Traffic flow forecasting using support vector machine and comparing prediction accuracy with decision tree Available to Purchase

Unlike Decision Tree (DT), the goal of this learning is to improve the accuracy of Support Vector Machine (SVM) traffic flow projections for site visitors. Materials and methods: Predictions of short-term traffic flows are erroneous because existing approaches do not account for the complicated nonlinearity of visitor patterns. In this research, we offer a deep learning-based model that employs a unique hybrid approach and multi-layer architectures to autonomously extract fundamental components of visitors’ drift data. Results: Data from both the training and testing sets has been thoroughly examined. The Support Vector Machine (SVM) achieves a higher accuracy rate of 97.04% compared to the Decision Tree (DT), which only manages 92.38%. With p-values below 0.05 and 0.001, the results show that the groups are statistically significant. Conclusion: Support Vector Machine (SVM) outperforms Decision Tree (DT) algorithm when it comes to forecasting traffic speed and flow.