Integrating IoT and Deep Learning for Traffic Analysis and Prediction with Weather Variables

As the population grows, effective traffic management becomes increasingly critical for reducing traffic congestion and improving transportation efficiency. This study explores the integration of Internet of Things (IoT) devices and deep learning algorithms to enhance real-time traffic analysis and prediction, incorporating weather data as a significant variable. The proposed system leverages IoT sensors to collect data on the number of vehicles, date, time, and weather conditions, which are then processed using advanced deep learning techniques. Utilizing a dataset comprising traffic and weather information from Istanbul over thirteen months, the study employs a Gated Recurrent Unit (GRU) convolutional neural network model to predict traffic patterns. This model resulted in an average Root Mean Square Error (RMSE) of 0.7729. This research concludes by discussing the practical implications of deploying such integrated systems in urban settings, including the challenges of sensor deployment and data integration and the benefits of improved traffic prediction accuracy.