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http://dx.doi.org/10.15683/kosdi.2021.9.30.427

A Study of Safety Accident Prediction Model (Focusing on Military Traffic Accident Cases)  

Ki, Jae-Sug (Department of Sports ICT Convergence, Sangmyung University)
Hong, Myeong-Gi (Department of Sports ICT Convergence, Sangmyung University)
Publication Information
Journal of the Society of Disaster Information / v.17, no.3, 2021 , pp. 427-441 More about this Journal
Abstract
Purpose: This study proposes a method for developing a model that predicts the probability of traffic accidents in advance to prevent the most frequent traffic accidents in the military. Method: For this purpose, CRISP-DM (Cross Industry Standard Process for Data Mining) was applied in this study. The CRISP-DM process consists of 6 stages, and each stage is not unidirectional like the Waterfall Model, but improves the level of completeness through feedback between stages. Results: As a result of modeling the same data set as the previously constructed accident investigation data for the entire group, when the classification criterion was 0.5, Significant results were derived from the accuracy, specificity, sensitivity, and AUC of the model for predicting traffic accidents. Conclusion: In the process of designing the prediction model, it was confirmed that it was difficult to obtain a meaningful prediction value due to the lack of data. The methodology for designing a predictive model using the data set was proposed by reorganizing and expanding a data set capable of rational inference to solve the data shortage.
Keywords
Accident Cause Information; Predictive Model; Exploratory Factor Analysis; Machine Learning; Traffic Accident;
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