Evidence map›Paper›PMID 39987191›Full record

ArticleScientific data2025

In-Depth Analysis of Tram Curve Noise Dataset for Rigorous Noise Assessment.

Sakdirat Kaewunruen, Mohamad Ali Ridho Bin Khairul Anuar, Junhui Huang, Hao Liu

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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Sakdirat KaewunruenUniversity of Birmingham, Birmingham, United Kingdom. s.kaewunruen@bham.ac.uk.ORCID http://orcid.org/0000-0003-2153-3538
Mohamad Ali Ridho Bin Khairul AnuarUniversity of Birmingham, Birmingham, United Kingdom.
Junhui HuangUniversity of Birmingham, Birmingham, United Kingdom.
Hao LiuUniversity of Birmingham, Birmingham, United Kingdom.

Funding

EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 Marie Skłodowska-Curie Actions (H2020 Excellent Science - Marie Skłodowska-Curie Actions) 691135
6 · The paper itself

Abstract

The growth of cities, both socially and economically, has resulted in a higher utilization of trains and high-density mobility. However, this has also led to an escalation of urban environmental issues, specifically pertaining to noise generated by rail operations. The amplified contact between wheels and rails, as well as the vibrations in trains and track structures, contributes to railway noise pollution. While there is existing research on railway noise, there remains a notable gap in the investigation of noise generated specifically at curved sections of railway tracks. Hence, this paper presents 110 sets of tram curve noise datasets, recorded with the MOTIV APP in accordance with ISO 3095:2013 standards. The dataset covers impact, rolling, flanging, and squeal noise, alongside parameters such as speed, direction, crowd levels, and weather conditions. The data are collected by recording the noise generated by the trams at the centre of the curve alignment when the trams traverse along the curve alignment. The comprehensive dataset and analysis contribute valuable insights into tram curve noise, aiding urban planning and noise mitigation efforts.

Identifiers

PMID39987191
PMCPMC11846905

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.