Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.
Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).
Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.
Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy. 相似文献
Problem: The rollover crash is a serious crash type that often causes higher injury severities. Moreover, factors that contribute to the injury severities of rollover crashes may show instabilities in different vehicle types and time periods, which requires further investigations. This study utilizes the rollover crash data in North Carolina from Highway Safety Information System (HSIS) to study the effect instabilities of factors in vehicle type and time periods in rollover crashes. Methods: The injury severities of drivers are estimated using the random parameters logit (RPL) model with heterogeneity in means and variances. Available factors in HSIS have been categorized into three groups, which are drivers, road, and environment, respectively. This study also justifies the segmentations through transferability tests. The effects of identified significant factors are evaluated using marginal effects. Results: Factors such as FWP (farm, wood, and pasture areas), unhealthy physical condition, impaired physical condition, road adverse, and so forth have shown instabilities in marginal effects among vehicle types and time periods. Practical Applications: The finding of this research could provide important references for policy makers and automobile manufactures to help mitigate the injury severity of rollover crashes. 相似文献
Accidents in the process industry involve several interacting factors, including human and organizational factors (HOFs). A long-standing obstacle to HOFs analysis is lack of data. Accident reports are an essential data source to learn from the past and contain HOFs-related data, but they are usually unstructured text in a not standardized format. Some studies have explored the extraction of information automatically from accident reports based on Natural Language Processing (NLP) techniques. However, they were not dedicated to HOFs. Risk communication is considered an essential pillar in safety and risk science. This research develops a HOFs-focused risk communication framework based on the NLP techniques that can support risk assessment and mitigation. The proposed approach automatically extracts the target groups oriented “Who, When, Where, Why” (4Ws) information from accident reports.This framework was applied to explore the eMARS database. The results show that the “4Ws” skeleton of narratives has appreciated performance in pattern recognition and holistic information analysis. The graphical representation interfaces are designed to display the features of HOFs-related accidents, which can better be communicated to the sharp-end operators and decision-makers. 相似文献