Driving Strategy of Heavy Haul Train based on Support Vector Regression

Shuo Yang, Xiaofeng Yang, Zhengnan Lin

2020

Abstract

In order to reduce the labor intensity of heavy-haul train drivers in the downgrade section and near the split phase area, the paper analyzes the factors that affect the driving strategy of heavy-haul train in accordance with the manual driving strategy. In this paper, A control model of heavy-haul train electric braking force based on support vector regression (SVR) is proposed to control the electric braking force. With the manual driving records used as training data, Electric braking force and other information are extracted as output results and features to train the control model. By trial and error, parameters of the control model are adjusted to optimize the model. The results show that the control model in this paper is close to the manual driving in the same situation, which is positive for reducing the labor intensity of drivers in heavy-haul railway.

Paper Citation

in Harvard Style

Yang S., Yang X. and Lin Z. (2020). Driving Strategy of Heavy Haul Train based on Support Vector Regression.In Proceedings of the International Symposium on Frontiers of Intelligent Transport System - Volume 1: FITS, ISBN 978-989-758-465-7, pages 70-74. DOI: 10.5220/0010121200700074

in Bibtex Style

@conference{fits20,
author={Shuo Yang and Xiaofeng Yang and Zhengnan Lin},
title={Driving Strategy of Heavy Haul Train based on Support Vector Regression},
booktitle={Proceedings of the International Symposium on Frontiers of Intelligent Transport System - Volume 1: FITS,},
year={2020},
pages={70-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010121200700074},
isbn={978-989-758-465-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Symposium on Frontiers of Intelligent Transport System - Volume 1: FITS,
TI - Driving Strategy of Heavy Haul Train based on Support Vector Regression
SN - 978-989-758-465-7
AU - Yang S.
AU - Yang X.
AU - Lin Z.
PY - 2020
SP - 70
EP - 74
DO - 10.5220/0010121200700074