Fuzzy Logic-Embedded Model With Machine Learning for Traffic Congestion Prediction

This study explores the application of fuzzy logic-embedded machine learning models for traffic congestion classification and prediction. The main objective is to compare the performance of a Fuzzy Logic-Embedded Long Short-Term Memory (FL LSTM) model, a Fuzzy Logic-Embedded Random Forest (FL RF), and a Fuzzy Logic-Embedded Support Vector Machine (FL SVM) for predicting traffic congestion levels. A simulated dataset, incorporating features such as traffic volume, vehicle speed, and road occupancy, was used to train and test the models. Results indicated that the FL RF model outperformed both FL LSTM and FL SVM in terms of accuracy, with the highest classification accuracy and lowest misclassification rates observed in the confusion matrix. The FL LSTM model, while effective in capturing temporal dependencies, plateaued in accuracy, while the FL SVM struggled to differentiate between certain congestion levels. The performance of FL RF is attributed to its robustness in handling high-dimensional data and noise, which is crucial for real-world traffic prediction. This study highlights the potential of integrating fuzzy logic with machine learning to handle uncertainty and imprecision in traffic data and suggests that future work could focus on incorporating deep learning techniques for further improvements in accuracy and real-time prediction capabilities.