Research on Weigh-in-Motion Algorithm of Vehicles Based on BSO-BP.

Suan Xu,Kaixing Hong,Xing Chen,Hongwei Xu,Yaqiong Fu

doi:10.3390/s22062109

Abstract

Weigh-in-motion (WIM) systems are used to measure the weight of moving vehicles. Aiming at the problem of low accuracy of the WIM system, this paper proposes a WIM model based on the beetle swarm optimization (BSO) algorithm and the error back propagation (BP) neural network. Firstly, the structure and principle of the WIM system used in this paper are analyzed. Secondly, the WIM signal is denoised and reconstructed by wavelet transform. Then, a BP neural network model optimized by BSO algorithm is established to process the WIM signal. Finally, the predictive ability of BP neural network models optimized by different algorithms are compared and conclusions are drawn. The experimental results show that the BSO-BP WIM model has fast convergence speed, high accuracy, the relative error of the maximum gross weight is 1.41%, and the relative error of the maximum axle weight is 6.69%.

Highlights

Aiming at the defects of the back propagation (BP) neural network and the superiority of the beetle swarm optimization (BSO) algorithm, this paper proposes a WIM model based on the BSO
This paper proposed a BP WIM model optimized based on the BSO algorithm to improve the accuracy of the WIM system
After the optimization of the BSO-BP model, the average relative error of axle weight of test set was reduced from 3.38% to 1.41%, the maximum relative error of axle weight was reduced from 6.86% to 6.69%, the average relative error of gross weight was reduced from 5.89% to 0.53%, and the maximum relative error of gross weight was reduced from 15.07% to 1.41%

Summary

Introduction

The problem of vehicle overload seriously endangers traffic safety, causing traffic accidents, and causing fatal damage to the structure of roads and bridges [1]. Weight measurement is the most direct way to identify overloaded vehicles. Static weighing of moving vehicles on the road requires the vehicle to stop, which is likely to cause traffic jams and affect the efficiency of vehicle traffic. Weigh-in-motion (WIM) can measure the weight of a moving vehicle without affecting vehicle traffic, so it has a higher detection efficiency. A proper estimation of weighing accuracy is crucial for WIM systems to be efficient in direct enforcement of overloading [2]. There are many factors that affect the accuracy of WIM, and the relationship between different influencing factors is unknown. Artificial neural networks (ANN) possess the characters of strong nonlinear mapping, adaptive learning, fault-tolerance performance, and robustness

Objectives

Results

Conclusion