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首页> 外文期刊>Transport Reviews >Cycling near misses: a review of the current methods, challenges and the potential of an AI-embedded system
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Cycling near misses: a review of the current methods, challenges and the potential of an AI-embedded system

机译：骑自行车靠近未命中：审查目前的AI嵌入式系统的方法，挑战和潜力

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摘要

Whether for commuting or leisure, cycling is a growing transport mode in many countries. However, cycling is still perceived by many as a dangerous activity. Because the mode share of cycling tends to be low, serious incidents related to cycling are rare. Nevertheless, the fear of getting hit or falling while cycling hinders its expansion as a transport mode and it has been shown that focusing on killed and seriously injured casualties alone only touches the tip of the iceberg. Compared with reported incidents, there are many more incidents in which the person on the bike was destabilised or needed to take action to avoid a crash; so-called near misses. Because of their frequency, data related to near misses can provide much more information about the risk factors associated with cycling. The quality and coverage of this information depends on the method of data collection; from survey data to video data, and processing; from manual to automated. There remains a gap in our understanding of how best to identify and predict near misses and draw statistically significant conclusions, which may lead to better intervention measures and the creation of a safer environment for people on bikes. In this paper, we review the literature on cycling near misses, focusing on the data collection methods adopted, the scope and the risk factors identified. In doing so, we demonstrate that, while many near misses are a result of a combination of different factors that may or may not be transport-related, the current approach of tackling these factors may not be adequate for understanding the interconnections between all risk factors. To address this limitation, we highlight the potential of extracting data using a unified input (images/videos) relying on computer vision methods to automatically extract the wide spectrum of near miss risk factors, in addition to detecting the types of events associated with near misses.
机译：无论是通勤还是休闲，自行车在许多国家都是一种日益增长的交通方式。然而，许多人仍然认为骑自行车是一项危险的活动。由于自行车的模式份额往往较低，因此与自行车相关的严重事件很少发生。尽管如此，骑车时害怕被撞倒或摔倒阻碍了其作为一种交通方式的扩展，而且已经证明，仅仅关注死亡和严重受伤的人员只触及冰山一角。与报告的事件相比，自行车上的人不稳定或需要采取措施避免撞车的事件要多得多；所谓的未遂事件。由于事故频发，与未遂事故相关的数据可以提供更多关于骑车相关风险因素的信息。这些信息的质量和覆盖范围取决于数据收集的方法；从调查数据到视频数据，并进行处理；从手动到自动。我们对如何最好地识别和预测未遂事件以及得出具有统计意义的结论的理解仍然存在差距，这可能会导致更好的干预措施，并为骑自行车的人创造更安全的环境。在本文中，我们回顾了有关自行车未遂事故的文献，重点介绍了所采用的数据收集方法、范围和识别的风险因素。通过这样做，我们证明，虽然许多未遂事故是不同因素组合的结果，这些因素可能与交通有关，也可能与交通无关，但目前处理这些因素的方法可能不足以理解所有风险因素之间的相互联系。为了解决这一限制，我们强调了使用统一输入（图像/视频）提取数据的潜力，该输入依赖于计算机视觉方法，除了检测与未遂事件相关的事件类型之外，还可以自动提取广泛的未遂风险因素。

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