The latest articles on DEV Community by Mohammed Faisal Khan (@faisalkhan4k). https://dev.to/faisalkhan4k https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F1282394%2F84c66c8a-eb71-408c-bb63-0737466d9d09.png https://dev.to/faisalkhan4k en Mohammed Faisal Khan Tue, 03 Mar 2026 04:18:33 +0000 https://dev.to/faisalkhan4k/predicting-traffic-in-the-city-of-buffalo-using-a-neural-network-kh https://dev.to/faisalkhan4k/predicting-traffic-in-the-city-of-buffalo-using-a-neural-network-kh <p>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.</p> <p>What it does<br> 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.</p> <p>How we built it<br> 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.</p> <p>Challenges<br> The biggest challenge was handling high-cardinality categorical columns with 80+ unique values. We solved this using domain-driven binning — grouping road names into types (highway, avenue, street) and municipalities into geographic regions, which reduced noise and improved model convergence significantly.</p> <p>Accomplishments<br> Crossed the 75% accuracy threshold on unseen test data with a lean 4,515-parameter model — proving that simple, well-engineered features outperform complex architectures on structured tabular data.</p> <p>What we learned<br> Feature engineering matters more than model complexity. Spending time cleaning and transforming the data — log transforms, geographic groupings, distance features — had a bigger impact on accuracy than changing the network architecture.</p> <p>Built with<br> Python, PyTorch, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, Open Data Buffalo</p> deeplearning data programming ai Mohammed Faisal Khan Sun, 20 Apr 2025 13:47:35 +0000 https://dev.to/faisalkhan4k/machine-learning-emb https://dev.to/faisalkhan4k/machine-learning-emb machinelearning ai