A Prediction Method of Passenger Alighting and Alighting Time in Urban Rail Transit

Abstract

The invention discloses a method for predicting the boarding and disembarking time of passengers in urban rail transit. The method includes: S1: collecting rail transit operation information to obtain a scatter diagram of the number of passengers boarding and disembarking and the time of passengers boarding and disembarking; S2: Fitting the scatter diagram to obtain passenger boarding and disembarking flow change curves, establishing a segmented function model for passenger boarding time prediction and a passenger disembarkation time prediction model; S3: considering passenger queuing behavior to optimize the model; S4 : adopt the least squares method to solve the optimized described model to obtain passenger boarding and alighting time real-time prediction model, the present invention establishes the piecewise function model of passenger boarding time prediction, and considers the impact of passenger queuing behavior on passenger boarding and alighting time , can more accurately predict passenger boarding and disembarking time, improve the operational efficiency of rail transit, and have guiding significance for the optimization of shortening passenger boarding and disembarking time.

Description

technical field [0001] The invention relates to the field of urban rail transit operation, and more specifically, to a method for predicting the time for passengers to get on and off in urban rail transit. Background technique [0002] Entering the 21st century, with the continuous improvement of my country's urbanization level, the urban population density will increase rapidly, and the resulting traffic congestion will become more and more serious, which will become one of the important factors restricting economic, social and cultural development. one. As an important part of public transportation, urban rail transit will become an effective transportation method to solve urban traffic congestion. Since urban rail transit has the characteristics of energy saving, land saving, large transport volume, all-weather, punctuality, pollution-free and safe, it can be concluded that the development of urban rail transit fully complies with the principle of sustainable development....

AI Technical Summary

Problems solved by technology

Traditional research methods only conduct qualitative research on the factors that affect passengers getting on and off the bus. Therefore, it is necessary to provide a prediction method that can accurately predict the time of passengers getting on and off the train during peak hours and non-peak hours, while considering passenger behavior The impact on passenger boarding and alighting time, which more accurately reflects the real-time prediction of passengers' boarding and alighting time under different passenger flow conditions

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