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

The paper introduces a machine learning pipeline that predicts 2‑hour peak pedestrian volumes at 101 intersections in Portland, Oregon, using built‑environment, land‑use, and street‑network features from open GIS data. Starting from a Negative Binomial GLM, the authors add feature selection, count‑aware gradient boosting, and repeated cross‑validation, ultimately selecting a histogram‑based gradient boosting model with Poisson loss and L1 Lasso feature selection. This model reduces cross‑validated RMSE by 12% and holdout RMSE by 19% compared to the GLM baseline.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• safety