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

2026-09-10

AI Summary

研究使用机器学习方法从GIS数据中预测行人流量，以解决交通机构需要但难以通过手动计数获得的全路网行人量估计问题。

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.

Problem

• Pedestrian Volume
• GIS Data
• Machine Learning

Innovation

• machine learning
• pedestrian volume estimation
• gradient boosting
• feature selection
• cross-validation