosystem framework involving actors, infrastructure, value, and customers. The ecosystem framework presented in this review provides valuable guidance to both transport sector academics and practitioners, highlighting the challenges involved in the successful deployment of MaaS schemes. In the end, this review provides future research directions to expand knowledge on MaaS to answer questions in the wake of fast‐growing transport technology and global mobility patterns.
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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 | 0.037 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.031 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".