IoT-Based Traffic Prediction for Smart Cities

This study explores the integration of Convolutional Neural Networks (CNNs) with Particle Swarm Optimization (PSO) to enhance traffic management in smart cities. The primary objective was to develop a predictive model that improves traffic forecasting accuracy, reduces congestion, and optimizes real-time traffic management. The methodology involved combining CNNs’ powerful spatial feature extraction capabilities with PSO’s optimization strengths. Traffic data collected from IoT sensors were processed to extract relevant spatial features using CNNs, and PSO was employed to fine-tune the CNN hyperparameters and feature selection. Key findings indicate that the CNN-PSO model significantly improves traffic prediction accuracy, with an average increase from 76.5% to 92.0% across various urban areas. The model demonstrated a 20.0% reduction in average traffic delay and a 25.0% enhancement in traffic flow efficiency. Real-time metrics revealed high prediction accuracy and low processing times, indicating the model’s effectiveness in timely traffic forecasting. These results underscore the model’s potential for optimizing traffic management systems, reducing congestion, and improving urban mobility. The implications for smart city development are substantial, as the model offers a robust solution for managing complex traffic patterns and enhancing real-time decision-making. The study suggests that integrating advanced machine learning techniques like CNNs and PSO can lead to more efficient and adaptive traffic management solutions, contributing to the development of smarter, more resilient urban environments. Future research could expand on these findings by incorporating additional data sources and testing the model’s scalability and real-world applicability.