Gil Gustavo, Savino Giovanni, Piantini Simone, Baldanzini Niccolò, Happee Riender, Pierini Marco
a Dipartimento di Ingegneria Industriale , Università degli Studi di Firenze , Firenze , Italy.
b Monash University Accident Research Centre, Monash University , Clayton , Victoria , Australia.
Traffic Inj Prev. 2017 Nov 17;18(8):877-885. doi: 10.1080/15389588.2017.1326594. Epub 2017 May 11.
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.
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 1 million motorcycle crashes in the period 2000-2012.
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.
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.
每行驶一公里，摩托车骑手遭遇的撞车事故比乘用车司机多得多。尽管如此，摩托车安全系统的开发和应用仍远远落后于乘用车。本研究旨在确定不同国家背景下最有效的摩托车安全解决方案。
开发了一个基于知识的摩托车安全系统（KBMS），以评估各种安全解决方案减轻或避免摩托车撞车事故的潜力。首先，通过对多个撞车事故数据库的分析，确定了一组26种常见的撞车场景。其次，由专家小组评估了10种安全解决方案在26种撞车场景中的相对有效性。第三，利用有关撞车事故的相关信息来权衡每个撞车场景在研究区域中的重要性。KBMS方法应用于一个意大利数据库，该数据库涵盖了2000年至2012年期间总计超过100万起摩托车撞车事故。
当应用于意大利的情况时，KBMS表明，旨在补偿骑手或司机的作为或不作为错误的自动系统可能是最有效的安全解决方案。KBMS方法展示了一种有效的方式来比较各种安全解决方案的潜力，通过一个得分列表，列出每种安全解决方案对撞车事故数据所属区域的预期有效性。将我们的结果与之前一项试图对摩托车安全系统进行系统排序的研究（PISa项目）进行比较，结果显示出令人鼓舞的一致性。
当前结果表明，自动系统在提高摩托车安全性方面具有最大潜力。如使用KBMS所提议的，将来自一系列学科的碰撞分析专业知识积累并编码到一个可扩展且可重复使用的分析工具中，有可能指导有效安全系统的研发。由于对撞车场景的专家评估与区域撞车数据库解耦，专家评估可能会被重新利用，从而在有新的撞车数据可用时能够快速重新分析。此外，KBMS方法在伤害预测、驾驶员/骑手培训策略以及现有道路基础设施的重新设计方面具有潜在应用价值。
