当前位置：
X-MOL 学术
›
IEEE Access
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
RFCNN: Traffic Accident Severity Prediction Based on Decision Level Fusion of Machine and Deep Learning Model
IEEE Access ( IF 3.9 ) Pub Date : 2021-09-14 , DOI: 10.1109/access.2021.3112546 Mubariz Manzoor , Muhammad Umer , Saima Sadiq , Abid Ishaq , Saleem Ullah , Hamza Ahmad Madni , Carmen Bisogni
IEEE Access ( IF 3.9 ) Pub Date : 2021-09-14 , DOI: 10.1109/access.2021.3112546 Mubariz Manzoor , Muhammad Umer , Saima Sadiq , Abid Ishaq , Saleem Ullah , Hamza Ahmad Madni , Carmen Bisogni
Traffic accidents on highways are a leading cause of death despite the development of traffic safety measures. The burden of casualties and damage caused by road accidents is very high for developing countries. Many factors are associated with traffic accidents, some of which are more significant than others in determining the severity of accidents. Data mining techniques can help in predicting influential factors related to crash severity. In this study, significant factors that are strongly correlated with the accident severity on highways are identified by Random Forest. Top features affecting accidental severity include distance, temperature, wind_Chill, humidity, visibility, and wind direction. This study presents an ensemble of machine learning and deep learning models by combining Random Forest and Convolutional Neural Network called RFCNN for the prediction of road accident severity. The performance of the proposed approach is compared with several base learner classifiers. The data used in the analysis include accident records of the USA from February 2016 to June 2020. Obtained results demonstrate that the RFCNN enhanced the decision-making process and outperformed other models with 0.991 accuracy, 0.974 precision, 0.986 recall, and 0.980 F-score using the 20 most significant features in predicting the severity of accidents.
更新日期：2021-09-24
京公网安备 11010802027423号