Walking through Toronto, we noticed how some streets could work better or how an addition of park equipment can affect the community. These changes opened us to the field of urban planning and how good urban planning brings people together. We wanted to help make that kind of change.
We built a 3D street editor with Next.js and MapLibre that accurately shows downtown Toronto using OpenStreetMap data. Users select an area of the map and edit lanes, signals, signs and green space. Designs are checked against City of Toronto guidelines, and a Python worker runs SUMO traffic simulations, with results stored in Convex.
We learned how to use pre-trained models, build a front end with Next.js, and work with mapping tech like MapLibre. We also saw how intricate an urban planner’s workflow really is.
Our biggest challenges were limited time, finding and choosing the right data, and getting the traffic simulations to behave realistically. On the technical side of things, coming up with a Convex-to-Python pipeline was also quite architecturally difficult.
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