Evidence map›Paper›PMID 39939620›Full record

ArticleScientific data2025

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

Yue Li, Qunshan Zhao, Mingshu Wang

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yue LiUrban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow, UK.ORCID https://orcid.org/0009-0003-6531-1184
Qunshan ZhaoUrban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow, UK. Qunshan.Zhao@glasgow.ac.uk.ORCID http://orcid.org/0000-0002-5549-9457
Mingshu WangSchool of Geographical and Earth Sciences, University of Glasgow, Glasgow, UK. Mingshu.Wang@glasgow.ac.uk.ORCID http://orcid.org/0000-0001-5260-3143

Funding

RCUK | Economic and Social Research Council (ESRC) ES/L011921/1RCUK | Economic and Social Research Council (ESRC) ES/S007105/1Royal Society IEC\NSFC\223042
6 · The paper itself

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.

Indexed as

TransportationCitiesCity PlanningCOVID-19HumansPandemicsSARS-CoV-2Scotland

Identifiers

PMID39939620
PMCPMC11821839

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.