Evidence map›Paper›PMID 40064898›Full record

ArticleScientific data2025

Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review.

Amith Khandakar, David G Michelson, Mansura Naznine, Abdus Salam, Md Nahiduzzaman, Khaled M Khan, Ponnuthurai Nagaratnam Suganthan, Mohamed Arselene Ayari, Hamid Menouar, Julfikar Haider

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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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

10 authors.

Amith KhandakarDepartment of Electrical Engineering, Qatar University, Doha, 2713, Qatar. amitk@qu.edu.qa.
David G MichelsonDepartment of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
Mansura NaznineDepartment of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.ORCID http://orcid.org/0009-0007-0296-9981
Abdus SalamDepartment of Electrical Engineering, Qatar University, Doha, 2713, Qatar.
Md NahiduzzamanDepartment of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Khaled M KhanDepartment of Computer Science and Engineering, Qatar University, Doha, 2713, Qatar.
Ponnuthurai Nagaratnam SuganthanKINDI Center for Computing Research, College of Engineering, Qatar University, Doha, Qatar.
Mohamed Arselene AyariDepartment of Electrical Engineering, Qatar University, Doha, 2713, Qatar.
Hamid MenouarQatar Mobility Innovations Center (QMIC), Qatar University, Doha, Qatar.
Julfikar HaiderDepartment of Engineering, Manchester Metropolitan University, Chester Street, Manchester, M1 5GD, UK.ORCID http://orcid.org/0000-0001-7010-8285

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe collisions can result from aggressive driving and poor road conditions, emphasizing the need for effective monitoring to ensure safety. Smartphones, with their array of built-in sensors, offer a practical and affordable solution for road-sensing. However, the lack of reliable, standardized datasets has hindered progress in assessing road conditions and driving patterns. This study addresses this gap by introducing a comprehensive dataset derived from smartphone sensors, which surpasses existing datasets by incorporating a diverse range of sensors including accelerometer, gyroscope, magnetometer, GPS, gravity, orientation, and uncalibrated sensors. These sensors capture extensive parameters such as acceleration force, gravitation, rotation rate, magnetic field strength, and vehicle speed, providing a detailed understanding of road conditions and driving behaviors. The dataset is designed to enhance road safety, infrastructure maintenance, traffic management, and urban planning. By making this dataset available to the community, the study aims to foster collaboration, inspire further research, and facilitate the development of innovative solutions in intelligent transportation systems.

Identifiers

PMID40064898
PMCPMC11893768

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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.