Predicting the probability of cargo theft for individual cases in railway transport
Authors:
- Augustyn Lorenc,
- Małgorzata Kuźnar,
- Tone Lerher,
- Maciej Szkoda
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.
- Record ID
- CUT7c54538f6f8843e58f42b57c6885d661
- Publication categories
- ;
- Author
- Journal series
- Tehnicki Vjesnik-Technical Gazette, ISSN 1330-3651, e-ISSN 1848-6339
- Issue year
- 2020
- Vol
- 27
- No
- 3
- Pages
- 773-780
- Other elements of collation
- il. (w tym kolor.); Bibliografia (na s.) - 779-780; Bibliografia (liczba pozycji) - 25; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 27, No. 3
- Keywords in English
- artificial neural network, cargo theft, drone monitoring, machine learning, rail transport security, security support system, supply chain disruption
- DOI
- DOI:10.17559/TV-20190320194915 Opening in a new tab
- URL
- https://hrcak.srce.hr/239085 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 40
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT7c54538f6f8843e58f42b57c6885d661/
- URN
urn:pkr-prod:CUT7c54538f6f8843e58f42b57c6885d661
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.