Assessing the impact of Mobility-as-a-Service (MaaS) on sustainable urban travel behaviors: a systematic literature review
Bibliographic record
Abstract
Mobility-as-a-Service (MaaS) is widely promoted as a way to reduce car dependency and encourage sustainable urban mobility, yet its actual environmental contribution remains unclear. This article presents a systematic literature review of 85 studies conducted in line with PRISMA guidelines to identify and analyze the elements within the MaaS ecosystem that influence sustainable travel behaviors. The evidence base spans multiple disciplines including psychology and behavioral sciences, sustainable development, urban planning, and transportation engineering, with most studies adopting cross-sectional survey approaches rather than examining longitudinal behavioral change or multimodal system integration. Findings show that adoption is consistently driven by convenience, affordability, technological appeal, and service reliability, while explicit sustainability motivations play a secondary role. Public transport integration, pricing structures that favor low-emission modes, and targeted subsidies for disadvantaged groups are linked with positive sustainability outcomes, whereas shared mobility services such as bike-sharing, e-scooters, and ride-hailing often substitute for existing low-emission modes rather than replacing private cars. Moreover, MaaS bundles that include car-based services can unintentionally stimulate car use, showing that measures designed to encourage adoption may work against sustainability goals. To address these challenges, the paper develops a conceptual framework that illustrates how governance arrangements, economic incentives, service design choices, and user engagement strategies interact to shape both platform uptake and environmental impacts. This framework emphasizes that adoption and sustainability are driven by different mechanisms and often work against each other, which highlighting the need for coordination to ensure that MaaS strengthens, rather than undermines, transitions toward sustainable urban mobility.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".