Statistical Relationship Between Objective and Subjective Road Safety Using Social Media Data: A Bayesian Multivariate Modeling Approach

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.