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Methods: A cross-sectional study was used to interview 1,422 farm vehicle drivers in southern China. Farm vehicle–related road traffic crashes that occurred from December 2013 to November 2014 were investigated. Data on farm vehicle–related road traffic crashes and related factors were collected by face-to-face interviews.
Results: The prevalence of farm vehicle–related road traffic crashes among the investigated drivers was 7.2%. Farm vehicle–related road traffic crashes were significantly associated with self-reported vision problem (adjusted odds ratio [AOR] = 6.48, 95% confidence interval [CI], 3.86–10.87), self-reported sleep disorders (AOR = 10.03, 95% CI, 6.28–15.99), self-reported stress (AOR = 20.47, 95% CI, 9.96–42.08), reported history of crashes (AOR = 5.40, 95% CI, 3.47–8.42), reported history of drunk driving (AOR = 5.07, 95% CI, 2.97–8.65), and reported history of fatigued driving (AOR = 5.72, 95% CI, 3.73–8.78). The number of road traffic crashes was highest in the daytime and during harvest season. In over 96% of farm vehicle–related road traffic crashes, drivers were believed to be responsible for the crash. Major crash-causing factors included improper driving, careless driving, violating of traffic signals or signs, and being in the wrong lane.
Conclusion: Findings of this study suggest that farm vehicle–related road traffic crashes have become a burgeoning public health problem in China. Programs need to be developed to prevent farm vehicle–related road traffic crashes in this emerging country. 相似文献
Methods: This article applies an ordered probit model, association rules, and classification and regression tree (CART) algorithms to the U.S. Federal Railroad Administration's (FRA) HRGC accident database for the period 2007–2013 to identify VRU injury severity factors at HRGCs.
Results: The results show that train speed is a key factor influencing injury severity. Further analysis illustrated that the presence of illumination does not reduce the severity of accidents for high-speed trains. In addition, there is a greater propensity toward fatal accidents for elderly road users compared to younger individuals. Interestingly, at night, injury accidents involving female road users are more severe compared to those involving males.
Conclusions: The ordered probit model was the primary technique, and CART and association rules act as the supporter and identifier of interactions between variables. All 3 algorithms' results consistently show that the most influential accident factors are train speed, VRU age, and gender. The findings of this research could be applied for identifying high-risk hotspots and developing cost-effective countermeasures targeting VRUs at HRGCs. 相似文献
Methods: The overview of current knowledge on disabilities following a road crash is based on a literature review. The health burden of serious road injuries is quantified in terms of years lived with disability (YLD), by combining incidence data from the Dutch hospital discharge register with information about temporary and lifelong disability.
Results: Literature shows that road traffic injuries can have a major impact on victims' physical and psychological well-being and functioning. Reported proportions of people with disability vary between 11 and 80% depending on the type of casualties, time elapsed since the crash, and the health impacts considered. Together, all casualties involving serious injuries in The Netherlands in 2009 account for about 38,000 YLD, compared to 25,000 years of life lost (YLL) of fatalities. Ninety percent of the burden of injury is due to lifelong consequences that are experienced by 20% of all those seriously injured in road accidents. Lower leg injuries and head injuries represent a high share in the total burden of injury as have cyclists that are injured in a crash without a motorized vehicle. Pedestrians and powered 2-wheeler users show the highest burden of injury per casualty.
Conclusion: Given their major impacts and contribution to health burden, road policy making should also be aimed at reducing the number of serious road injuries and limiting the resulting health impacts. 相似文献
Methods: 49 contributing factors of the SRTCs and PSRTCs that occurred from 2007 to 2013 were collected from the database “In-depth investigation and analysis system for major road traffic crashes” (IIASMRTC) and were analyzed through the integrated use of principal component analysis and hierarchical clustering to determine the primary and secondary groups of contributing factors.
Results: Speeding and overloading of passengers were the primary contributing factors, featuring in up to 66.3 and 32.6% of accidents, respectively. Two secondary contributing factors were road related: lack of or nonstandard roadside safety infrastructure and slippery roads due to rain, snow, or ice.
Conclusions: The current approach to SRTCs and PSRTCs is focused on the attribution of responsibility and the enforcement of regulations considered relevant to particular SRTCs and PSRTCs. It would be more effective to investigate contributing factors and characteristics of SRTCs and PSRTCs as a whole to provide adequate information for safety interventions in regions where SRTCs and PSRTCs are more common. In addition to mandating a driver training program and publicization of the hazards associated with traffic violations, implementation of speed cameras, speed signs, markings, and vehicle-mounted Global Positioning Systems (GPS) are suggested to reduce speeding of passenger vehicles, while increasing regular checks by traffic police and passenger station staff and improving transportation management to increase income of contractors and drivers are feasible measures to prevent overloading of people. Other promising measures include regular inspection of roadside safety infrastructure and improving skid resistance on dangerous road sections in mountainous areas. 相似文献