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Research on Weigh-in-Motion Algorithm of Vehicles Based on BSO-BP
Suan Xu1, Xing Chen1, Yaqiong Fu1
1School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|March 26, 2022
Summary
This study introduces an improved weigh-in-motion (WIM) model using beetle swarm optimization (BSO) and a backpropagation (BP) neural network. The BSO-BP model significantly enhances WIM system accuracy for vehicle weight measurements.
Area of Science:
- Engineering
- Computer Science
- Transportation Science
Background:
- Weigh-in-motion (WIM) systems are crucial for real-time vehicle weight monitoring.
- Existing WIM systems often suffer from low accuracy, necessitating improved modeling techniques.
Purpose of the Study:
- To develop a novel WIM model that enhances measurement accuracy.
- To address the limitations of current WIM systems through advanced algorithms.
Main Methods:
- Analysis of WIM system structure and principles.
- Wavelet transform for WIM signal denoising and reconstruction.
- Development of a beetle swarm optimization (BSO) algorithm-optimized error backpropagation (BP) neural network model.
Main Results:
- The BSO-BP WIM model demonstrated fast convergence speed and high accuracy.
- Achieved a relative error of 1.41% for maximum gross weight.
- Achieved a relative error of 6.69% for maximum axle weight.
Conclusions:
- The proposed BSO-BP WIM model offers a significant improvement in accuracy over existing methods.
- BSO effectively optimizes the BP neural network for WIM signal processing.
- This model provides a reliable solution for accurate vehicle weight measurement.
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