A New AI Framework Could Help Cities Plan for Future Traffic
Researchers at NYU Tandon have published a geospatial AI framework that pairs ARIMA and LSTM forecasting with H3 hexagonal hotspot analysis and a locally hosted LLaMA query portal, so planners can ask questions of fifteen years of New York traffic data in plain English. The neural model cut root mean square error to 342.56 vehicles per day against ARIMA’s 417.62, and the framework projects average daily volume rising from 12,540 in 2025 to 19,680 in 2029. This article works through the arithmetic, the quotes and the limits the authors state.
