<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Traffic on Dr. Jane Smith</title><link>https://joaocarlos.github.io/Hugo-academia/tags/traffic/</link><description>Recent content in Traffic on Dr. Jane Smith</description><generator>Hugo</generator><language>en</language><atom:link href="https://joaocarlos.github.io/Hugo-academia/tags/traffic/index.xml" rel="self" type="application/rss+xml"/><item><title>AI for Traffic Optimization</title><link>https://joaocarlos.github.io/Hugo-academia/projects/ai-for-traffic-optimization/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://joaocarlos.github.io/Hugo-academia/projects/ai-for-traffic-optimization/</guid><description>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.</description></item></channel></rss>