Vehicle Bridge Wireless Load Monitoring System Based on Polynomial Regression

Yu Wang

doi:10.47297/taposatWSP2633-456926.20260703

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Vehicle Bridge Wireless Load Monitoring System Based on Polynomial Regression

Volume 7, Issue 3

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Vehicle Bridge Wireless Load Monitoring System Based on Polynomial Regression
Vehicle Bridge Wireless Load Monitoring System Based on Polynomial Regression
University of Shanghai for Science and Technology, Shanghai, 200093, China
Vol. 7, Issue 3, Pages: 127-133(2026)
Published：30 March 2026
DOI：10.47297/taposatWSP2633-456926.20260703
Accepted：

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Yu Wang.Vehicle Bridge Wireless Load Monitoring System Based on Polynomial Regression[J].Theory and Practice of Science and Technology,2026,07(03):127-133.
DOI：

Yu Wang.Vehicle Bridge Wireless Load Monitoring System Based on Polynomial Regression[J].Theory and Practice of Science and Technology,2026,07(03):127-133. DOI： 10.47297/taposatWSP2633-456926.20260703.

摘要

Abstract

Overloading of vehicles is a common phenomenon in society

exhibiting high concealment during normal operation and thus being difficult to detect. Therefore

this paper proposes a novel load collection and wireless communication system that integrates wireless communication and machine learning to achieve real-time load prediction for small freight vehicles. The system is based on radio frequency (RF) communication. It collects axle stress data from the freight vehicle

transmits the acquired data wirelessly to a receiver

and then forwards it to the on-board computer via Controller Area Network (CAN) communication. Ultimately

a machine learning model is employed to predict the vehicle load

thereby enabling load monitoring. The proposed method is based on a lightweight algorithmic model. Considering the requirements for long-term operation

a low-power design is implemented to minimize deployment costs while ensuring fundamental computational capability. By employing a polynomial regression prediction algorithm and evaluating its overall performance on a real-world load dataset

the optimal data collection points are identified. Experimental validation demonstrates that the mean squared error of the prediction results is 0.3149

and the cross-validation score reaches 0.9233. Furthermore

the maximum error within the 95% confidence interval accounts for only 1.658% of the maximum axle load. These results confirm the system's capability to accurately predict actual load data.

关键词

Keywords

references

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