Evidence map›Paper›PMID 42082517›Full record

ArticleScientific data2026

RoadSens-4M: A Multimodal Smartphone & Camera Dataset for Holistic Road-way Analysis.

Amith Khandakar, David G Michelson, Shaikh Golam Rabbani, Fariya Bintay Shafi, Md Faysal Ahamed, Khondokar Radwanur Rahman, Md Abidur Rahman, Md Fahmidun Nabi, Mohamed Arselene Ayari, Khaled Khan and 1 more

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Article in Scientific data, 2026. 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

11 authors.

Amith KhandakarDepartment of Electrical Engineering, Qatar University, Doha, Qatar. amitk@qu.edu.qa.
David G MichelsonDepartment of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada.
Shaikh Golam RabbaniDepartment of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Fariya Bintay ShafiDepartment of Electrical Engineering, Qatar University, Doha, Qatar.
Md Faysal AhamedDepartment of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Khondokar Radwanur RahmanDepartment of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Md Abidur RahmanDepartment of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Md Fahmidun NabiDepartment of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Mohamed Arselene AyariDepartment of Civil and Environmental Engineering, Qatar University, Doha, Qatar.
Khaled KhanDepartment of Computer Science and Engineering, Qatar University, Doha, Qatar. k.khan@qu.edu.qa.
Ponnuthurai Nagaratnam SuganthanDepartment of Computer Science and Engineering, Qatar University, Doha, Qatar.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It's important to monitor road issues such as bumps and potholes to enhance safety and improve road conditions. Smartphones are equipped with various built-in sensors that offer a cost-effective and straightforward way to assess road quality. However, progress in this area has been slow due to the lack of high-quality, standardized datasets. This paper discusses a new dataset created by a mobile app that collects sensor data from devices like GPS, accelerometers, gyroscopes, magnetometers, gravity sensors, and orientation sensors. This dataset is one of the few that integrates Geographic Information System (GIS) data with weather information and video footage of road conditions, providing a comprehensive understanding of road issues with geographic context. The dataset allows for a clearer analysis of road conditions by compiling essential data, including vehicle speed, acceleration, rotation rates, and magnetic field intensity, along with the visual and spatial context provided by GIS, weather, and video data. Its goal is to provide funding for initiatives that enhance traffic management, infrastructure development, road safety, and urban planning. Additionally, the dataset will be publicly accessible to promote further research and innovation in smart transportation systems.

Identifiers

PMID42082517
PMCPMC13365414

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LicenceCC BY-NC-ND
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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.