Method for forecasting monthly urban rail transit passenger flow through ARIMA model based on seasonal index

Abstract

The invention discloses a method for forecasting monthly urban rail transit passenger flow through an ARIMA model based on a seasonal index. The method comprises the steps that original monthly passenger flow sample data is selected, the seasonal index of each month in a year is obtained through calculation according to a direct average seasonal index method, seasonal adjustment is conducted on an original monthly passenger flow sequence in samples by means of the seasonal index, monthly passenger flow sequence data subjected to conversion is subjected to stabilization processing, model identification and parameter estimation and inspection in sequence so as to construct the proper ARIMA model, and finally, a forecast result output by the model is subjected to reverse seasonal adjustment, that is to say, a final monthly passenger flow forecast value is obtained. The method can be used for improving the accuracy of the monthly urban rail transit passenger flow forecast and providing reliable data support for large production plans such as adjustment of a monthly vehicle maintenance plan, a serviceable car allocation plan and a daily transportation plan.

Description

technical field [0001] The invention relates to the technical field of passenger flow forecasting of urban rail transit, in particular to a method for predicting monthly passenger flow of urban rail transit based on an ARIMA model of a seasonal index. Background technique [0002] With the continuous construction and use of new lines in major and medium-sized cities in my country and the gradual formation of rail transit networks, people can reach travel destinations more conveniently and quickly, attracting more passengers to choose urban rail transit as a travel tool. This change brings new opportunities and challenges to the operation and management of rail transit. In the previous research on short-term passenger flow forecasting, they mainly focused on short-term passenger flow forecasting in units of days or hours, and there were few studies on short-term passenger flow forecasting with a longer time span such as monthly. However, in the actual operation of urban rail t...

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Problems solved by technology

In the previous research on short-term passenger flow forecasting, they mainly focused on short-term passenger flow forecasting in units of days or hours, and there were few studies on short-term passenger flow forecasting with a longer time span such as monthly. However, in the actual operation of urban rail transit, vehicles Large-scale production plans such as monthly maintenance plans, vehicle deployment plans, and adjustments to daily transportation plans need to refer to the results of monthly passenger flow forecasts. Therefore, it is necessary to conduct more in-depth research on urban rail transit monthly passenger flow forecasting technology

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