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Cycling near misses: a review of the current methods, challenges and the potential of an AI-embedded system
Transport Reviews ( IF 10.185 ) Pub Date : 2020-11-04 , DOI: 10.1080/01441647.2020.1840456
Mohamed R. Ibrahim 1 , James Haworth 1 , Nicola Christie 2 , Tao Cheng 1 , Stephen Hailes 3
Affiliation  

ABSTRACT

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.



中文翻译:

差点骑自行车:回顾当前的方法,挑战以及人工智能嵌入式系统的潜力

摘要

无论是上下班还是休闲,骑自行车是许多国家/地区日益增长的交通方式。但是,骑自行车仍然被许多人视为危险活动。由于循环的模式份额趋于降低,因此很少发生与循环相关的严重事件。然而,由于担心骑车时被撞或跌倒,阻碍了它作为一种运输方式的发展,事实证明,仅关注死亡和重伤的伤亡者仅触及冰山一角。与已报告的事件相比,还有更多的事件使骑自行车的人不稳定或需要采取行动以避免撞车;所谓的未遂。由于它们的频率,与未命中有关的数据可以提供有关与骑自行车相关的危险因素的更多信息。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,以及处理;从手动到自动。在我们如何最好地识别和预测差错并得出具有统计意义的结论方面,我们的理解仍然存在差距,这可能会导致采取更好的干预措施并为骑车人创造更安全的环境。在本文中,我们回顾了有关未命中自行车的文献,重点是采用的数据收集方法,范围和确定的危险因素。通过这样做,我们证明,尽管许多差错是由可能与运输相关或可能与运输无关的不同因素组合造成的,但目前解决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关系。 。

更新日期:2020-11-04
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