A hybrid support vector machine and neural network model with fuzzy logic fusion for smart city traffic prediction

Managing traffic effectively is one of the key challenges in building smarter, more sustainable cities. This paper presents a hybrid model designed to enhance traffic prediction and classification through more intelligent data analysis. The method utilises machine learning techniques, such as Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), and combines them to enhance their performance through decision-level integration and improved feature selection methods. The proposed approach is tested on a real-world dataset comprising 1243 traffic records, yielding improved performance using traditional measures of accuracy, sensitivity, and specificity. The achieved performance highlights the importance of integrating multiple intelligent approaches at the decision level, enabling the system to adapt to real-time changes in traffic conditions. Performance evaluations, with their standard deviations, demonstrate the superiority of our model (accuracy: 98.6%, sensitivity: 98.8%, and specificity: 98.2%). The proposed hybrid fuzzy decision fusion model is efficient in real-time and has been proven robust for Lahore's environment. The proposed model is specifically designed for mid-scale urban locations that are experiencing rapid growth, sharing the same infrastructure and resource characteristics as Lahore. To generalise it to smart cities, the model requires datasets from those cities to validate and then apply it effectively.