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Transport Workers Activities Analysis Using an Artificial Neural Network

  • Maskim Kulagin
  • Valentina Sidorenko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 875)

Abstract

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

Machine learning Neural network Artificial intelligence Probability Random forest Gradient boosting 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Russian University of Transport (MIIT)MoscowRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia

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