The subway’s short-term passenger flow prediction during Morning rush hour base on ARIMA —— using Hangzhou city as an example

Abstract

With the development of city’s population boost rapidly, people’s demand on public transport rising quickly, pushing great pressure to the whole public transport system in the city. In order to relieving urban traffic, public transport like subway has increased rapidly to face this situation. This article will collect passenger flow data during morning rush hour from Hang zhou during January 1-7, 2019, testing the stationarity of data by using Scatter plot, Sequence Diagram and Augmented Dickey-Fuller test (ADF) and using the ARIMA model for prediction. The result shows that through changing different parameters to build model, analysis data and comparing the result, researcher can choose the most accurate prediction model among all these models. Thefinal result shows that the model formed in this paper can make accurate prediction result of passenger flow, which can be used as a reference indicator in citizens’ route planning to help them avoid traffic jams.