Forecasting road traffic accident using deep artificial neural network approach in case of Oromia Special Zone

Millions of people are dying, and billions of properties are damaged by road traffic accidents each year worldwide. In the case of our country Ethiopia, the effect of traffic accidents is even more by causing injuries, death, and property damage. Forecasting road traffic accident and predicting the severity of road traffic accident contributes a role indirectly in reducing road traffic accidents. This study deals with forecasting the number of accident and prediction of the severity of an accident in the Oromia Special Zone using deep artificial neural network models. Around 6170 road traffic accidents data are collected from Oromia Police Commission Excel data and Oromia Special Zone Traffic Police Department; the dataset consists of accidents in the Special Zone of Oromia Districts (Woredas) from 2005 to 2012 with 15 accidents attributes. 5928 or (80%) of the dataset was used for the training model, and 1482 or (20%) of the dataset was used for the testing model. This study proposed six different neural network architectures such as backpropagation neural network (BPNN), feedforward neural network (FFNN), multilayer perceptron neural network (MLPNN), recurrent neural networks (RNN), radial basis function neural network (RBFNN) and long short-term memory (LSTM) models for accident severity prediction and the LSTM model for a time serious forecasting of number accidents within specified years. The models will take input data, classify accidents, and predict the severity of an accident. Accident predictor GUI has been created using Python Tkinter library for easy accident severity prediction. According to the model performance results, RNN model showed the best prediction accuracy of 97.18%, whereas MLP , LTSM, RBFNN, FFNN, and BPNN models showed the accuracy of 97.13%, 91.00%, 87.00%, 80.56%, and 77.26%, respectively. LTSM model forecasted accident for three years which is 3555 where the actual accident number is 3561. The prediction and forecast result obtained from the model will be helpful in planning and management of road traffic accidents.