Discovering global trends of design in sustainable transportation: a bibliometric analysis and visualization
Bibliographic record
Abstract
Several studies on sustainable transportation have been explored in multidisciplinary background fields. However, the social approach still lacks a connection between sustainable transportation studies and the design field. Hence, the bibliometric analysis was conducted to scope the design, sustainability, and transportation research trends. Bibliometric details extracted 254 documents from the Scopus database from 1969 until September 2023 with the research strings of "sustainable transportation design" OR "sustainable transportation" AND "transportation design" OR "design*." Adopted PRISMA was used as methodology, and the analysis of 254 documents was divided into five sections: Document Type and Language Used, Research Trend and Citation Pattern, Productive Author Analysis, Top Countries Contributed to The Publication and Topical Cluster by Keyword and Title-Abstract Analysis. The results showed the trend of research increasing over the years while the citation fluctuated due to the different times of citation impact. The Productive author was Awasthi (TC=308; NCP=3; h=3), and the top countries are the United States, China, and Canada. The top keyword data extracted from the Scopus database showed that "Sustainable Transportation" was the highest keyword in publications (TP=124; P=48.82%), the keywords "Transportation" came at the second-highest (TP=66; P=25.98%), and the third-highest keywords in the publication was "Sustainable Development" (TP=55; P=21.65%). The exciting part to acknowledge is that the keywords "design" with (TP=14; P=5.51%) also appeared in the Top 20 of the highest keywords in publication. The keywords, title and abstract analysis led to the topical cluster that showed the connection between sustainability, transportation and design through network map visualization. The study concluded with discussions on the contribution by feeding the gaps, limitations, suggestions on design perspectives, database comparison and in-depth content analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.029 | 0.069 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".