Tensor-based analysis of eclectic-reported crowd accidents: An innovative retrospective methodology design study.

INTRODUCTION

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

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.