Subway departure strategy method and system based on genetic algorithm

Abstract

The invention discloses a subway departure strategy method and system based on a genetic algorithm. The method comprises the following steps: acquiring subway line information, subway line passenger information and subway station waiting passenger information, establishing a subway environment state matrix, establishing a subway departure decision, and acquiring an environment reward value corresponding to the departure decision at each time point according to the subway environment state matrix; encoding the departure decision at each time point into a departure gene of a genetic algorithm, establishing a departure decision individual comprising a plurality of departure genes of the genetic algorithm, and establishing a departure decision population comprising a plurality of departure decision individuals; randomly selecting one departure decision individual in the departure decision population as a departure decision parent, taking the remaining departure decision individuals as departure decision parents, and generating offspring departure decision individuals through random combination and reproduction of corresponding positions of genes; and taking the departure decision-making individual with the highest environment reward value as a final departure basis.

Description

technical field [0001] The invention relates to the field of subway control, in particular to a genetic algorithm-based subway departure strategy method and system. Background technique [0002] As an important part of public transportation in big cities, the subway passenger flow changes with people's life and work needs every day. There are obvious differences in the travel rules of working days and rest days. Moreover, the subway passenger flow is dynamic and time-varying, so it is required to adjust the departure density according to the actual situation. [0003] At present, the departure time and departure interval of the subway are fixed, and there are only slight adjustments during peak periods or rest days. The so-called peak period is also judged by the staff of the subway station based on the real-time passenger flow, which is subjective and lagging, which brings a bad experience to passengers riding in the peak period or on rest days. An effective solution is t...

AI Technical Summary

Problems solved by technology

The traditional method only calculates the inbound and outbound information of all passengers to predict, but does not predict the complete itinerary of passengers. Therefore, it is necessary to predict the departure density in a new way according to the subway operating environment.

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