machine learning in traffic management

Machine learning in traffic management involves using algorithms that analyze large amounts of traffic data to identify patterns and make predictions. This enables systems to optimize traffic flow, reduce congestion, and improve safety by adjusting signals, estimating travel times, and managing incidents proactively. Essentially, machines learn from past traffic behaviors to make informed decisions in real-time, helping cities run smoother and commuter experiences be more efficient.