Urban Transportation Data Research Overview: A Bibliometric Analysis Based on CiteSpace
Bibliographic record
Abstract
Urban transportation data are crucial for smart city development, enhancing traffic management’s intelligence, accuracy, and efficiency. This paper conducts a comprehensive investigation encompassing policy analysis, a literature review, concept definition, and quantitative analysis using CiteSpace from both domestic and international perspectives. Urban transportation data comprise multiple dimensions, such as infrastructure status, real-time monitoring, policy planning, and environmental assessment, which originate from various sources and stakeholders. Highly influential authors and active institutions, particularly in the USA, China, Canada, and England, contribute significantly to extensive and collaborative research. Key areas include intelligent transportation, traffic flow prediction, data fusion, and deep learning. Domestic research focuses on practical applications, while international studies delve into interdisciplinary research areas. With advancements in intelligent systems and big data technology, research has evolved from basic data collection to sophisticated methodologies, such as deep learning and spatiotemporal analysis, driving substantial progress. This paper concludes by recommending enhanced data integration, improved privacy and security, fostering big data and AI applications, facilitating policy formulation, and exploring innovative transportation modes, thereby underscoring the importance of urban transportation data in shaping the future of smart cities. The findings provide theoretical and practical guidance for the future intelligence, efficiency, and sustainability of urban transportation systems.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.245 | 0.306 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".