Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/28099
Title: Discrete train speed profile optimization for urban rail transit: A data-driven model and integrated algorithms based on machine learning
Authors: Huang, K.
Wu, J.
Yang, X.
Gao, Z.
LIU, Feng 
Zhu, Y.
Issue Date: 2019
Source: Journal of advanced transportation, (Art N° 7258986)
Abstract: 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.
Notes: Wu, JJ (reprint author), Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China. Beijing Jiaotong Univ, Minist Transport, Key Lab Transport Ind Big Data Applicat Technol C, Beijing 100044, Peoples R China. 17114226@bjtu.edu.cn; jjwu1@bjtu.edu.cn; 11111047@bjtu.edu.cn; zygao@bjtu.edu.cn; feng.liu@uhasselt.be; 11114241@bjtu.edu.cn
Document URI: http://hdl.handle.net/1942/28099
ISSN: 0197-6729
e-ISSN: 2042-3195
DOI: 10.1155/2019/7258986
ISI #: 000473415100001
Rights: 2019 Kang Huang et al. Tis 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.
Category: A1
Type: Journal Contribution
Validations: ecoom 2020
Appears in Collections:Research publications

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