Study on subway passenger flow forecast based on time series analysis method

Abstract

In the complex system of urban rail transit, passenger flow forecast is an essential basis for rail transit network planning, rail station scale construction, and rail transit operation management. Urban rail transit passenger flow characteristics embody not only the periodicity and tendency but also the mutagenicity and particularity. Despite the time series analysis method is efficient and applicable, there are serious challenges associated with producing reliable and high-quality forecasts. When it comes to the mutagenicity and particularity of time series in passenger flow especially, it fails to live up to expectations. To tackle these challenges, a time series prediction model based on automatic machine learning is proposed, which can interpret the trend, periodicity, and holiday effects of subway passenger flow. In this paper, the Prophet model is constructed to verify the robustness and accuracy of the passenger flow time series forecast, drawing on the daily time series of passenger flow in the Beijing metro Guomao Station. Our founding indicates that the MAPE of the prediction reaches 5%. The comparison illustrates that the accuracy of the Prophet model is superior to that of the ARIMA and SARIMA models.