교통사고 심각 정도 예측을 위한 TATI 모델 제안
Proposed TATI Model for Predicting the Traffic Accident Severity
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초록
TATI 모델이란 Traffic Accident Text to RGB Image 모델로, 교통사고 심각 정도 예측을 위한 본 논문에서 제안하는 방법론이다. 교통사고치사율은 매년 감소하는 추세이나 OECD 회원국 중 하위권에 속해있다. 교통사고 치사율 감소를 위해 많은 연구들이 진행되었고, 그 중에서 교통사고심각 정도를 예측하여 발생 및 치사율을 줄이기 위한 연구가 꾸준하게 진행되고 있다. 이와 관련하여 최근에는 통계 모델과 딥러닝 모델을 활용하여교통사고 심각 정도 예측을 하는 연구가 활발하다. 본 논문에서는 교통사고 심각 정도를 예측하기 위해서 교통사고 데이터를 컬러 이미지로 변환하고,CNN 모델을 통해 이를 수행한다. 성능 비교를 위해 제안하는 모델과 다른 모델들을 같은 데이터로 학습시키고, 예측결과를 비교하는 실험을 진행했다. 10번의 실험을 통해 4개의 딥러닝 모델의 정확도와 오차 범위를 비교하였다. 실험 결과에 따르면 제안하는 TATI 모델의 정확도가 0.85로가장 높은 정확도를 보였고, 0.03으로 두 번째로 낮은 오차 범위를 보여 성능의 우수성을 확인하였다.
Abstract
The TATI model is a Traffic Accident Text to RGB Image model, which is a methodology proposed in this paper for predicting theseverity of traffic accidents. Traffic fatalities are decreasing every year, but they are among the low in the OECD members. Many studieshave been conducted to reduce the death rate of traffic accidents, and among them, studies have been steadily conducted to reducethe incidence and mortality rate by predicting the severity of traffic accidents. In this regard, research has recently been active to predictthe severity of traffic accidents by utilizing statistical models and deep learning models. In this paper, traffic accident dataset is convertedto color images to predict the severity of traffic accidents, and this is done via CNN models. For performance comparison, we experimentthat train the same data and compare the prediction results with the proposed model and other models. Through 10 experiments, wecompare the accuracy and error range of four deep learning models. Experimental results show that the accuracy of the proposed modelwas the highest at 0.85, and the second lowest error range at 0.03 was shown to confirm the superiority of the performance.
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