M.S. (Master of Science)
Department of Statistics and Actuarial Science
There have been numerous studies on traffic accidents and their fatality rate. For this challenging machine learning regression problem, Neural Networks (NNs) have produced state-of-the-art data. Despite their success, they are often used in a fre- quentist scheme, which means they cannot account for uncertainty in their forecasts. BNNs are comprised of a Probabilistic Model and a Neural Network. The aim of such a design is to bring together the benefits of Neural Networks and stochastic modeling. Neural networks have the ability to approximate continuous functions uni- versally. Statistical models allow for the direct definition of a model with known parameter interactions in order to produce results. As a result, both DNNs and BNNs are implemented in this article, and then a model evaluation was performed. The data set used in this paper is U.S. Fatalities data from open source CRAN R package named AER. For the model evaluation, two measures were employed: mean absolute error (MAE) and root mean square error (RMSE). The low MAE and RMSE observed in the results obtained using the proposed random forest model demonstrate its accuracy.
Hu, Yiqun, "Traffic Fatality Rate Prediction Based on Deep Neural Network and Bayesian Neural Network" (2021). Graduate Research Theses & Dissertations. 7213.
Northern Illinois University
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