A novel approach to predict the traffic accident assistance based on deep learning

Abstract According to the World Health Organization, thousands of people die every year in road traffic accidents. A crucial problem is the prediction of medical assistance in these accidents. For this purpose, we propose a new deep learning model whose goal is to distinguish whether a traffic accident requires medical assistance. The proposed perspective is general, so the model is valid for any dataset from any city. For this purpose, we present a model divided into three differentiated stages. In the first pre-processing stage, a general data treatment is performed, from data collection and cleaning to balancing. Secondly, the post-processing stage employs genetic and boosting algorithms to obtain the importance of all the data set variables used in the prediction. In the last stage, Model Training, a new model based on two-dimensional convolutional neural networks is applied to obtain a prediction of the need for medical assistance in traffic accidents. Finally, we test the effectiveness and accuracy of the proposed model by applying it to traffic accident datasets in six different cities. The obtained experimental results show that our framework achieves higher accuracy in all cities compared to six state-of-the-art models, confirming its suitability and applicability, even in real time.