Accident; analysis and prevention
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Advanced driver assistance systems like (forward) collision warnings can increase traffic safety. As safety-critical situations (especially in urban traffic) can be diverse, integrated adaptive systems (such as multi-stage warnings) need to be developed and examined in a variety of use cases over time instead of the more common approach of testing only one-time effectiveness in the most relevant use case. Thus, this driving simulator experiment investigated a multi-stage collision warning in partially repetitive trials (T) of various safety-critical situations (scenarios confronting drivers with hazards in form of pedestrians, obstacles or preceding vehicles). ⋯ Moreover, the drivers applied the gained knowledge from the learning phase to various new situations (transfer: faster brake reactions in T4 compared to T1 or T2). The well accepted and positively rated (helpful and understandable) two-stage collision warning can thus be recommended as it facilitates accident mitigation by earlier decelerations. Practice with advanced driver assistance systems (even in driving simulators) should be endorsed to maximize their benefits for traffic safety and accident prevention.
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In Switzerland, the usage and accident numbers of e-bikes have strongly increased in recent years. According to official statistics, single-vehicle accidents constitute an important crash type. Up to date, very little is known about the mechanisms and causes of these crashes. ⋯ Women, elderly people, riders of e-bikes with a pedal support up to 45 km/h and e-cyclists who considered themselves to be less fit in comparison to people of the same age had an increased risk of injury. This study confirms the high relevance of single-vehicle crashes with e-bikes. Measures to prevent this type of accident could include the sensitisation of e-cyclists regarding the most common accident mechanisms and causes, a regular maintenance of bicycle pathways, improvements regarding tram and railway tracks and technological advancements of e-bikes.
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Meta Analysis
Bicycle helmets - To wear or not to wear? A meta-analyses of the effects of bicycle helmets on injuries.
A meta-analysis has been conducted of the effects of bicycle helmets on serious head injury and other injuries among crash involved cyclists. 179 effect estimates from 55 studies from 1989-2017 are included in the meta-analysis. The use of bicycle helmets was found to reduce head injury by 48%, serious head injury by 60%, traumatic brain injury by 53%, face injury by 23%, and the total number of killed or seriously injured cyclists by 34%. Bicycle helmets were not found to have any statistically significant effect on cervical spine injury. ⋯ Bicycle helmet effects may be somewhat larger when bicycle helmet wearing is mandatory than otherwise; however, helmet wearing rates were not found to be related to bicycle helmet effectiveness. It is also likely that bicycle helmets have larger effects among drunk cyclists than among sober cyclists, and larger effects in single bicycle crashes than in collisions with motor vehicles. In summary, the results suggest that wearing a helmet while cycling is highly recommendable, especially in situations with an increased risk of single bicycle crashes, such as on slippery or icy roads.
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Technological advances allow supporting drivers in a multitude of occasions, ranging from comfort enhancement to collision avoidance, for example through driver warnings, which are especially crucial for traffic safety. This psychological driving simulator experiment investigated how to warn drivers visually in order to prevent accidents in various safety-critical situations. Collision frequencies, driving behavior and subjective evaluations of situation criticality, warning understandability and helpfulness of sixty drivers were measured in two trials of eight scenarios each (within-subjects factors). ⋯ A stop symbol as reaction generic warning is recommendable for diverse kinds of use cases, leading to fast and strong reactions. However, for rather moderate driver reactions an attention generic approach with a caution symbol might be more suitable. Further research should investigate multi-stage warnings with adaptive strategies for application to various situations including other modalities and false alarms.
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One of the proactive approaches in reducing traffic crashes is to identify hazardous traffic conditions that may lead to a traffic crash, known as real-time crash prediction. Threshold selection is one of the essential steps of real-time crash prediction. And it provides the cut-off point for the posterior probability which is used to separate potential crash warnings against normal traffic conditions, after the outcome of the probability of a crash occurring given a specific traffic condition on the basis of crash risk evaluation models. ⋯ And K-fold cross-validation was used to validate the performance of proposed threshold selection methods with the help of several evaluation criteria. The results indicate that (i) the mixed logit model can obtain a good performance; (ii) the classification performance of the threshold selected by the minimum cross-entropy method outperforms the other methods according to the criteria. This method can be well-behaved to automatically identify thresholds in crash prediction, by minimizing the cross entropy between the original dataset with continuous probability of a crash occurring and the binarized dataset after using the thresholds to separate potential crash warnings against normal traffic conditions.