traffic prediction models

Traffic prediction models analyze historical traffic data, such as vehicle flow, speeds, and times of day, to forecast future traffic conditions. They use algorithms—like machine learning or statistical methods—that identify patterns and trends, considering factors like weather, events, and roadwork. The goal is to estimate how congested roads will be at specific times, helping travelers choose better routes, and enabling traffic management systems to reduce congestion and improve safety. Essentially, these models transform past traffic information into reliable predictions, making travel planning more efficient and informed.