<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10012873" mdate="2023-01-02 00:00:00"> <author>Celso L. Fernando and Toshio Yoshii and Takahiro Tsubota</author> <title>Combining the Deep Neural Network with the KMeans for Traffic Accident Prediction</title> <pages>1 - 8</pages> <year>2023</year> <volume>17</volume> <number>1</number> <journal>International Journal of Computer and Systems Engineering</journal> <ee>https://publications.waset.org/pdf/10012873</ee> <url>https://publications.waset.org/vol/193</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>Understanding the causes of a road accident and predicting their occurrence is key to prevent deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the kmeans with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network. </abstract> <index>Open Science Index 193, 2023</index> </article>