Evidence map›Paper›PMID 41318590›Full record

ArticleScientific data2025

IUTF Dataset: Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation.

Xuhui Lin, Qiuchen Lu, Long Chen, Jack Chin Pang Cheng, Jiayi Yan, Jingke Hong, Pengjun Zhao

Abstract read
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Xuhui LinPhd Candidate, The Bartlett School of Sustainable Construction, University College London, London, UK.
Qiuchen LuProfessor/Royal Academy of Engineering (RAEng)/Leverhulme Trust Research Fellow, The Bartlett School of Sustainable Construction, University College London, London, UK. qiuchen.lu@ucl.ac.uk.ORCID http://orcid.org/0000-0001-8687-0901
Long ChenAssistant Professor, Department of Architecture and Civil Engineering, The City University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0000-0002-6771-752X
Jack Chin Pang ChengProfessor, Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Jiayi YanScientist, Department of Information Services, University of Cambridge, Cambridge, UK.
Jingke HongProfessor, Chongqing University, Chongqing, China.ORCID http://orcid.org/0000-0002-5038-4167
Pengjun ZhaoProfessor, School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, China.ORCID http://orcid.org/0000-0001-5373-5551

Funding

Royal Academy of Engineering N/A
6 · The paper itself

Abstract

Understanding the impact of extreme weather, particularly flooding, on urban transportation systems is critical for enhancing city resilience and traffic management. However, research and policy development are often hampered by a lack of datasets that comprehensively integrate detailed traffic dynamics, high-resolution weather information, and road network topology across multiple diverse urban environments. To address this significant gap, we present the Integrated Urban Traffic-Flood (IUTF) dataset. This open-access resource covers 40 major cities across Europe, North America, and Asia, including 21,739 sensors. The IUTF dataset uniquely combines (i) high-resolution traffic parameters derived from over 21,700 sensors (with raw data typically at 5-minute intervals, harmonised to hourly); (ii) detailed hourly precipitation data from ERA5 reanalysis, spatially aligned with (iii) the underlying road network topology for over 1 million road segments, processed from OpenStreetMap. This meticulously curated and validated dataset, created through a novel spatio-temporal harmonisation framework, enables unprecedented, cross-border analysis of weather impacts on urban mobility. It provides a foundational data resource to support applications in traffic flow prediction, infrastructure planning, and the future development of quantitative resilience models.

Identifiers

PMID41318590
PMCPMC12800332

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LicenceCC BY
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Registered trials

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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.