Applying Machine Learning to Preselective Weighing of Moving Vehicles
The paper presents the general characteristics of weighing systems for vehicles in motion. A number of problems and constraints that accompany these systems to ensure adequate accuracy in the operation of these systems are pointed out. The efficient operation of WIM systems is also related to the pr...
| প্রধান লেখক: | , , , , , |
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| বিন্যাস: | প্রবন্ধ |
| ভাষা: | English |
| প্রকাশিত: |
MDPI AG
2025-02-01
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| মালা: | Applied Sciences |
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://www.mdpi.com/2076-3417/15/4/1743 |
| _version_ | 1826583384021794816 |
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| author | Paweł Kowaleczko Tomasz Kamiński Mariusz Rychlicki Zbigniew Kasprzyk Marek Stawowy Jacek Trzeszkowski |
| author_facet | Paweł Kowaleczko Tomasz Kamiński Mariusz Rychlicki Zbigniew Kasprzyk Marek Stawowy Jacek Trzeszkowski |
| author_sort | Paweł Kowaleczko |
| collection | DOAJ |
| description | The paper presents the general characteristics of weighing systems for vehicles in motion. A number of problems and constraints that accompany these systems to ensure adequate accuracy in the operation of these systems are pointed out. The efficient operation of WIM systems is also related to the proper preselection of vehicles for weighing in motion. The next part of the paper presents the basic classification and characteristics of machine learning algorithms, as well as examples of applications and implementations of these algorithms in various industries. The paper presents a model based on the XGBoost algorithm for estimating the weight of vehicles in motion, taking into account key characteristics of vehicles. The model was tested on large datasets from two locations in Poland, achieving high accuracy rates. The results indicate the model’s potential in optimizing preselection systems, allowing for the effective identification of overloaded vehicles. Future work will focus on testing the model at other locations to verify its scalability and operational efficiency. |
| first_indexed | 2025-03-14T15:21:00Z |
| format | Article |
| id | doaj.art-eb20ec41921b422a9e88bc28ef6b51c7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2025-03-14T15:21:00Z |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-eb20ec41921b422a9e88bc28ef6b51c72025-02-25T13:08:23ZengMDPI AGApplied Sciences2076-34172025-02-01154174310.3390/app15041743Applying Machine Learning to Preselective Weighing of Moving VehiclesPaweł Kowaleczko0Tomasz Kamiński1Mariusz Rychlicki2Zbigniew Kasprzyk3Marek Stawowy4Jacek Trzeszkowski5Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, Pl. Politechniki 1 St., 00-661 Warsaw, PolandRoad and Bridge Research Institute, Instytutowa 1 St., 03-302 Warsaw, PolandFaculty of Transport, Warsaw University of Technology, Koszykowa 75 St., 00-662 Warsaw, PolandFaculty of Transport, Warsaw University of Technology, Koszykowa 75 St., 00-662 Warsaw, PolandFaculty of Transport, Warsaw University of Technology, Koszykowa 75 St., 00-662 Warsaw, PolandCypher Jacek Trzeszkowski, gen. Józefa Hallera 18 St., 05-091 Ząbki, PolandThe paper presents the general characteristics of weighing systems for vehicles in motion. A number of problems and constraints that accompany these systems to ensure adequate accuracy in the operation of these systems are pointed out. The efficient operation of WIM systems is also related to the proper preselection of vehicles for weighing in motion. The next part of the paper presents the basic classification and characteristics of machine learning algorithms, as well as examples of applications and implementations of these algorithms in various industries. The paper presents a model based on the XGBoost algorithm for estimating the weight of vehicles in motion, taking into account key characteristics of vehicles. The model was tested on large datasets from two locations in Poland, achieving high accuracy rates. The results indicate the model’s potential in optimizing preselection systems, allowing for the effective identification of overloaded vehicles. Future work will focus on testing the model at other locations to verify its scalability and operational efficiency.https://www.mdpi.com/2076-3417/15/4/1743weigh-in-motion systemmachine learning algorithmspreselective weighing of vehicles in motion |
| spellingShingle | Paweł Kowaleczko Tomasz Kamiński Mariusz Rychlicki Zbigniew Kasprzyk Marek Stawowy Jacek Trzeszkowski Applying Machine Learning to Preselective Weighing of Moving Vehicles Applied Sciences weigh-in-motion system machine learning algorithms preselective weighing of vehicles in motion |
| title | Applying Machine Learning to Preselective Weighing of Moving Vehicles |
| title_full | Applying Machine Learning to Preselective Weighing of Moving Vehicles |
| title_fullStr | Applying Machine Learning to Preselective Weighing of Moving Vehicles |
| title_full_unstemmed | Applying Machine Learning to Preselective Weighing of Moving Vehicles |
| title_short | Applying Machine Learning to Preselective Weighing of Moving Vehicles |
| title_sort | applying machine learning to preselective weighing of moving vehicles |
| topic | weigh-in-motion system machine learning algorithms preselective weighing of vehicles in motion |
| url | https://www.mdpi.com/2076-3417/15/4/1743 |
| work_keys_str_mv | AT pawełkowaleczko applyingmachinelearningtopreselectiveweighingofmovingvehicles AT tomaszkaminski applyingmachinelearningtopreselectiveweighingofmovingvehicles AT mariuszrychlicki applyingmachinelearningtopreselectiveweighingofmovingvehicles AT zbigniewkasprzyk applyingmachinelearningtopreselectiveweighingofmovingvehicles AT marekstawowy applyingmachinelearningtopreselectiveweighingofmovingvehicles AT jacektrzeszkowski applyingmachinelearningtopreselectiveweighingofmovingvehicles |