Tehnički vjesnik, Vol. 27 No. 3, 2020.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20190320194915
Predicting the Probability of Cargo Theft for Individual Cases in Railway Transport
Augustyn Lorenc
; Cracow University of Technology, Institute of Rail Vehicles, Al. Jana Pawla II 37, 31-86 Cracow, Poland
Małgorzata Kuźnar
; Cracow University of Technology, Institute of Rail Vehicles, Al. Jana Pawla II 37, 31-86 Cracow, Poland
Tone Lerher
; University of Maribor, Faculty of Logistics, Mariborskacesta 7, 3000 Celje, Slovenia
Maciej Szkoda
; Cracow University of Technology, Institute of Rail Vehicles, Al. Jana Pawla II 37, 31-86 Cracow, Poland
Sažetak
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.
Ključne riječi
artificial neural network; cargo theft; drone monitoring; machine learning; rail transport security; security support system; supply chain disruption
Hrčak ID:
239085
URI
Datum izdavanja:
14.6.2020.
Posjeta: 2.726 *