Article Details

Abstract

Firstly, the paper analyzes the patterns and trends of urban rail transit passenger volume, and selects the Auto-Regression Moving Average (ARMA) model to predict passenger volume. Secondly, to address the issue of reduced accuracy in predicting passenger volume for the next three days or more using the ARMA model, a rolling prediction approach is adopted for optimization. Finally, aiming at the issue of insufficient predictive performance of ARMA rolling prediction for turning points in passenger volume changes, relevant factors affecting changes in passenger volume are analyzed and regression analysis methods are adopted for correction. The results show that the combination method of ARMA rolling prediction and regression correction, compared with the pure ARMA method, not only retains the optimization of passenger volume prediction performance for the next three days or more, but also captures the impact of external factors on passenger volume changes. And it improves the accuracy of passenger flow prediction.

Keywords

passenger flow prediction; urban rail transit; time series; regression analysis

References

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About the authors

Liao Guiyu (1992-), female, Zhuang nationality, from Liuzhou, Guangxi, engineer, master's student, research direction: data analysis and mining, business intelligence.