Big data applications in intelligent transport systems: a bibliometric analysis and review
Bibliographic record
Abstract
Big Data applications have transformed Intelligent Transport Systems (ITS), enabling improvements in traffic management, safety, and efficiency. This study presents a bibliometric analysis and review of the recent advancements of big data applications in ITS. For this bibliometric analysis, the Scopus database was utilized due to its extensive resources. Various tools, such as RStudio, VOSviewer, Excel, and Python, were used to analyze data and identify trends, patterns, and relationships in the selected articles through performance analysis and science mapping. The study examined 447 articles published between 2014 and 2023. The analysis indicates that research in this field has experienced exponential growth annually, although it experienced setbacks in 2020 due to the global pandemic before regaining momentum. While the number of research publications has risen sharply, the slower growth in citations highlights the need for a greater focus on producing higher-quality research. Our investigation revealed that the most significant research efforts focused on traffic flow prediction, traffic anomaly prediction, traffic safety, the integration of big data with the Internet of Things (IoT) and the Internet of Vehicles (IoV). China, United States, and Canada were the primary contributors to this field, with China conducting the majority of studies. We summarized and critically reviewed the most cited papers, as well as those that present the most significant innovations in this field. We found that ethical, privacy, and security concerns related to the use of Big Data in ITS have received limited attention. This work aims to serve as a valuable resource for researchers and practitioners, encouraging innovation and the development of more effective and sustainable transportation solutions.
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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 | Observational | high |
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 | 0.020 | 0.052 |
| 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, 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".