[논문]교통사고 심각 정도 예측을 위한 TATI 모델 제안

추민지; 박소현; 박영호

doi:10.3745/ktsde.2021.10.8.301

교통사고 심각 정도 예측을 위한 TATI 모델 제안
Proposed TATI Model for Predicting the Traffic Accident Severity 원문보기

정보처리학회논문지. KIPS transactions on software and data engineering. 소프트웨어 및 데이터 공학, v.10 no.8, 2021년, pp.301 - 310

추민지 (숙명여자대학교 IT공학과) , 박소현 (숙명여자대학교 빅데이터활용 연구센터) , 박영호 (숙명여자대학교 IT공학과)

초록
AI-Helper

TATI 모델이란 Traffic Accident Text to RGB Image 모델로, 교통사고 심각 정도 예측을 위한 본 논문에서 제안하는 방법론이다. 교통사고 치사율은 매년 감소하는 추세이나 OECD 회원국 중 하위권에 속해있다. 교통사고 치사율 감소를 위해 많은 연구들이 진행되었고, 그 중에서 교통사고 심각 정도를 예측하여 발생 및 치사율을 줄이기 위한 연구가 꾸준하게 진행되고 있다. 이와 관련하여 최근에는 통계 모델과 딥러닝 모델을 활용하여 교통사고 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 0.85로 가장 높은 정확도를 보였고, 0.03으로 두 번째로 낮은 오차 범위를 보여 성능의 우수성을 확인하였다.

Abstract ▼ AI-Helper

The TATI model is a Traffic Accident Text to RGB Image model, which is a methodology proposed in this paper for predicting the severity of traffic accidents. Traffic fatalities are decreasing every year, but they are among the low in the OECD members. Many studies have been conducted to reduce the death rate of traffic accidents, and among them, studies have been steadily conducted to reduce the incidence and mortality rate by predicting the severity of traffic accidents. In this regard, research has recently been active to predict the severity of traffic accidents by utilizing statistical models and deep learning models. In this paper, traffic accident dataset is converted to color images to predict the severity of traffic accidents, and this is done via CNN models. For performance comparison, we experiment that train the same data and compare the prediction results with the proposed model and other models. Through 10 experiments, we compare the accuracy and error range of four deep learning models. Experimental results show that the accuracy of the proposed model was the highest at 0.85, and the second lowest error range at 0.03 was shown to confirm the superiority of the performance.

주제어

표/그림 (10)

그림 Fig. 1. Overall Diagram of Severity Estimation and its Application
그림 Fig. 2. Flow Chart of TATI Model
표 Table 1. Data Variable Description and Code Generation Rules
표 Table 2. Relativity of 7 PV(Parent Variables) and 15 IV(Independent Variables)
그림 Fig. 3. Computes Feature Importance Results of 15 Independet Variables Normalizes between 0-1
그림 Fig. 4. Highest Accuracy under TATI Model at Different Structures
표 Table 3. Hyper-parameters Setting for TATI Model
표 Table 4. TATI Model at Different Structures
그림 Fig. 5. Accuracy Under 4 Models in Ten Experiments
표 Table 5. Experiments Result TATI Model at Different Structures

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