Applying probabilistic mathematical modeling approach and AI technique to investigate serious train accidents in Japan

Tatsuo Oyama , Masashi Miwa
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引用次数: 3

Abstract

We investigated data for serious train accidents (STAs) in Japan to elucidate their causes and consequences and to improve countermeasures for reducing the number of STAs. We used statistical data on the STAs occurring in Japan from 1987 to 2018, which included the frequency, types, causes, and consequences of the STAs, along with additional derailment, collision, and casualty data. We investigated the historical trend of the STAs using various probabilistic mathematical modeling approaches, such as Markov models, logit regression models, Bayesian approaches, and artificial-intelligence techniques. We showed that the number of casualties in STAs involving collisions was significantly larger than that for accidents not involving collisions. Thus, the statistical analysis indicated that preventing train collisions is the most important and necessary measure for reducing damage to passengers. Additionally, we proposed several countermeasures for ensuring the safety of passengers in Japan, e.g., install automatic train stops for all railway companies of Private Railway and terminate the use of ground-level crossings without gates. We evaluated the effectiveness of these countermeasures from various viewpoints.

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应用概率数学建模方法和人工智能技术调查日本严重列车事故
我们调查了日本严重列车事故(STA)的数据,以阐明其原因和后果,并改进减少STA数量的对策。我们使用了1987年至2018年日本发生的STA的统计数据,包括STA的频率、类型、原因和后果,以及其他脱轨、碰撞和伤亡数据。我们使用各种概率数学建模方法,如马尔可夫模型、logit回归模型、贝叶斯方法和人工智能技术,研究了STA的历史趋势。我们发现,涉及碰撞的STA的伤亡人数明显大于不涉及碰撞的事故。因此,统计分析表明,防止列车碰撞是减少对乘客伤害的最重要和必要的措施。此外,我们还提出了一些确保日本乘客安全的对策,例如,为私人铁路的所有铁路公司安装自动列车停靠站,并终止使用无闸门的地面平交道口。我们从不同的角度评估了这些对策的有效性。
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