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General Information
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2021 Vol.11(3): 75-79 ISSN: 2010-3697
DOI: 10.7763/IJMO.2021.V11.781

Analysis and Prediction of Trough Track Superelevation Based on Big Data

Jiabin Huang, Zhichao He, Chen Li, Hongru Fan, Yi Yin, and Yongjun Xie
Abstract—The detection of the safety status of urban rail transit is an important part of ensuring the operation of the rail. Using the data detected by the rail to analyze and predict the quality of the rail is very important for the research of rail inspection. Based on the existing research, this paper uses big data to analyze the collected superelevation data, and builds a trough-track superelevation big data prediction model based on the combination of the stochastic oscillation sequence gray model and ALO-Elman network to analyze the historical superelevation data and mine information about superelevation trends. In this paper, the average value of superelevation data of the equal-spaced groove track in a certain interval is collected for verification. The experimental results show that the method can reasonably predict the change trend of the ultra-elevation. The change trend is basically in line with the original data change trend, and the deviation is controlled to be small within range.

Index Terms—Antlion algorithm, Elman neural network, stochastic oscillation sequence grey Model, superelevation, urban traffic.

The authors are with Jinan University, China (e-mail: 978671072@qq.com, 978553915@qq.com, 446231303@qq.com, 1921350530@qq.com, 528463264@qq.com, 528463264@qq.com).


Cite: Jiabin Huang, Zhichao He, Chen Li, Hongru Fan, Yi Yin, and Yongjun Xie, "Analysis and Prediction of Trough Track Superelevation Based on Big Data," International Journal of Modeling and Optimization vol. 11, no. 3, pp. 75-79, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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