Traffic injury prevention
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Traffic injury prevention · Jan 2014
Comparative StudyEstimating rear-end accident probabilities at signalized intersections: a comparison study of intersections with and without green signal countdown devices.
Rear-end accidents are the most common accident type at signalized intersections, because the diversity of actions taken increases due to signal change. Green signal countdown devices (GSCDs), which have been widely installed in Asia, are thought to have the potential of improving capacity and reducing accidents, but some negative effects on intersection safety have been observed in practice; for example, an increase in rear-end accidents. ⋯ GSCDs helped shorten indecision zones and reduce rear-end collisions near the stop line during the yellow interval, but they easily resulted in risky car following behavior and much higher rear-end collision probabilities at indecision zones during both flashing green and yellow intervals. GSCDs are recommended to be cautiously installed and education on safe driving behavior should be available.
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Traffic injury prevention · Jan 2014
Development and validation of the Spanish Hazard Perception Test.
The aim of the current study is to develop and obtain valid evidence for a hazard perception test suitable for the Spanish driving population. To obtain valid evidence to support the use of the test, the effect of hazardous and quasi-hazardous situations on the participants' hazard prediction is analyzed and the pattern of results for drivers with different driving experience--that is, learner, novice, and expert drivers and reoffender vs. nonoffender drivers--is compared. Potentially hazardous situations are those that develop without involving any real hazard (i.e., the driver did not actually have to decelerate or make any evasive maneuver to avoid a potential collision). The current study analyzed repeat offender drivers attending compulsory reeducation programs as a result of reaching the maximum number of penalty points on their driving license due to repeated violations of traffic laws. ⋯ The test has adequate psychometric properties and is useful in distinguishing between learner, novice, and expert drivers. In addition, it is useful in that it analyzes the performance of both safe and unsafe drivers (reoffenders who have already lost their driving license).
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Traffic injury prevention · Jan 2014
The benefits and tradeoffs for varied high-severity injury risk thresholds for advanced automatic crash notification systems.
The objectives of this study are to (1) characterize the population of crashes meeting the Centers for Disease Control and Prevention (CDC)-recommended 20% risk of Injury Severity Score (ISS)>15 injury and (2) explore the positive and negative effects of an advanced automatic crash notification (AACN) system whose threshold for high-risk indications is 10% versus 20%. ⋯ This article provides important information comparing the expected positive and negative effects of an AACN system with thresholds at the 10% and 20% levels using 2 outcome metrics. Overall, results suggest that the 20% risk threshold would not provide a useful notification to improve the quality of care for a large number of seriously injured crash victims. Alternately, a lower threshold may increase the over triage rate. Based on the vehicle damage observed for crashes reaching and exceeding the 10% risk threshold, we anticipate that rescue services would have been deployed based on current Public Safety Answering Point (PSAP) practices.
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Traffic injury prevention · Jan 2014
Comparative StudyComparison and validation of injury risk classifiers for advanced automated crash notification systems.
The odds of death for a seriously injured crash victim are drastically reduced if he or she received care at a trauma center. Advanced automated crash notification (AACN) algorithms are postcrash safety systems that use data measured by the vehicles during the crash to predict the likelihood of occupants being seriously injured. The accuracy of these models are crucial to the success of an AACN. The objective of this study was to compare the predictive performance of competing injury risk models and algorithms: logistic regression, random forest, AdaBoost, naïve Bayes, support vector machine, and classification k-nearest neighbors. ⋯ Logistic regression slightly outperformed the machine learning algorithms based on sensitivity and specificity of the models. Previous studies on AACN risk curves used the same data to train and test the power of the models and as a result had higher sensitivity compared to the cross-validated results from this study. Future studies should account for future data; for example, by using cross-validation or risk presenting optimistic predictions of field performance. Past algorithms have been criticized for relying on age and sex, being difficult to measure by vehicle sensors, and inaccuracies in classifying damage side. The models with accurate damage side and including age/sex did outperform models with less accurate damage side and without age/sex, but the differences were small, suggesting that the success of AACN is not reliant on these predictors.
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Traffic injury prevention · Jan 2014
Analysis on tank truck accidents involved in road hazardous materials transportation in china.
Due to the sheer size and capacity of the tanker and the properties of cargo transported in the tank, hazmat tanker accidents are more disastrous than other types of vehicle accidents. The aim of this study was to provide a current survey on the situation of accidents involving tankers transporting hazardous materials in China. ⋯ The safety situation of China's hazmat tanker transportation is grim. Such accidents not only have high spill percentages and consistently large spills but they can also cause serious consequences, such as fires and explosions. Improving the training of drivers and the quality of vehicles, deploying roll stability aids, enhancing vehicle inspection and maintenance, and developing good delivery schedules may all be considered effective measures for mitigating hazmat tanker accidents, especially severe crashes.