Urban Traffic AI Optimization: How to Make Traffic Lights Smarter
How a traffic management department in a third-tier city used an AI traffic signal optimization system to increase average main road speed by 23% and reduce peak congestion duration by 35%. The complete process from POC to city-level deployment, suitable as a reference for smart city projects.
Steps
- 1
Traffic flow data collection: Deploy cameras and geomagnetic sensors at major intersections to collect real-time data on traffic volume, speed, and queue length.
- 2
Build traffic model: Use Python + TensorFlow to train an intersection flow prediction model, learning traffic patterns at different times of the day.
- 3
Intelligent signal optimization algorithm: Dynamically adjust signal timing based on real-time traffic data to reduce unnecessary waiting.
- 4
POC validation: Select 5 typical intersections for small-scale testing, collect 3 months of data to verify effectiveness.
- 5
City-level rollout: Extend the successful model to over 200 intersections across the city and establish a centralized monitoring center.
Recommended tools
Also available in 中文.