Analysis of energy consumption reduction in metro systems using rolling stop-skipping patterns. (January 2019)

• Record Type: Journal Article
• Authors: Yang, Songpo; Wu, Jianjun; Yang, Xin; Liao, Feixiong; Li, Daqing; Wei, Yun
• Is Part Of: Computers & industrial engineering. Volume 127(2019)
• Page Start: 129
• Page End: 142
• Publication Date: 2019-01
• Subjects: Timetable optimization -- Stop-skipping pattern -- Energy consumption -- Quadratic programming; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.11.048
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A rolling two-stage strategy is developed for determining the stop-skipping patterns. A strictly convex quadratic programming is formulated using Taylor Approximation. An efficient solution algorithm is devised to solve the large-scale problem in short CPU times. The approach reduces energy consumption by 15.39% and increases train capacity by 3.56 person/min. 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.