Project Overview
This project developed novel machine learning algorithms for real-time traffic signal optimization. By analyzing historical traffic patterns and real-time sensor data, our system dynamically adjusts signal timing to minimize congestion and reduce emissions.
Key Achievements
- Reduced average commute times by 12% in pilot districts
- Decreased vehicle emissions by 8% through improved traffic flow
- Deployed system in 3 cities (San Francisco, Oakland, San Jose)
- Technology licensed to commercial traffic management companies
Publications
- Smith et al., “Deep Learning for Urban Traffic Prediction” (IEEE T-ITS 2024)
- Doe et al., “Reinforcement Learning for Signal Control” (AAAI 2022)
Impact
The project outcomes have been adopted by the California Department of Transportation for state-wide implementation, with an estimated annual savings of $50M in fuel costs and 100,000 tons of CO2 emissions reduction.