Statistical Relationship Between Objective and Subjective Road Safety Using Social Media Data: A Bayesian Multivariate Modeling Approach
Bibliographic record
Abstract
The current literature rarely addresses the statistical relationship between collision occurrence (objective safety) and reports of perceived unsafety at road sites (subjective safety). This study aims to analyze this relationship using social media data collected and classified from Twitter (now X), applying a Bayesian seemingly unrelated regression (SUR) framework to jointly model both safety dimensions. By analyzing collision data and road-safety-related tweets (RSTs) from Vancouver between 2017 and 2019, macro-level prediction models were developed within the SUR framework and relative risks (RRs) were derived from model parameters to identify the key risk factors. When the RRs for objective safety aligned with those for subjective safety, it indicated that subjective safety could serve as a reliable predictor of objective safety. Five collision types and 9 distinct categories of classified RSTs were employed to develop 45 Bayesian SUR models, where four groups of explanatory variables were used to test different scenarios. While there was a general alignment between subjective and objective safety overall, notable discrepancies were also found when different categories were analyzed. Near-miss observations and safety reports related to active modes were particularly aligned to collision occurrence, especially with property-damage-only collisions. Conversely, design and maintenance-related issues showed minimal alignment with collisions. Overall, the study provided a valuable resource for future risk reduction studies by showing how specific subjective safety observations can or cannot reflect real-world collision risk. This approach suggests the potential for integrating subjective reports into comprehensive safety assessments, offering key insights for policymakers to develop safety strategies more proactively.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".