Transport Workers Activities Analysis Using an Artificial Neural Network

P. 308–316.

Kulagin M., Sidorenko V.

This article describes modern methods of data processing regarding the task of assessing activities of transportation employees. The main purpose was to find dependencies in data and construct an algorithm for predicting the probability of transport safety violation by employee. The research was conducted for locomotive drivers. The following algorithms were used: neural networks, gradient boosting over decision trees and random forest. Based on the obtained results and drawn conclusions one can think of the perspective for the elaboration and introduction this work for practical use in railway industry, e.g. in “Russian Railways”.

Keywords: probability, machine learning, artificial intelligence, Neural Network, Gradient boosting, random forest

In book

Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18) Volume 2

Vol. 2. , Springer, 2019.