A passenger flow prediction method for urban rail transit under emergencies

Abstract

The invention discloses a method for forecasting the passenger volume of urban rail transit under emergencies. The method comprises steps of S1: acquiring urban rail transit physical network information, train running information, passenger volume data and emergency information to determine the passenger volume distribution and affected passenger volume data of urban rail transit; S2: constructing a set of alternative travel plans for passengers under emergencies, updating the affected passenger volume data after the passengers select each alternative travel plan; and S3: calculating the passenger volume of all stations of urban rail transit based on unaffected passenger volume data. The method fully considers the impact of emergencies on the operation of urban rail transit and considers the characteristics of passengers' travel behaviors under emergencies to improve the accuracy of forecasting passenger volume distribution of urban rail transit under emergencies. The method provides a theoretical basis to formulate an effective emergency plan and operation management measures, can effectively divert passengers stranded due to emergencies to protect passenger travel safety, and can improve the service level of the subway operation department.

Description

Technical field [0001] The present invention relates to the technical field of urban rail transit. More particularly, to urban rail transit passenger flow forecasting method under one kind of emergencies. Background technique [0002] With the rapid social and economic development, urban rail transportation for its large capacity, high efficiency, low energy consumption, command and controllability, and other characteristics has become an important way of big city traffic travel. In recent years, the rapid development of my country's urban rail transit, which in some big cities such as Beijing, Shanghai, Guangzhou and other rail transit network has entered into the operational phase. In the context into network operation, urban rail transit passenger traffic continued to increase. Due to urban rail transit traffic has large, complex network structure characteristics, into a network under conditions event of emergencies, due to the lack of effective passenger flow forecasting, ope...

AI Technical Summary

Problems solved by technology

Existing rail transit passenger flow forecasting methods roughly handle passenger travel choices under emergencies, fail to fully consider the diversity and possibility of travel plans, and fail to effectively predict passenger flow based on actual needs

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