Objectives: To identify the changing trends and crucial preventive approaches to road traffic accidents (RTAs) adopted in the Kingdom of Saudi Arabia (KSA) over the last 2.5 decades, and to analyze aspects previously overlooked. Methods: This systematic review was based on evidence of RTAs in KSA. All articles published during the last 25 years on road traffic accident in KSA were analyzed. This study was carried out from December 2013 to May 2014 in the Department of Family and Community Medicine, Taibah University, Al-Madinah Al-Munawwarah, KSA. Results: Road traffic accidents accounted for 83.4% of all trauma admissions in 1984-1989, and no such overall trend was studied thereafter. The most frequently injured body regions as reported in the latest studies were head and neck, followed by upper and lower extremities, which was found to be opposite to that of the studies reported earlier. Hospital data showed an 8% non-significant increase in road accident mortalities in contrast to police records of a 27% significant reduction during the years 2005-2010. Excessive speeding was the most common cause reported in all recent and past studies. Conclusion: Disparity was common in the type of reporting of RTAs, outcome measures, and possible causes over a period of 2.5 decade. All research exclusively looked into the drivers' faults. A sentinel surveillance of road crashes should be kept in place in the secondary and tertiary care hospitals for all regions of KSA.
In 2003, China enacted the Road Traffic Safety Law in an attempt to promote traffic safety. We employ a difference-in-differences strategy on province level data, where fire accidents are used as a control group for road accidents, to estimate the effects of the law on road accidents and casualties. Our findings suggest that while the law was successful in decreasing the number of accidents and casualties, the ratio of deaths to accidents and injuries to accidents increased. Exploring the potential channels, we find no evidence that “hit-and-kill” incentives, that is, incentives for motorists to kill the pedestrians that they hit due to China's peculiar personal injury compensation rules, drive the increase in death to accident ratio. We show that an increase in the severity of accidents could, in fact, be consistent with a model where all motorists drive more carefully after the reform, but have heterogeneous responses such that the decrease in accident probability is larger for safer than for riskier drivers. •The RTSL decreased the number of road accidents and casualties.•The reform increased the ratio of deaths to accidents.•There is no evidence of “hit-and-kill” incentives.•The results point to heterogeneous responses to the reform.
Since 1970, traumatomechanics has been a focal point in research at the Institute for Legal Medicine and Traffic Medicine in Heidelberg. Here, the main topics are the understanding of the interrelation between mechanical strain and the resulting degree of injury; at the forefront of all interest is the determination of the mechanical resilience in humans, their organs and tissues. Important are not only the means, but likewise the individual strain tolerance and the causes for its variability. Their understanding leads to scientifically justifiable expert’s reports. In safety research, these data are of major importance for the validation of crash-dummies and for the improvement of safety protection systems. Before this background, national and international institutions have supported numerous projects. With the help of 2 examples, the Thorax-Trauma-Index (TTI) and the synergy between safety belt and airbag, the relevance of these data for international regulatory provisions and the progress in safety practice are illustrated. Some traumatomechanical insights can only be gathered from human corpses. Legal prerequisites and ethical problems of experiments with corpses are discussed.
This study examined if speed reduction effects from animal-vehicle collision (AVC) countermeasures are merely local or do extend to a wider area, and what implications the results have on road planning practice regarding AVCs.Twenty-five drivers drove repeatedly on a 9-km long road stretch in a high-fidelity driving simulator. The development of vehicle speed in the surrounding of an automatic speed camera, a wildlife warning sign and a radio message, were investigated in a full factorial within-subject experiment. The factors wildlife fence (with/without) and forest (dense/open landscape) were also included.The radio warning message had the largest influence on vehicle speed with a speed reduction of 8 km/h that lasted beyond 1 km and 2 km after the implementation. Eighty-eight per cent of the drivers reported being made extra aware of AVC due to the radio message, which was also associated with stress, insecurity and unsafety. The warning sign reduced vehicle speed by 1.5 km/h, but speed reductions were not significantly reduced 1 km after the implementation. Only 8 % of the drivers felt insecure/unsafe after passing the wildlife warning sign, explaining its limited impact on speed. There were no main effects of the automatic speed camera on vehicle speed at longer distances after implementation.We recommend that AVC countermeasures should be of various design, occur at various segments along the road, and preferably be adaptive and geo-localized to minimize habituation effects on drivers.
With the rapid development of economy and urbanization, the number of urban motor vehicles keeps increasing. Urban travel is more convenient, but the traffic safety problems are increasingly prominent. Traffic accident data include not only time and place, but also people, roads, vehicles and the surrounding environment. Traffic accident black spot is the spatial location of traffic accident concentrated distribution. Most of the traditional traffic accident black spot identification only considers time and space factors, ignoring other factors. Based on the traffic accident data of Suzhou Industrial Park, this paper makes a fusion analysis of the multi-source influencing factors involved in traffic accident black spot. According to the structured association characteristics of urban traffic accident big data, a support vector machine method based on maximizing the classification interval is used to train the complex model and optimal learning of accident black spots in the study area. The accuracy of black spot identification is improved. At the same time, aiming at the rapid growth of traffic accident multi source data, a black point identification algorithm based on deep neural network is proposed. The deep neural network of relevant data category information is established to verify the model's ability to identify accident black spots. A feature-based black spot identification method based on depth neural network is proposed. Furthermore, a dynamic adaptive machine learning architecture is built.
There are likely to be individual differences in bus driver behaviour when adhering to strict schedules under time pressure. A reliable and valid assessment of these individual differences would be useful for bus companies keen to mitigate risk of crash involvement. This paper reports on three studies to develop and validate a self-report measure of bus driver behaviour. For study 1, two principal components analyses of a pilot questionnaire revealed six components describing bus driver behaviour and four bus driver coping components. In study 2, test-retest reliability of the components were tested in a sub-sample and found to be adequate. Further, the 10 components were used to predict bus crash involvement at three levels of culpability with consistently significant associations found for two components. For study 3, avoidance coping was consistently associated with celeration variables in a bus simulator, especially for a time-pressured drive. Statement of Relevance:The instrument can be used by bus companies for driver stress and fatigue management training to identify at-risk bus driver behaviour. Training to reduce the tendency to engage in avoidance coping strategies, improve evaluative coping strategies and hazard monitoring when under stress may improve bus driver safety.