Analytical Methods and Determinants of Frequency and Severity of Road Accidents: A 20-Year Systematic Literature Review
Bibliographic record
Abstract
In this systematic literature review (SLR), we use a series of quantitative bibliometric analyses to (1) identify the main papers, journals, and authors of the publications that make use of statistical analysis (SA) and machine learning (ML) tools as well as technological elements of smart cities (TESC) and Geographic Information Systems to predict road traffic accidents (RTAs); (2) determine the extent to which the identified methods are used for the analysis of RTAs and current trends regarding their use; (3) establish the relationship between the set of variables analyzed and the frequency and severity of RTAs; and (4) identify gaps in method use to highlight potential areas for future research. A total of 3888 papers published between January 2000 and June 2021, distributed in four clusters—RTA + HA + SA (SA, n = 399); RTA + HA + ML (ML, n = 858); RTA + HA + SC (TESC, n = 2327); and RTA + HA + GIS (GIS, n = 304)—were analyzed. We identified Accident Analysis and Prevention as the most important journal, Fred Mannering as the main author, and The Statistical Analysis of Crash-Frequency Data: A Review and Assessment of Methodological Alternatives as the most cited publication. Although the negative binomial regression method was used for several years, we noticed that other regression models as well as methods based on deep learning, convolutional neural networks, transfer learning, 5G technology, Internet of Things, and intelligent transport systems have recently emerged as suitable alternatives for RTA analysis. By introducing a new approach based on computational algorithms and data visualization, this SLR fills a gap in the area of RTA analysis and provides a clear picture of the current scientific production in the field. This information is crucial for projecting further research on RTA analysis and developing computational and data visualization tools oriented to the automation of RTA predictions based on intelligent 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 | 0.045 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.069 | 0.060 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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, 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".