Evidence map›Paper›PMID 40871761›Full record

ArticleSensors (Basel, Switzerland)2025

Vehicle Load Information Acquisition Using Roadside Micro-Electromechanical Systems Accelerometers.

Qian Zhao, Zhoujing Ye, Zhao Tan, Jie Xu, Linbing Wang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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

5 authors.

Qian ZhaoNational Engineering Research Center for Digital Construction and Evaluation of Urban Rail Transit, China Railway Design Corporation, Co., Ltd., Tianjin 300308, China.
Zhoujing YeNational Center for Material Service Safety, University of Science and Technology Beijing, Beijing 100083, China.ORCID 0000-0001-8115-1780
Zhao TanNational Engineering Research Center for Digital Construction and Evaluation of Urban Rail Transit, China Railway Design Corporation, Co., Ltd., Tianjin 300308, China.
Jie XuSchool of Civil Engineering, Tianjin University, Tianjin 300350, China.ORCID 0000-0002-6930-979X
Linbing WangThe Sensing and Perception Lab, University of Georgia, Athens, GA 30602, USA.ORCID 0000-0003-2670-376X

Funding

National Natural Science Foundation of China 52208414
6 · The paper itself

Abstract

Vehicle load is crucial for road design, maintenance, and expansion, while vehicle speed and lateral position are essential for traffic management and driving safety. This paper introduces a method for collecting vehicle speed, lateral position, and load information using roadside Micro-Electromechanical Systems (MEMS) accelerometers located on the pavement. Firstly, this research analyzes the distribution of pavement vibration responses in both lateral and vertical directions based on the Finite Element Method (FEM) data provided in the literature. Then, pavement vibration data is collected by roadside sensors with a Full-scale Accelerated Loading Tester, considering varying vehicle speeds, loads, and lateral positions. The results reveal that the vertical peak acceleration increases linearly with vehicle speed within a range of 5-22 km/h, decreases following a power law as the lateral distance between the wheel center and sensor increases from 0.4 to 0.9 m, which is consistent with the trends observed in the literature's FEM data. The vibration energy of the vertical acceleration exhibits a positive linear correlation with the total vehicle load, with a correlation coefficient of 0.885. This approach offers a practical method for vehicle load estimation, optimal sensor deployment, and enhancement of pavement performance monitoring systems.

Indexed as

Accelerated Pavement Testing (APT)lateral positionMEMS accelerometerpavement vibrationvehicle load estimationvehicle speed

Identifiers

PMID40871761
PMCPMC12390523

What OpenQuestion holds

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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.