S-FBLS-driven accident severity prediction at urban road intersection

Abstract:

In order to comprehensively improve the effect of traffic accident severity prediction, for the current stage of traditional machine learning and deep learning methods with limited prediction accuracy and slow convergence of the network, proposing an improved FBLS method for predicting accident severity at urban road intersection. The model replaces the feature node layer of BLS with Takagi-Sugeno fuzzy system to extract the hidden features of high-dimensional accident data more extensively and still retains the fast convergence characteristics of BLS; the SMOTE algorithm is also fused in the input layer of the FBLS to balance the accident data categories and enhance the reliability of the prediction results. Through the historical data of traffic accidents in Greater Manchester, UK, the original FBLS was selected in the horizontal dimension, and RF, SVM, BPNN, LSTM, CNN, which are commonly used for traffic accident severity prediction, were chosen in the vertical dimension, to compare the model performance with the S-FBLS. The results show that comparison with the original FBLS, S-FBLS improves the accuracy of severe accidents by 52.87%, comparison with five comparative models, S-FBLS improves the network training speed by more than 97%, improves the overall accuracy by 2.2%, 8.95%, 8.68%, 6.47%, 5.64%, improves the specificity by an average of 6.49%, improves the sensitivity by an average of 6.31%, and improves the precision by an average of 5.66%. The S-FBLS-driven accident severity prediction method can provide a reliable theoretical support for the early warning of the occurrence of accident at urban road intersection.