Can the co-existence of bike, e-scooter, and car-sharing promote sustainable mode choices?
Bibliographic record
Abstract
The introduction of shared mobility systems has received attention as a potential measure to reduce private car use and tackle the environmental and social externalities of urban mobility. This research investigates travel mode choice behaviour in the context of the co-existence of three emerging shared modes (car, bike, and e-scooter sharing) with four so-called “conventional” modes (walking, public transport, private bikes, and private cars). Based on data collected via an online survey, including a stated preference mode choice experiment pivoted on real-life trips, of residents ( N = 1448) of three European cities (Vienna, Brussels, and Munich) the study offers insights into mode substitution and its determinants. The analyses revealed that car and bike sharing have the highest potential to replace trips by private cars and public transport, respectively. Interestingly, despite the competition between shared modes and public transport, the introduction of shared mobility services could also enhance the attractiveness of public transport via a decoy effect. These findings suggest that shared mobility systems could have a broader positive effect on the modal split of sustainable mobility services. The results of discrete choice models point out that interest in shared mobility varies across individuals with different sociodemographic characteristics and mobility habits, suggesting that although shared mobility can reduce some mobility gaps, its contribution to overall mobility equity needs further investigation. The study output highlights that while the presence of multiple shared mobility services could bring some social and environmental sustainability benefits, there are also limitations in their potential to advance current conditions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".