https://joaocarlos.github.io/Hugo-academia/projects/Recent content in Projects on Dr. Jane SmithHugoenhttps://joaocarlos.github.io/Hugo-academia/projects/ai-for-traffic-optimization/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/projects/ai-for-traffic-optimization/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.https://joaocarlos.github.io/Hugo-academia/projects/smart-urban-monitoring-network/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/projects/smart-urban-monitoring-network/Project Overview The Smart Urban Monitoring Network (SUMN) is a large-scale research project aimed at deploying and managing a city-wide network of environmental sensors. The project combines cutting-edge IoT hardware with advanced machine learning algorithms to provide real-time insights into urban environmental conditions. Objectives Deploy 1,000+ environmental sensors across San Francisco Develop adaptive sensing algorithms to optimize data collection Create predictive models for air quality and noise pollution Provide open data access to researchers and city planners Key Results Successfully deployed 500 sensors in pilot phase Achieved 99.