Our city’s waste collection is stuck in the stone age – weekly pickups regardless of bin status. Last weekend half the bins downtown overflowed before pickup, while suburban routes ran half-empty trucks. I’ve read about AI optimizing delivery routes, but how would that work for garbage collection? Need something that actually looks at waste accumulation patterns instead of fixed schedules. Anybody implemented dynamic route planning that adapts daily?
We solved this exact issue using Latenode’s AI agents. Set up cameras with image analysis models to estimate bin fill levels, then route optimization models adjust pickup schedules in real time.
Key advantage: Uses multiple specialized AI models through one platform instead of juggling separate services.
Full solution took 3 days to configure: https://latenode.com
Try using historical collection data first if real-time sensors aren’t feasible. We built a Python model analyzing past fill rates by neighborhood, weather patterns, and local events. It’s 70% accurate at predicting hotspots. Not perfect, but better than static routes. Next step is integrating live sensor data from smart bins when budget allows.
combine gmaps api w/ trash sensors. route algo sorts bins >80% full first
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