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Sensor Data Fusion in Multi-Sensor Weigh-In-Motion Systems.

Janusz Gajda¹, Ryszard Sroka¹, Piotr Burnos¹

¹Department of Measurement and Electronics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Cracow, Poland.

Sensors (Basel, Switzerland)

|June 18, 2020

Summary

This study compared two methods for estimating vehicle weight from Multi-Sensor Weigh-In-Motion (MS-WIM) systems. The maximum likelihood estimator significantly reduced uncertainty in static load estimates compared to the mean value estimator.

Keywords:

accuracy of WIM systems data fusion multi-sensor WIM weigh in motion (WIM) systems

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Area of Science:

Transportation Engineering
Metrology
Data Analysis

Background:

Accurate estimation of Gross Vehicle Weight (GVW) and axle loads is crucial for road infrastructure management and traffic enforcement.
Multi-Sensor Weigh-In-Motion (MS-WIM) systems provide dynamic measurements that require processing to determine static loads.
Existing estimation methods may introduce uncertainty, impacting the reliability of WIM data.

Purpose of the Study:

To compare the performance of two estimators: mean value and maximum likelihood.
To evaluate their effectiveness in processing MS-WIM data for static load estimation.
To determine which estimator offers lower measurement uncertainty for WIM applications.

Main Methods:

Utilized both simulation methods with synthetic data and experimental data from a 16-line polymer axle load sensor WIM system.
Applied mean value and maximum likelihood estimators to process dynamic axle load measurements.
Assessed measurement uncertainty using the standard deviation of the static load estimate.

Main Results:

The maximum likelihood estimator demonstrated substantially lower uncertainty in static load estimates compared to the mean value estimator.
Both simulation and experimental data confirmed the superior performance of the maximum likelihood estimator.
The findings indicate a significant improvement in the reliability of WIM data processing.

Conclusions:

The maximum likelihood estimator is a more accurate and reliable method for determining static axle loads from MS-WIM systems.
This improved accuracy has significant practical implications for the long-term utilization and data integrity of MS-WIM systems.
The study recommends the adoption of the maximum likelihood estimator for enhanced WIM data analysis.