Deep Learning Models for Predicting Traffic Patterns in Smart Cities

Applied various deep learning architectures to predict and analyze traffic patterns in urban environments, with AlexNet emerging as the best-performing model, achieving an accuracy of 93.18%.

The project utilized a comprehensive traffic dataset to capture flow dynamics across different times of day. Implemented statistical methods to validate model performance, enhancing reliability for real-time traffic predictions.