International Journal of Injury Control and Safety Promotion最新文献

Drunk-driving events often escalate into drunk-driving crashes, however, the contributing factors of this progression remain elusive. To mitigate the likelihood of crashes stemming from drunk-driving events, this paper introduces the notion of 'the severity of drunk-driving event' and examines the complex relationship between the severity and its contributing factors, considering spatiotemporal heterogeneity. The study utilizes a Geographically and Temporally Weighted Binary Logistic Regression (GTWBLR) model to conduct spatiotemporal analysis based on police-reported drunk-driving events in Beijing, China. The results show that most factors passed the non-stationary test, indicating their effects on the severity of drunk-driving event vary significantly across different spatial and temporal domains. Notably, during non-workday, drunk-driving events in northeast of Beijing are more likely to escalate into crashes. Furthermore, severe weather during winter in the northwest of Beijing is associated with high risk of drunk-driving crashes. Based on these insights, the authorities can strengthen drunk-driving checks in the northeast region of Beijing, particularly during non-workdays. And it is crucial to promptly clear accumulated snow on the roads during severe winter weather to improve road safety. These insights and recommendations are highly valuable for reducing the risk of drunk-driving crashes.

Speeding increases the likelihood and severity of road traffic crashes, but many riders do not consider speeding as a serious safety issue. By using belief-based variables derived from the Theory of Planned Behaviour (i.e. behavioural beliefs, normative beliefs, and control beliefs), this study investigated the socio-cognitive determinants of speeding intentions among Vietnamese adolescents operating motorized/electrified two-wheelers. 189 adolescent powered two-wheeled riders in Ho Chi Minh City participated in a cross-sectional survey. The results lend clear support to the Expectancy-Value approach since belief-based product factors (e.g. outcome beliefs x outcome evaluations) significantly and independently contributed to the prediction of speeding intentions. Speeding intentions were mostly influenced by behavioural beliefs, followed by normative beliefs and control beliefs, respectively. This study not only proves the Expectancy-Value approach as an appropriate framework for the investigation of speeding intentions but also supports authorities in the formulation and execution of more effective interventions for reducing speeding among adolescent powered two-wheeled riders in Vietnam. Instead of motivation-oriented methods, there is a need for strategies that stimulate the translation of good intentions into the desirable behaviour, and encourage adolescents not to relapse in case they are exposed to risk facilitating circumstances. Yet, besides focussing on person-specific dispositions towards speeding, policy makers are advised to adopt a more broadly encompassing systemic approach with inclusion of safe roads, safe vehicles, improved post-crash care, and shared stakeholder responsibilities.

Drawing on the core idea of Propensity Score Matching, this study proposes a new concept named Historical Traffic Violation Propensity to describe the driver's historical traffic violations, and combines the new concept with an improved mutual information-based feature selection algorithm to construct a method for screening key traffic violations from the perspective of expressing driver's accident risk. The validation analysis based on the real data collected in Shenzhen demonstrated that drivers' state of Historical Traffic Violation Propensity on 19 key traffic violations screened have a stronger predictive ability of their subsequent accidents compared to the level in existing research. The positive state of Historical Traffic Violation Propensity on 'Drinking', 'Parking in dangerous areas', 'Wrong use of turn lights', 'Violating prohibited and restricted traffic regulations', and 'Disobeying prohibition sign' will increase the probability of a driver's subsequent accident by more than 1.7 times. The research provides directions to more efficiently and accurately capture the driver's accident risk through historical traffic violations, which is valuable for identifying high-risk drivers as well as the key psychological or physical risk factors that manifest in daily driving activities and lead to subsequent accidents.