Analysis of energy consumption reduction in metro systems using rollingstop-skipping patterns

S. Yang, Jianjun Wu (Corresponding author), Xin Yang, F. Liao, Daqing Li, Yun Wei

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Energy-efficient operations optimization has attracted much attention recently to reduce the energy consumption and the associated costs of metro systems. Compared with all-stop patterns, stop-skipping patterns potentially lead to decreasing energy consumption and increasing train loading utilization. This paper develops an optimization-based approach to design energy-efficient metro timetables and speed profiles with a stop-skipping pattern based on passenger smart-card data. First, we develop an algorithm to generate: a set of likely to be skipped stations is identified according to historical travel records, and a heuristic rule is adopted to select the specific skipped station. Second, we reformulate the energy-efficient timetabling optimization problem as a convex quadratic programming problem and develop a solution algorithm to determine the optimized timetables and speed profiles. A numerical example is conducted using the real-world passenger data and the train operational data of a metro line in Beijing. The results show that the developed approach reduces energy consumption by 15.39% and increases the loading volume per train by 3.56 person/min in comparison with the current timetable.
LanguageEnglish
Pages129-142
JournalComputers & Industrial Engineering
Volume127
DOIs
StatePublished - 1 Jan 2019

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Energy utilization
Smart cards
Quadratic programming
Costs

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Yang, S. ; Wu, Jianjun ; Yang, Xin ; Liao, F. ; Li, Daqing ; Wei, Yun. / Analysis of energy consumption reduction in metro systems using rollingstop-skipping patterns. In: Computers & Industrial Engineering. 2019 ; Vol. 127. pp. 129-142
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abstract = "Energy-efficient operations optimization has attracted much attention recently to reduce the energy consumption and the associated costs of metro systems. Compared with all-stop patterns, stop-skipping patterns potentially lead to decreasing energy consumption and increasing train loading utilization. This paper develops an optimization-based approach to design energy-efficient metro timetables and speed profiles with a stop-skipping pattern based on passenger smart-card data. First, we develop an algorithm to generate: a set of likely to be skipped stations is identified according to historical travel records, and a heuristic rule is adopted to select the specific skipped station. Second, we reformulate the energy-efficient timetabling optimization problem as a convex quadratic programming problem and develop a solution algorithm to determine the optimized timetables and speed profiles. A numerical example is conducted using the real-world passenger data and the train operational data of a metro line in Beijing. The results show that the developed approach reduces energy consumption by 15.39{\%} and increases the loading volume per train by 3.56 person/min in comparison with the current timetable.",
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Analysis of energy consumption reduction in metro systems using rollingstop-skipping patterns. / Yang, S.; Wu, Jianjun (Corresponding author); Yang, Xin ; Liao, F.; Li, Daqing ; Wei, Yun.

In: Computers & Industrial Engineering, Vol. 127, 01.01.2019, p. 129-142.

Research output: Contribution to journalArticleAcademicpeer-review

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AB - Energy-efficient operations optimization has attracted much attention recently to reduce the energy consumption and the associated costs of metro systems. Compared with all-stop patterns, stop-skipping patterns potentially lead to decreasing energy consumption and increasing train loading utilization. This paper develops an optimization-based approach to design energy-efficient metro timetables and speed profiles with a stop-skipping pattern based on passenger smart-card data. First, we develop an algorithm to generate: a set of likely to be skipped stations is identified according to historical travel records, and a heuristic rule is adopted to select the specific skipped station. Second, we reformulate the energy-efficient timetabling optimization problem as a convex quadratic programming problem and develop a solution algorithm to determine the optimized timetables and speed profiles. A numerical example is conducted using the real-world passenger data and the train operational data of a metro line in Beijing. The results show that the developed approach reduces energy consumption by 15.39% and increases the loading volume per train by 3.56 person/min in comparison with the current timetable.

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