International Research Progress in School Travel and Behavior: A Literature Review and Bibliometric Analysis
Bibliographic record
Abstract
A deep understanding of school travel mode can help policymaking related to the optimization of the school travel structure, alleviating urban traffic congestion due to the increasingly prominent phenomenon of urban sprawl. However, existing studies in this field are based on a specific research perspective, and comprehensive reviews are rather limited. Therefore, this study aims to provide an in-depth, systematic review of school travel by using bibliometric analysis. Firstly, based on the Web of Science, TRID, ScienceDirect, and MEDLINE databases, 457 studies about school travel are selected from between 1996 and 2021. Secondly, utilizing bibliometric analysis, the research progress is summarized with emphasis on the annual performance of the literature, publication status of a country or region, literature source institutions, keywords of the literature, and co-citation network analysis. The research results show that (1) the United States, Canada, and Australia rank top in the number of studies on school travel, and they also have high citation frequency and connection strength. (2) This study collects studies published in 34 journal publications, and the “Journal of Transport & Health” is the main source for publishing research. (3) The choice of school travel mode is significantly affected by individual characteristics, family conditions, and social status. The built environment and parental factors play a leading role in students’ active travel to school, and independent mobility and active transport contribute to students’ healthy development. However, policy planning is necessary to further improve the transportation infrastructure sustainability and school route safety. (4) Finally, several promising directions and potential limitations are discussed for developing countries based on the research progress in developed economies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.161 | 0.211 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".