Edelmann Dóra, Őszi Arnold, Goda Tibor
Doctoral School on Safety and Security Sciences, Óbuda University, 1034, Budapest, Hungary.
Institute for Security Sciences and Cyber Defense, Banki Donat Faculty of Mechanical and Safety Engineering, Óbuda University, 1034, Budapest, Hungary.
Heliyon. 2024 Nov 19;10(23):e40330. doi: 10.1016/j.heliyon.2024.e40330. eCollection 2024 Dec 15.
Analysis of crowd accidents contributes to accident prevention. Therefore, we employ a tensor-based approach. The innovative tensor-based approach facilitates the streamlining of longitudinal studies, promotes error detection, and enhances the transparency and traceability of data collection. This study focuses on crowd accidents, the direct cause of which is the movement of the crowd (Excluding other external factors: e.g. fire, structural damage.). It aims at investigating the reliability of the records documented in relation to crowd accidents and the type of repetitions that can be found in the events.
The study employed a web-based retrospective methodology with innovative tensor-based analysis, examining 186 fatal crowd accidents from 1979 to 2023. Data was collected from public sources, including news reports, government reports, and scientific publications. The analysis considered the following variables: event type, place, date, number of victims, cause, environmental characteristics, date and reliability of documented information source origination. Tensor-based method combines the improvement of the quality of the coverage and investigates changes in content over time. The seven-step method, which stores information about crowd accidents in matrices, is presented here in detail. The v factor is introduced to evaluate the credibility of sources.
The results show that those news items about crowd accidents are the most reliable which were created 2 years after the events. Crowd accidents are analyzed based on their influencing defining characteristics. We claim that we were able to isolate new risk factors related to the locations of crowd accidents. Globally, we focus on accidents that occurred during donation distributions and when entering buildings.
It can be concluded that the new, seven-step, tensor-based data collection method improves the credibility value of individual information by more than 25 %. The impact of accident factors plays a key role in establishing risk factors and in the prevention of accidents. The tensor-based approach can be directly applied to record databases, enhance data provenance, and capture the temporal evolution of information.
对人群事故进行分析有助于预防事故。因此，我们采用基于张量的方法。这种创新的基于张量的方法有助于简化纵向研究、促进错误检测，并提高数据收集的透明度和可追溯性。本研究聚焦于人群事故，其直接原因是人群的移动（不包括其他外部因素：如火灾、结构损坏）。其目的是调查与人群事故相关记录的可靠性以及事件中可发现的重复类型。
本研究采用基于网络的回顾性方法，并结合创新的基于张量的分析，研究了1979年至2023年期间的186起致命人群事故。数据从包括新闻报道、政府报告和科学出版物在内的公共来源收集。分析考虑了以下变量：事件类型、地点、日期、受害者数量、原因、环境特征、记录信息源的日期和可靠性。基于张量的方法结合了覆盖质量的改进，并研究内容随时间的变化。这里详细介绍了将人群事故信息存储在矩阵中的七步法。引入v因子来评估来源的可信度。
结果表明，关于人群事故的新闻报道在事件发生两年后创建的最为可靠。根据人群事故的影响定义特征进行分析。我们声称能够分离出与人群事故发生地点相关的新风险因素。在全球范围内，我们关注捐赠分发期间和进入建筑物时发生的事故。
可以得出结论，新的基于张量的七步数据收集方法将个体信息的可信度值提高了25%以上。事故因素的影响在确定风险因素和预防事故中起着关键作用。基于张量的方法可直接应用于记录数据库，增强数据来源，并捕捉信息的时间演变。
