Application of algorithmic models of machine learning to the freight transportation process
The results of the analysis of algorithmic models of machine learning application to the freight transportation process are given in this paper. Analysis of existing research allowed discovering a range of advantages in the application of computational intelligence in logistic systems, including inc...
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| Format: | Article |
| Language: | English |
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Lviv Polytechnic National University
2022-12-01
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| Series: | Transport Technologies |
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| Online Access: | https://science.lpnu.ua/tt/all-volumes-and-issues/volume-3-number-2-2022/application-algorithmic-models-machine-learning |
| _version_ | 1797855192121606144 |
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| author | Viktoriia Kotenko |
| author_facet | Viktoriia Kotenko |
| author_sort | Viktoriia Kotenko |
| collection | DOAJ |
| description | The results of the analysis of algorithmic models of machine learning application to the freight transportation process are given in this paper. Analysis of existing research allowed discovering a range of advantages in the application of computational intelligence in logistic systems, including increasing the accuracy of forecasting, reduction of transport costs, increasing the efficiency of cargo delivery, risks reduction, and search for key performance factors. In the research process, the main directions of application of algorithmic models of machine learning were determined. They are vehicle routing, choice of cargo type, transportation type and vehicle type; forecasting fuel consumption by vehicles, disruptions in transportation, transport costs, duration of the order fulfillment; evaluation of the rolling stock fleet and the efficiency of carrying out the transport task. Based on the researched publications, the most common algorithmic models of machine learning in freight transportation were identified, and their effectiveness was analyzed. Linear and logistic regression models are simple enough; however, they do not always provide high simulation results. Deep learning models are quite widely applied to all identified areas. Decision tree and random forest models often show the highest simulation performance. Models of k-nearest neighbors and support vectors should be used both in classification tasks, for example, in choosing the type of cargo and type of transportation, and for forecasting the fuel consumption and the duration of the transport process. |
| first_indexed | 2024-04-09T20:19:43Z |
| format | Article |
| id | doaj.art-5a85f3fc66134b10be003bd65c9e6e9b |
| institution | Directory Open Access Journal |
| issn | 2708-2199 2709-5223 |
| language | English |
| last_indexed | 2024-04-09T20:19:43Z |
| publishDate | 2022-12-01 |
| publisher | Lviv Polytechnic National University |
| record_format | Article |
| series | Transport Technologies |
| spelling | doaj.art-5a85f3fc66134b10be003bd65c9e6e9b2023-03-31T06:37:48ZengLviv Polytechnic National UniversityTransport Technologies2708-21992709-52232022-12-0132102110.23939/tt2022.02.010Application of algorithmic models of machine learning to the freight transportation processViktoriia Kotenko0https://orcid.org/0000-0002-0033-3302Vinnytsya National Technical UniversityThe results of the analysis of algorithmic models of machine learning application to the freight transportation process are given in this paper. Analysis of existing research allowed discovering a range of advantages in the application of computational intelligence in logistic systems, including increasing the accuracy of forecasting, reduction of transport costs, increasing the efficiency of cargo delivery, risks reduction, and search for key performance factors. In the research process, the main directions of application of algorithmic models of machine learning were determined. They are vehicle routing, choice of cargo type, transportation type and vehicle type; forecasting fuel consumption by vehicles, disruptions in transportation, transport costs, duration of the order fulfillment; evaluation of the rolling stock fleet and the efficiency of carrying out the transport task. Based on the researched publications, the most common algorithmic models of machine learning in freight transportation were identified, and their effectiveness was analyzed. Linear and logistic regression models are simple enough; however, they do not always provide high simulation results. Deep learning models are quite widely applied to all identified areas. Decision tree and random forest models often show the highest simulation performance. Models of k-nearest neighbors and support vectors should be used both in classification tasks, for example, in choosing the type of cargo and type of transportation, and for forecasting the fuel consumption and the duration of the transport process.https://science.lpnu.ua/tt/all-volumes-and-issues/volume-3-number-2-2022/application-algorithmic-models-machine-learningintellectual approachmachine learningalgorithmic models of machine learningfreight transportationcargo delivery |
| spellingShingle | Viktoriia Kotenko Application of algorithmic models of machine learning to the freight transportation process Transport Technologies intellectual approach machine learning algorithmic models of machine learning freight transportation cargo delivery |
| title | Application of algorithmic models of machine learning to the freight transportation process |
| title_full | Application of algorithmic models of machine learning to the freight transportation process |
| title_fullStr | Application of algorithmic models of machine learning to the freight transportation process |
| title_full_unstemmed | Application of algorithmic models of machine learning to the freight transportation process |
| title_short | Application of algorithmic models of machine learning to the freight transportation process |
| title_sort | application of algorithmic models of machine learning to the freight transportation process |
| topic | intellectual approach machine learning algorithmic models of machine learning freight transportation cargo delivery |
| url | https://science.lpnu.ua/tt/all-volumes-and-issues/volume-3-number-2-2022/application-algorithmic-models-machine-learning |
| work_keys_str_mv | AT viktoriiakotenko applicationofalgorithmicmodelsofmachinelearningtothefreighttransportationprocess |