A Weather-Informed Traffic Forecasting System Constructed Using the SAE Method

Access to accurate traffic data is critical for drivers to effectively plan routes and reduce congestion. Traffic patterns, including vehicle speeds and volumes, may be analysed with the use of monitors set up along roads. The vast majority of currently available traffic prediction systems make use of historical traffic data. But, there are two issues with people: (1) they just used traffic statistics to make forecasts, and (2) they didn't consider the volume of data or the time it would take to analyses it. Yet, the weather has a significant effect in predicting how busy the roads will be. In this research, we provide a WTPS, or weather-based traffic predictive model, which uses both traffic and weather information to make accurate predictions. The experimental findings demonstrate that WTPS beats the state-of-the-art methods in terms of prediction accuracy in both dry and wet conditions. In order to handle high volumes of traffic, WTPS was developed using big data concepts.