Prediction of traffic accidents on suburban roads Isfahan Province using recurrent neural networks

Deep learning techniques play crucial role in today's modern world. In recent years, the recurrent neural network has led to extensive research for predicting time series. This study tries to take into account time series dependencies using recurrent neural network. Based on long-short-term memory algorithm, to predict the severity of driving injuries based on 10,269 random records that occurred on the roads of Isfahan province from 2018 to December 2021, it should be designed and implemented. To do this, several network architectures and configurations were tested through a systematic grid search to determine an optimal network for predicting the injury severity of traffic accidents. The selected network architecture is fully composed of seven independent variables, a long-short-term memory layer with 64 input nodes, and an output layer with a softmax function. Also, in order to understand the advantages and better comparison in this model, two optimal algorithms including the random gradient descent algorithm and Adam's optimal algorithm were also compared, So that the results of the model on the network showed that Adam's optimal algorithm works better than the random gradient descent algorithm because the accuracy of the model when using Adam's algorithm is equal to It became 73.26%, while for the random gradient descent algorithm, the accuracy of the model reached 68.20%. The findings of this study showed that the recurrent neural network model in the framework of deep learning can be a promising tool for predicting the severity of accidents.