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

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.