Timetable optimization method based on regenerated energy utilization and passenger travel time
The invention discloses a timetable optimization method based on regenerated energy utilization and passenger travel time, and belongs to the technical field of rail transit. Comprising the following steps: S1, measuring utilization of regenerated energy based on train motor power curve overlapping;...
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creator | SHAO HANCONG MAO HONGYE LI YONGYI |
description | The invention discloses a timetable optimization method based on regenerated energy utilization and passenger travel time, and belongs to the technical field of rail transit. Comprising the following steps: S1, measuring utilization of regenerated energy based on train motor power curve overlapping; s2, measuring the influence of timetable optimization on the passengers based on the average travel time of the passengers; s3, establishing a mathematical model of a timetable based on the data in S1 and S2: establishing a dual-objective optimization model of minimum total energy consumption of the system and minimum average travel time of passengers by taking station dwell time as a decision variable; and S4, associating and solving the dual-objective optimization model by adopting an NSGA-II algorithm, designing crossover and mutation operators according to composition structure characteristics of the train timetable, and solving an optimal Pareto solution set. According to the method, the energy-saving optimiz |
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Comprising the following steps: S1, measuring utilization of regenerated energy based on train motor power curve overlapping; s2, measuring the influence of timetable optimization on the passengers based on the average travel time of the passengers; s3, establishing a mathematical model of a timetable based on the data in S1 and S2: establishing a dual-objective optimization model of minimum total energy consumption of the system and minimum average travel time of passengers by taking station dwell time as a decision variable; and S4, associating and solving the dual-objective optimization model by adopting an NSGA-II algorithm, designing crossover and mutation operators according to composition structure characteristics of the train timetable, and solving an optimal Pareto solution set. 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Comprising the following steps: S1, measuring utilization of regenerated energy based on train motor power curve overlapping; s2, measuring the influence of timetable optimization on the passengers based on the average travel time of the passengers; s3, establishing a mathematical model of a timetable based on the data in S1 and S2: establishing a dual-objective optimization model of minimum total energy consumption of the system and minimum average travel time of passengers by taking station dwell time as a decision variable; and S4, associating and solving the dual-objective optimization model by adopting an NSGA-II algorithm, designing crossover and mutation operators according to composition structure characteristics of the train timetable, and solving an optimal Pareto solution set. 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Comprising the following steps: S1, measuring utilization of regenerated energy based on train motor power curve overlapping; s2, measuring the influence of timetable optimization on the passengers based on the average travel time of the passengers; s3, establishing a mathematical model of a timetable based on the data in S1 and S2: establishing a dual-objective optimization model of minimum total energy consumption of the system and minimum average travel time of passengers by taking station dwell time as a decision variable; and S4, associating and solving the dual-objective optimization model by adopting an NSGA-II algorithm, designing crossover and mutation operators according to composition structure characteristics of the train timetable, and solving an optimal Pareto solution set. According to the method, the energy-saving optimiz</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
title | Timetable optimization method based on regenerated energy utilization and passenger travel time |
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