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<p>Traffic flow data in Europe are collected locally by city authorities using different systems and standards, making it difficult to compare cities or evaluate large-scale maps, such as those used for emission inventories. To address this gap, we compiled and harmonized publicly available traffic data for 36 European cities, linking geolocalized information to road segments, spanning years from 2015 to 2024 depending on data availability. Annual Average Daily (or Weekday) Traffic is provided, and supplementary variables (e.g., truck flow percentages and speed metrics) are included where available. The data are georeferenced, with geometries corresponding to each measurement location. The dataset was enriched with additional attributes through map matching of traffic measurement locations to OpenStreetMap. Code and methodology for transforming raw data into a uniform structure are documented in Python Jupyter Notebooks, ensuring transparency and reproducibility. This dataset in a unified format facilitates cross-city comparisons and supports applications in environmental science, including the estimation of greenhouse gas and pollutant emissions, as well as urban planning and road transport management.</p>

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Harmonized Annual Averaged Traffic Data at Street Segment Level for European Cities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月5日

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  • Xavier Bonnemaizon,
  • Philippe Ciais,
  • Chuanlong Zhou,
  • Qinren Shi,
  • Rohith Teja Mittakola,
  • Clément Goldmann,
  • Simon Ben Arous,
  • Nicolas Megel,
  • Steven J. Davis

摘要

Traffic flow data in Europe are collected locally by city authorities using different systems and standards, making it difficult to compare cities or evaluate large-scale maps, such as those used for emission inventories. To address this gap, we compiled and harmonized publicly available traffic data for 36 European cities, linking geolocalized information to road segments, spanning years from 2015 to 2024 depending on data availability. Annual Average Daily (or Weekday) Traffic is provided, and supplementary variables (e.g., truck flow percentages and speed metrics) are included where available. The data are georeferenced, with geometries corresponding to each measurement location. The dataset was enriched with additional attributes through map matching of traffic measurement locations to OpenStreetMap. Code and methodology for transforming raw data into a uniform structure are documented in Python Jupyter Notebooks, ensuring transparency and reproducibility. This dataset in a unified format facilitates cross-city comparisons and supports applications in environmental science, including the estimation of greenhouse gas and pollutant emissions, as well as urban planning and road transport management.