English(EN) Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework

机器学习框架利用GIS数据估算行人流量

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了一个利用GIS数据估算行人流量的机器学习框架，旨在改善交通机构的安全投资优先排序。该提出的流程包括特征选择和梯度提升模型，与传统的负二项广义线性模型（Negative Binomial GLM）相比，交叉验证的均方根误差（RMSE）降低了12%。表现最佳的模型，即基于直方图的梯度提升模型，采用泊松损失和L1 Lasso特征选择，在留存RMSE方面也降低了19%。相关代码已在GitHub上公开。 AI

影响该框架可以通过提供更准确的行人流量估算来改善城市规划和安全。

排序理由详细介绍一种新的机器学习框架在特定应用的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 27 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Bahareh Golchin, Banafsheh Rekabdar, Sirisha Kothuri, Joseph Broach · 2026-09-14 04:00

利用GIS衍生的建成环境特征估算行人流量：一个机器学习框架

arXiv:2609.12173v1 Announce Type: new 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 pipe…

本文如何被排名

报道来源 [1]

利用GIS衍生的建成环境特征估算行人流量：一个机器学习框架

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