Predicting Traffic in the City of Buffalo Using a Neural Network

Every year, transportation departments spend significant resources physically surveying roads to measure traffic. Many roads go unmeasured. We built a Neural Network that predicts whether any road in the Buffalo-Niagara region is Low, Medium, or High traffic — no survey needed. What it does Given a road's location, type, direction, and region, the model instantly classifies its traffic level with 75% accuracy(WIP). City planners can use this to prioritize road repairs and signal upgrades. Businesses can use it to evaluate street-level traffic before opening a new location. How we built it We trained a feedforward Neural Network in PyTorch on 28,567 real road measurements from Open Data Buffalo. Key steps included log-transforming AADT to handle skew, rule-based feature engineering to reduce high-cardinality columns like road names and municipalities, and adding a custom distance-from-Buffalo feature to capture spatial traffic patterns. Challenges The biggest challenge was handling high