Scipediacontent: Scipediacontent moved page Draft Content 655827139 to Wu et al 2019e

2021-02-12T13:37:17Z

Scipediacontent moved page Draft Content 655827139 to Wu et al 2019e

Scipediacontent: Created page with " == Abstract == Energy-efficient train speed profile optimization problem in urban rail transit systems has attracted much attention in recent years because of the requiremen..."

2021-02-12T13:37:13Z

Created page with " == Abstract == Energy-efficient train speed profile optimization problem in urban rail transit systems has attracted much attention in recent years because of the requiremen..."

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== 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.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_655827139-beopen376-3149-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://downloads.hindawi.com/journals/jat/2019/7258986.pdf http://downloads.hindawi.com/journals/jat/2019/7258986.pdf] under the license https://creativecommons.org/licenses/by

* [http://dx.doi.org/10.1155/2019/7258986 http://dx.doi.org/10.1155/2019/7258986] under the license cc-by

* [http://downloads.hindawi.com/journals/jat/2019/7258986.pdf http://downloads.hindawi.com/journals/jat/2019/7258986.pdf],
: [http://downloads.hindawi.com/journals/jat/2019/7258986.xml http://downloads.hindawi.com/journals/jat/2019/7258986.xml],
: [http://dx.doi.org/10.1155/2019/7258986 http://dx.doi.org/10.1155/2019/7258986] under the license http://creativecommons.org/licenses/by/4.0

* [http://dx.doi.org/10.1155/2019/7258986 http://dx.doi.org/10.1155/2019/7258986],
: [https://doaj.org/toc/0197-6729 https://doaj.org/toc/0197-6729],
: [https://doaj.org/toc/2042-3195 https://doaj.org/toc/2042-3195] under the license http://creativecommons.org/licenses/by/4.0/

* [https://www.hindawi.com/journals/jat/2019/7258986 https://www.hindawi.com/journals/jat/2019/7258986],
: [http://downloads.hindawi.com/journals/jat/2019/7258986.pdf http://downloads.hindawi.com/journals/jat/2019/7258986.pdf],
: [https://academic.microsoft.com/#/detail/2942910330 https://academic.microsoft.com/#/detail/2942910330]

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Wu et al 2019e - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 655827139 to Wu et al 2019e

Scipediacontent: Created page with " == Abstract == Energy-efficient train speed profile optimization problem in urban rail transit systems has attracted much attention in recent years because of the requiremen..."