Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework

Abstract: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 intersections in Portland, Oregon, from built-environment, land-use, and street-network features drawn from open GIS data. Starting from the Negative Binomial GLM used in practice, we add feature selection, count-aware gradient boosting, and repeated cross-validation, selecting one configuration by a combined rank over RMSE, MAPE, and SMAPE across four cross-validation strategies. The winner, a histogram-based gradient boosting model with Poisson loss and L1 Lasso feature selection, reduces cross-validated RMSE by 12% over the GLM baseline (89.8 to 78.7) and holdout RMSE by 19% (108.0 to 87.9). Code is released on GitHub.

Machine Learning (cs.LG)

arXiv:2609.12173 [cs.LG]

(or arXiv:2609.12173v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.12173 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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