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首页> 外文期刊>Journal of Turbulence >Are automatic systems the future of motorcycle safety? A novel methodology to prioritize potential safety solutions based on their projected effectiveness
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Are automatic systems the future of motorcycle safety? A novel methodology to prioritize potential safety solutions based on their projected effectiveness

机译：自动系统是摩托车安全的未来吗？ 一种基于预计效果的潜在安全解决方案优先考虑的新方法

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Objective: Motorcycle riders are involved in significantly more crashes per kilometer driven than passenger car drivers. Nonetheless, the development and implementation of motorcycle safety systems lags far behind that of passenger cars. This research addresses the identification of the most effective motorcycle safety solutions in the context of different countries.Methods: A knowledge-based system of motorcycle safety (KBMS) was developed to assess the potential for various safety solutions to mitigate or avoid motorcycle crashes. First, a set of 26 common crash scenarios was identified from the analysis of multiple crash databases. Second, the relative effectiveness of 10 safety solutions was assessed for the 26 crash scenarios by a panel of experts. Third, relevant information about crashes was used to weigh the importance of each crash scenario in the region studied. The KBMS method was applied with an Italian database, with a total of more than 1million motorcycle crashes in the period 2000-2012.Results: When applied to the Italian context, the KBMS suggested that automatic systems designed to compensate for riders' or drivers' errors of commission or omission are the potentially most effective safety solution. The KBMS method showed an effective way to compare the potential of various safety solutions, through a scored list with the expected effectiveness of each safety solution for the region to which the crash data belong. A comparison of our results with a previous study that attempted a systematic prioritization of safety systems for motorcycles (PISa project) showed an encouraging agreement.Conclusions: Current results revealed that automatic systems have the greatest potential to improve motorcycle safety. Accumulating and encoding expertise in crash analysis from a range of disciplines into a scalable and reusable analytical tool, as proposed with the use of KBMS, has the potential to guide research and development of effective safety systems. As the expert assessment of the crash scenarios is decoupled from the regional crash database, the expert assessment may be reutilized, thereby allowing rapid reanalysis when new crash data become available. In addition, the KBMS methodology has potential application to injury forecasting, driver/rider training strategies, and redesign of existing road infrastructure.
机译：目的：摩托车车手比乘用车司机每公里的撞车队员显着更大。尽管如此，摩托车安全系统的开发和实施远远落后于乘用车的落后。本研究解决了不同国家背景下最有效的摩托车安全解决方案的识别。方法：开发了一种基于知识的摩托车安全系统（KBMS），以评估各种安全解决方案的潜力，以减轻或避免摩托车撞击。首先，从多次崩溃数据库的分析中确定了一组26个常见的崩溃方案。其次，通过专家小组评估了10个安全解决方案的相对有效性。第三，有关崩溃的相关信息被用来权衡所研究地区的每个碰撞情况的重要性。 KBMS方法应用于意大利数据库，共有2000-2012期间总共超过100万辆摩托车崩溃。结果：当应用于意大利背景时，KBMS建议旨在弥补骑手的自动系统佣金或遗漏的错误是可能最有效的安全解决方案。 KBMS方法显示了通过评分列表比较各种安全解决方案的潜力的有效方法，该列表具有碰撞数据所属的区域的每个安全解决方案的预期效力。我们的结果与先前研究的结果进行了比较，该研究试图为摩托车（PISA项目）的安全系统的系统优先级进行了令人鼓舞的协议。结论：目前的结果表明，自动系统具有最大的提高摩托车安全潜力。累积和编码碰撞分析的专业知识从一系列学科分析到可扩展和可重复使用的分析工具，如使用KBMS所提出的，有可能导向有效安全系统的研究和开发。由于对崩溃方案的专家评估从区域崩溃数据库中解耦，专家评估可以重新利用，从而在新的崩溃数据可用时允许快速重新分析。此外，KBMS方法具有潜在的损害预测，驾驶员/骑手培训策略和现有道路基础设施重新设计的应用。

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