R-Mapを用いた危害シナリオ評価支援システムの作成

抄録

In recent years, with the increasing number of inbound tourists to Japan, the development of a multicultural society has been progressing. In this context, it has become a significant challenge in product design to consider a diverse range of users who have different cultural backgrounds and lifestyles. Therefore, this study aimed to establish a product design support methodology that considers diverse users by predicting accidents using a machine learning model and visualizing the results with R-Map. Initially, in constructing the machine learning model, features such as age, race, gender, behavior, and product information were extracted from the NEISS 2023 dataset to predict the types of injuries, the injured body parts, the frequency of accidents, and the severity of hazards. The model was trained using a random forest algorithm, performing multi-class classification. Subsequently, the predicted results were mapped onto the R-Map to visualize the frequency of accidents, the severity of hazards, and the number of incidents. Although the improvement of the model's accuracy was identified as a future challenge, the study demonstrated that this approach could be useful for comparing accident scenarios among different personas. By adopting this methodology, it becomes possible to consider the diversity of users in product design, thereby contributing to safer and more inclusive products.