UrbanSense is a digital twin system that helps cities anticipate problems before they occur. It creates a virtual model of a small urban area using real-world data such as traffic density, air quality, weather conditions, and population movement. These factors are treated as an interconnected system where changes in one affect others. UrbanSense enables “what-if” simulations to study scenarios like increased traffic or extreme weather and provides data-driven recommendations through a simple dashboard to support proactive urban planning and better decision-making.
I used AI tools during the development of UrbanSense for debugging and UI-related suggestions. ChatGPT and Claude Opus 4.5 were used to help identify and fix errors, improve CSS styling (including background and layout), and refine implementation ideas. All suggestions were reviewed, modified, and manually implemented by me.
