Examining the adoption potential of a new travel chain integrating electric vehicle sharing and rail transit
Bibliographic record
Abstract
Electric Vehicle Sharing (EVS) can reduce transport-related emissions, yet its scalability faces operational and cost barriers. Integrating EVS with Rail Transit (EVS + RT) offers a sustainable mobility pathway by enhancing first-/last-mile connections. Prior studies often view EVS as a substitute for traditional modes, overlooking multimodal adoption willingness. This study investigates EVS + RT adoption via a web-based commuter survey, analyzing demographics, travel patterns, and latent preferences. A Mixed Logit model quantifies the roles of income, environmental awareness, commute distance, and station proximity. Findings show that access to transit hubs, charging convenience, and unified payment platforms raise adoption, while cost sensitivity and car dependence hinder it. Results highlight the need for user-centered planning, such as optimizing EVS station placement and dynamic pricing. By integrating practical and psychological factors, the study provides policy insights to scale EVS + RT and support low-carbon urban mobility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".