The Expanding Landscape of Road Rage: A Systematic Review of Conflicts Involving Drivers, Pedestrians, and Micromobility
Bibliographic record
Abstract
Road rage, characterized by aggressive behaviours among road users, has become a significant public health concern in urban transportation. This review, which shifts from a driver-centric to a multi-modal, systemic perspective, synthesized 135 empirical studies to examine road rage across multiple user groups, while also analyzing trends, contributing factors, and mitigation strategies. Studies were sourced from Scopus and included diverse road user types and methodologies, with experimental studies being the most common (39.3%), followed by observational (24.4%) and mixed-methods approaches (20.7%). The research was mainly focused on China (32 studies) and the United States (16 studies), with 116 studies (85.9%) emphasizing driver behaviour. However, pedestrian (65 studies, 48.1%) and cyclist interactions (23 studies, 17.0%) are increasingly acknowledged. Environmental factors were the most frequently examined contributors (122 studies), with substantial statistical significance noted: 59.3% of effect sizes were large (≥ 0.5). Infrastructure modifications (64 studies) and education programs (46 studies) appeared as key intervention strategies, with 30 studies reporting successful outcomes. This review highlights the need for interdisciplinary approaches that include all road users and stresses the importance of standardized reporting, cultural factors, and rigorous evaluations to improve transportation safety.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; a candidate call from one teacher head, 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".