| Title: | Predicting the probability of cargo theft for individual cases in railway transport |
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| Authors: | ID Augustyn, Lorenc (Author)
ID Kuźnar, Małgorzata (Author)
ID Lerher, Tone (Author)
ID Szkoda, Maciej (Author) |
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| Files: |  Lorenc-2020-Predicting_the_Probability_of_Carg.pdf (1,93 MB)
MD5: 2A706C42C65010E417D1F97469B9D995
 https://doi.org/10.17559/TV-20190320194915
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FL - Faculty of Logistic
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| Abstract: | In the heavy industry, the value of cargo transported by rail is very high. Due to high value, poor security and volume of rail transport, the theft cases are often. The main problem of securing rail transport is predicting the location of a high probability of risk. Because of this,the aim of the presented research was to predict the highest probability of rail cargo theft for areas. It is important to prevent theft cases by better securing the railway lines. To solve that problem the authors' model was developed. The model uses information about past transport cases for the learning process of Artificial Neural Networks (ANN) and Machine Learning (ML).The ANN predicted the probability for 94.7% of the cases of theft and the Machine Learning identified 100% of the cases. This method can be used to develop a support system for securing the rail infrastructure. |
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| Keywords: | rail transport security, supply chain disruption, drones, security support systems, cargo theft, predicting, logistics, artificial neural network, drone monitoring, machine learning |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 14.06.2020 |
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| Publisher: | Strojarski fakultet, Elektrotehnički fakultet, Građevinski fakultet |
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| Year of publishing: | 2020 |
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| Number of pages: | Str. 773-780 |
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| Numbering: | Letn. 27, št. 3 |
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| PID: | 20.500.12556/DKUM-91694  |
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| UDC: | 656.2 |
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| ISSN on article: | 1848-6339 |
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| COBISS.SI-ID: | 25129987 |
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| DOI: | 10.17559/TV-20190320194915 |
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| Publication date in DKUM: | 28.01.2025 |
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| Views: | 113 |
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| Downloads: | 8 |
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| Metadata: |    |
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| Categories: | Misc.
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