Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning
Joint Authors
Wu, Jianjun
Zhu, Yu-Ting
Huang, Kang
Yang, Xin
Liu, Feng
Ziyou, Gao
Source
Journal of Advanced Transportation
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-02
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Energy-efficient train speed profile optimization problem in urban rail transit systems has attracted much attention in recent years because of the requirement of reducing operation cost and protecting the environment.
Traditional methods on this problem mainly focused on formulating kinematical equations to derive the speed profile and calculate the energy consumption, which caused the possible errors due to some assumptions used in the empirical equations.
To fill this gap, according to the actual speed and energy data collected from the real-world urban rail system, this paper proposes a data-driven model and integrated heuristic algorithm based on machine learning to determine the optimal speed profile with minimum energy consumption.
Firstly, a data-driven optimization model (DDOM) is proposed to describe the relationship between energy consumption and discrete speed profile processed from actual data.
Then, two typical machine learning algorithms, random forest regression (RFR) algorithm and support vector machine regression (SVR) algorithm, are used to identify the importance degree of velocity in the different positions of profile and calculate the traction energy consumption.
Results show that the calculation average error is less than 0.1 kwh, and the energy consumption can be reduced by about 2.84% in a case study of Beijing Changping Line.
American Psychological Association (APA)
Huang, Kang& Wu, Jianjun& Yang, Xin& Ziyou, Gao& Liu, Feng& Zhu, Yu-Ting. 2019. Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning. Journal of Advanced Transportation،Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1170084
Modern Language Association (MLA)
Huang, Kang…[et al.]. Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning. Journal of Advanced Transportation No. 2019 (2019), pp.1-17.
https://search.emarefa.net/detail/BIM-1170084
American Medical Association (AMA)
Huang, Kang& Wu, Jianjun& Yang, Xin& Ziyou, Gao& Liu, Feng& Zhu, Yu-Ting. Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning. Journal of Advanced Transportation. 2019. Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1170084
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1170084