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Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework
Transportation agencies need pedestrian volume estimates across entire road networks to prioritize safety investments, yet manual counts are expensive and cover only a small share of intersections. We present a machine learning pipeline that predicts 2-hour PM peak pedestrian volume at 101 urban int
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