METHODOLOGY FOR DETERMINING THE VALUE OF COMPLEXITY PARAMETER FOR EMERGENCY SITUATION DURING DRIVING OF THE TRAIN

Authors

DOI:

https://doi.org/10.15802/stp2014/33077

Keywords:

safety, emergency situation, locomotive crew, intelligent system

Abstract

Purpose. During development of intelligent control systems for locomotive there is a need in the evaluation of the current train situation in the terms of traffic safety. In order to estimate the probability of the development of various emergency situations in to the traffic accidents, it is necessary to determine their complexity. The purpose of this paper is to develop the methodology for determining the complexity of emergency situations during the locomotive operation. Methodology. To achieve this purpose the statistical material of traffic safety violations was accumulated. The causes of violations are divided into groups: technical factors, human factors and external influences. Using the theory of hybrid networks it was obtained a model that gives the output complexity parameter of the emergency situation. Network type: multilayer perceptron with hybrid neurons of the first layer and the sigmoid activation function. The methods of the probability theory were used for the analysis of the results. Findings. The approach to the formalization of manufacturing situations that can only be described linguistically was developed, that allowed to use them as input data to the model for emergency situation. It was established and proved that the exponent of complexity for emergency situation during driving the train is a random quantity and obeys to the normal distribution law. It was obtained the graph of the cumulative distribution function, which identified the areas for safe operation and an increased risk of accident. Originality. It was proposed theoretical basis for determining the complexity of emergency situations in the train work and received the maximum complexity value of emergency situations that can be admitted in the operating conditions. Practical value. Constant monitoring of this value allows not only respond to the threat of danger, but also getting it in numerical form and use it as one of the input parameters for the locomotive intelligent control system. The decision on further control actions will be based on it.

Author Biography

O. M. Horobchenko, Ukrainian State Academy of Railway Transport

Dep. «Operation and Maintenance of Rolling Stock»

References

Analiz stanu bezpeky rukhu poizdiv u lokomotyvnomu hospodarstvi Ukrainy za 2008 rik [Analysis of traffic safety in the locomotive sector ofUkraine in 2008]. Kyiv, Ukrzalіznysia Publ., 2009. 58 p.

Horobchenko O.M. Vyznachennia imovirnosti vynyknennia transportnoi podii v lokomotyvnomu hospodarstvi [The probability of traffic accident determination in the locomotive department]. Visnyk Dnipropetrovskoho natsionalnoho universytetu zaliznychnoho transportu imeni akademika V. Lazariana [Bulletin of Dnipropetrovsk National University of Railway Transport named after Academician V. Lazaryan], 2010, issue 35, pp.41-44.

Horobchenko O.M. Modeliuvannia vynyknennia neshtatnoi sytuatsii v erhatychnii systemi «lokomotyvna bryhada – poizd» [Simulation of emergency situation in ergatic system «locomotive crew – the train»]. Zbіrnyk naukovykh prats DonІZT [Proc. of Donetsk Institute of Railway Transport], 2014, issue 38, pp. 144-147.

Yerofeyev A.A., Polyakov A.O. Intellektualnyye sistemy upravleniya [Intelligent Control Systems],Saint Petersburg, SPb GTU Publ., 1999. 265 p.

Kameniev O.Yu. Problematyka pidkhodiv do doslidzhennia bezpeky vykorystannia erhatychnykh system keruvannia na zaliznychnomu transporti [Problems approaches to study the safety of ergodic control systems for railways]. Nauka ta prohres transportu. Visnyk Dnipropetrovskoho natsionalnoho universytetu zaliznychnoho transportuScience and Transport Progress. Bulletin of Dnipropetrovsk National University of Railway Transport, 2013, no. 44, pp. 7-16.

Kolesnikov A.V. Gibridnyye intellektualnyye sistemy. Teoriya i tekhnologiya razrabotki [Hybrid intelligent systems. Theory and technology of development].Saint Petersburg, SPb GTU Publ., 2001. 700 p.

Kruglov V.V., Dli M.I., Golubov R.Yu. Nechetkaya logika i iskusstvennyye neyronnyye seti [Fuzzy logic and artificial neural networks],Moscow, Mir Publ., 2004. 224 p.

Leman E. Proverka statisticheskikh gipotez [Testing of statistical hypotheses], Moskow, NaukaPubl., 1979. 408 p.

Makarenko L.M. Vplyv liudskoho chynnyka na bezpeku rukhu zaliznychnoho transportu [Influence of human factor on the safety of railway transportation]. Zaliznychnyi transport Ukrainy – Railway transport of Ukraine, 2010, no. 1, pp. 46-51.

Khaykin S. Neyronnyye seti. Polnyy kurs [Neural networks. Full course].Moscow, Vilyams Publ., 2006, 1104 p.

Tsaregorodtsev V.G. Konstruktivnyy algoritm sinteza struktury mnogosloynogo perseptrona [A constructive algorithm for the synthesis of multilayer perceptron structure]. Vestnik Kazakhskogo natsionalnogo universiteta imeni al-Farabi. Seriya «Matematika, mekhanіka, informatsiya» – Bulletin of Kazakh National University named after al-Farabi. Series «Mathematics, Mechanics, Information», 2008, no. 4 (59), part. 3, pp. 308-315.

Li-min JIA, Ping LI. The system architecture of Chinese railway intelligent transportation system. Proc. of the Eastern Asia Society for Transportation Studies,Beijing, 2007, vol. 5, pp. 1424-1432.

Pan Deng, Zheng Yingping, Zhang Chuansheng. On intelligent automatic train control of railway moving automatic block systems based on multi-agent systems. 29th Chinese Control Conference (CCC). Beijing, 2010, vol. 1, pp. 4471-4476.

Published

2014-12-10

How to Cite

Horobchenko, O. M. (2014). METHODOLOGY FOR DETERMINING THE VALUE OF COMPLEXITY PARAMETER FOR EMERGENCY SITUATION DURING DRIVING OF THE TRAIN. Science and Transport Progress, (6(54), 50–58. https://doi.org/10.15802/stp2014/33077

Issue

Section

OPERATION AND REPAIR OF TRANSPORT MEANS