Scholarlink

<p>Quantifying urban traffic emission dynamics is critical for evaluating decarbonization policies, yet existing models often lack resolution, timeliness or scalability. Ubiquitous urban data offer new opportunities for large-scale, fine-grained and near-real-time emission estimation. Here we present a data-driven framework that integrates traffic camera footage with mobile phone data to estimate citywide vehicular emissions. Applied to Manhattan, New York, our method captures substantial spatiotemporal variation in emissions across hours, days and road segments. Omitting fine-grained inputs, such as traffic signals, speed variation or fleet heterogeneity, introduces average uncertainties of −49% to +25% in emission estimates. We further evaluate the early impacts of Manhattan’s congestion pricing programme, finding that 8 weeks after the implementation, traffic volumes declined by 10%, resulting in a 16–22% drop in emissions. Our approach enables timely, high-resolution policy assessment using widely available urban big data, offering a practical and transferable tool for supporting climate action.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Ubiquitous data-driven framework for traffic emission estimation and policy evaluation

  • 期刊论文

  • 发表日期：

    2026年4月2日

查看全文
  • Songhua Hu,
  • Paolo Santi,
  • Tom Benson,
  • Xuesong Zhou,
  • An Wang,
  • Ashutosh Kumar,
  • Carlo Ratti

摘要

Quantifying urban traffic emission dynamics is critical for evaluating decarbonization policies, yet existing models often lack resolution, timeliness or scalability. Ubiquitous urban data offer new opportunities for large-scale, fine-grained and near-real-time emission estimation. Here we present a data-driven framework that integrates traffic camera footage with mobile phone data to estimate citywide vehicular emissions. Applied to Manhattan, New York, our method captures substantial spatiotemporal variation in emissions across hours, days and road segments. Omitting fine-grained inputs, such as traffic signals, speed variation or fleet heterogeneity, introduces average uncertainties of −49% to +25% in emission estimates. We further evaluate the early impacts of Manhattan’s congestion pricing programme, finding that 8 weeks after the implementation, traffic volumes declined by 10%, resulting in a 16–22% drop in emissions. Our approach enables timely, high-resolution policy assessment using widely available urban big data, offering a practical and transferable tool for supporting climate action.