Evaluating Traffic Flow Prediction Accuracy Using Support Vector Machines and Feedforward Neural Networks

SVM's predicting accuracy for traffic flow in comparison to FeedForward neural networks. The extensive nonlinearity of the site visitors flow is too complex for the existing methodologies to accurately estimate short-term traffic flow, resulting in subpar prediction accuracy. In this research, we propose an absolutely mannequin based on deep learning that uses multiple-layer architectures and novel hybrid approaches to automatically identify fundamental components of traffic flow data. The train dataset evaluation and look at the dataset have been completed successfully. Compared to the FeedForward Neural Network, which has an accuracy of 94.28%, the Support Vector Machine (SVM) is 97.99% more accurate. < 0.001 statistical significance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(p&lt;0.05)$</tex>. The groupings are statistically significant, according to the data. In conclusion, Support Vector Machine outperforms the FeedForward Neural Network algorithm in traffic flow and speed forecasting predictions.