Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
Weigh-in-motion systems can measure the number of axles to obtain a vehicle’s type and upper limit of weight, which, combined with the weight measured by the system, can be used for highway toll collection and overload management. This paper proposes a new modular system based on multi-sensor fusion...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/614 |
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| author | Xiaoyong Liu Zhiyong Yang Bowen Shi |
| author_facet | Xiaoyong Liu Zhiyong Yang Bowen Shi |
| author_sort | Xiaoyong Liu |
| collection | DOAJ |
| description | Weigh-in-motion systems can measure the number of axles to obtain a vehicle’s type and upper limit of weight, which, combined with the weight measured by the system, can be used for highway toll collection and overload management. This paper proposes a new modular system based on multi-sensor fusion and neural network axle recognition to address issues concerning the high failure rate of axle recognition devices and low weighing accuracy. We use a modular system consisting of multiple weighing platforms, enabling whole-vehicle-load weighing with multiple vehicles traveling through platforms. In addition, we propose a sequential generation model based on a Transformer and Gated Recurrent Unit to calculate the weighing signal generated by the weighing sensors, and then obtain the number of axles and the gross vehicle weight. Finally, the axle recognition algorithm and modular systems are tested in multiple scenarios. The accuracy of the axle recognition is 99.51% and 99.84% in the test set and the toll station, respectively. The weighing error of the modular system in the test field is less than 0.5%, and 99.18% of vehicles had an error of less than 5% at the toll station. The modular system has the advantages of high accuracy, consistent performance, and high traffic efficiency. |
| format | Article |
| id | doaj-art-4632c02a2edc4ba8b0f1faf7fca45c77 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-4632c02a2edc4ba8b0f1faf7fca45c772025-01-24T13:20:04ZengMDPI AGApplied Sciences2076-34172025-01-0115261410.3390/app15020614Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural NetworksXiaoyong Liu0Zhiyong Yang1Bowen Shi2School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaWeigh-in-motion systems can measure the number of axles to obtain a vehicle’s type and upper limit of weight, which, combined with the weight measured by the system, can be used for highway toll collection and overload management. This paper proposes a new modular system based on multi-sensor fusion and neural network axle recognition to address issues concerning the high failure rate of axle recognition devices and low weighing accuracy. We use a modular system consisting of multiple weighing platforms, enabling whole-vehicle-load weighing with multiple vehicles traveling through platforms. In addition, we propose a sequential generation model based on a Transformer and Gated Recurrent Unit to calculate the weighing signal generated by the weighing sensors, and then obtain the number of axles and the gross vehicle weight. Finally, the axle recognition algorithm and modular systems are tested in multiple scenarios. The accuracy of the axle recognition is 99.51% and 99.84% in the test set and the toll station, respectively. The weighing error of the modular system in the test field is less than 0.5%, and 99.18% of vehicles had an error of less than 5% at the toll station. The modular system has the advantages of high accuracy, consistent performance, and high traffic efficiency.https://www.mdpi.com/2076-3417/15/2/614axle recognitionmodular systemtransformergated recurrent unitweigh-in-motion |
| spellingShingle | Xiaoyong Liu Zhiyong Yang Bowen Shi Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks Applied Sciences axle recognition modular system transformer gated recurrent unit weigh-in-motion |
| title | Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks |
| title_full | Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks |
| title_fullStr | Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks |
| title_full_unstemmed | Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks |
| title_short | Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks |
| title_sort | weigh in motion method based on modular sensor system and axle recognition with neural networks |
| topic | axle recognition modular system transformer gated recurrent unit weigh-in-motion |
| url | https://www.mdpi.com/2076-3417/15/2/614 |
| work_keys_str_mv | AT xiaoyongliu weighinmotionmethodbasedonmodularsensorsystemandaxlerecognitionwithneuralnetworks AT zhiyongyang weighinmotionmethodbasedonmodularsensorsystemandaxlerecognitionwithneuralnetworks AT bowenshi weighinmotionmethodbasedonmodularsensorsystemandaxlerecognitionwithneuralnetworks |