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来自格拉斯哥城市交通控制系统的高分辨率交通流数据。

High-resolution traffic flow data from the urban traffic control system in Glasgow.

作者信息

Li Yue, Zhao Qunshan, Wang Mingshu

机构信息

Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow, UK.

School of Geographical and Earth Sciences, University of Glasgow, Glasgow, UK.

出版信息

Sci Data. 2025 Feb 12;12(1):253. doi: 10.1038/s41597-025-04494-y.

Abstract

Traffic flow data has been used in various disciplines, including geography, transportation, urban planning, and public health. However, existing datasets often have limitations such as low spatiotemporal resolution and inconsistent quality due to data collection methods and the need for an adequate data cleaning process. This paper introduces a long-term traffic flow dataset at an intra-city scale with high spatio-temporal granularity. The dataset covers the Glasgow City Council area for four consecutive years spanning the COVID-19 pandemic, from October 2019 to September 2023, providing comprehensive temporal and spatial coverage. Such detailed information facilitates diverse applications, including traffic dynamic analysis, traffic management, infrastructure planning, and urban environment improvement. Also, it provides a valuable dataset to understand traffic flow change during a once-in-a-lifetime pandemic event.

摘要

交通流数据已被应用于包括地理学、交通运输、城市规划和公共卫生等在内的各个学科。然而,由于数据收集方法以及充分的数据清理过程的需要,现有的数据集往往存在时空分辨率低和质量不一致等局限性。本文介绍了一个城市内部尺度上具有高时空粒度的长期交通流数据集。该数据集涵盖了格拉斯哥市议会区域,跨越2019年10月至2023年9月的连续四年,涵盖了新冠疫情期间,提供了全面的时空覆盖。如此详细的信息便于进行各种应用,包括交通动态分析、交通管理、基础设施规划和城市环境改善。此外,它还提供了一个有价值的数据集,以了解在一场百年一遇的疫情事件期间交通流的变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a738/11821839/c57883aa0247/41597_2025_4494_Fig1_HTML.jpg

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