How can machine learning improve traffic prediction?

How can machine learning improve traffic prediction?

Machine learning can analyze vast amounts of traffic data to identify patterns and predict congestion.

What types of data are used for these predictions?

Data such as historical traffic patterns, weather conditions, and real-time sensor data are commonly used.

Are there specific algorithms that work best for this?

Yes, algorithms like neural networks, decision trees, and support vector machines are often effective.

How accurate are these predictions?

Accuracy can vary, but many models achieve over 80% accuracy in predicting traffic conditions.

What are the real-world applications of this technology?

Applications include smart traffic lights, navigation apps, and urban planning to reduce congestion.