An Intelligent Driving Recommendation System Integrating Temporal Convolutional Networks and Fuzzy Logic for Real-Time Traffic and Weather Optimization

Traffic congestion and road safety are paramount challenges compounded by the rising number of cars on the roads.
Conventional traffic management systems using fixed rules and past data find dealing with dynamic road conditions difficult.
This study suggests an Intelligent Driving Recommendation System (IDRS) combining deep learning and rule-based algorithms for real-time traffic and weather observation. The key to the strategy is the use of Temporal Convolutional Networks (TCN) behind traffic and weather forecasting using fuzzy logic-based adaptive decision-making. The process involves data gathering from IoT sensors, real-time monitoring systems, meteorological sources, and preprocessing techniques such as normalization, outlier detection, and categorical encoding. TCN models are trained to forecast congestion and weather severity, and a fuzzy inference system produces context-aware driving recommendations. Experimental results demonstrate the efficacy of the proposed system, achieving 99.7% accuracy in predicting traffic conditions and weather effects. The proposed TCN-Fuzzy Logic
model surpasses LightGBM, CNN, and RFCNN, achieving 99.7% accuracy with enhanced precision, recall, and F1-score,
demonstrating superior performance in classification tasks through advanced temporal and fuzzy logic integration. A
comparative analysis was performed with those models, showing that the proposed TCN-Fuzzy Logic approach, with its edge in diminishing travel risks and efficiently managing traffic flows, emerges as superior. The study adds to developing AI-enabled real-time driving recommendation systems based on safety, efficiency, and sustainable intelligent transportation networks. The reed-looking study shall delve further into enhanced integration and efficacious optimization of calmative efficiency through edge computing techniques.