Short-term Passenger Flow Prediction of Urban Rail Transit based on ARIMA Model

Rui Sun

doi:10.54097/sxg7er77

Authors

Rui Sun

DOI:

https://doi.org/10.54097/sxg7er77

Keywords:

Urban rail transit; ARIMA model; passenger flow prediction.

Abstract

The urban economy is growing quickly, the city is getting bigger and the problem of traffic congestion is getting worse. As the primary means of transferring passengers, urban rail transit is an essential component of it. Therefore, developing urban rail transit is an important way to alleviate traffic congestion in large and medium-sized cities in the country. However, as rail transit grows, the number of lines grows daily and the volume of passengers increases quickly, putting strain on the urban transportation system to handle the increased volume of passengers. Consequently, it is now necessary to find a solution to the problem of how to quickly estimate future passenger flow based on past rail transit data, assist the operation management department in carrying out preventive work ahead of time and enable it to accomplish safe and orderly operation. Based on the Beijing Line 10 passenger flow, this study forecasts the passenger flow for the next few days by using the ARIMA model to uncover the travel rules hidden in the huge amount of concealed data. The forecast findings aid in the logical optimization of rail transportation, hence raising the level of service quality.

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