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Volume 55 Issue 1
Jan.  2020
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Journal of Southwest Jiaotong University  > 2020  >  55(1): 41-51.
LI Jie, PENG Qiyuan, YANG Yuxiang. Passenger Flow Prediction for Guangzhou-Zhuhai Intercity Railway Based on SARIMA Model[J]. Journal of Southwest Jiaotong University, 2020, 55(1): 41-51. doi: 10.3969/j.issn.0258-2724.20180617
Citation: LI Jie, PENG Qiyuan, YANG Yuxiang. Passenger Flow Prediction for Guangzhou-Zhuhai Intercity Railway Based on SARIMA Model[J]. Journal of Southwest Jiaotong University, 2020, 55(1): 41-51. doi: 10.3969/j.issn.0258-2724.20180617
shu

Passenger Flow Prediction for Guangzhou-Zhuhai Intercity Railway Based on SARIMA Model

doi: 10.3969/j.issn.0258-2724.20180617
  • Received Date: 10 Aug 2018
  • Rev Recd Date: 10 Nov 2018
    • Available Online: 27 Nov 2019
  • Publish Date: 01 Feb 2020
    Abstract
  • To achieve the short-term prediction on the railway passenger flow and analyze the influence of prediction step on prediction accuracy, firstly, the characteristics and variation of passenger flow for Guangzhou-Zhuhai intercity railway were analyzed. Then, considering the passenger flow characteristics, a prediction model based on the seasonal autoregressive integrated moving average (SARIMA) was built with the Statsmodels module in Python. Next, the model performance was validated on different prediction steps. The conclusion shows that when the prediction step is 1, the mean absolute percentage error (MAPE) for Guangzhou South station, Xiaolan station and Zhuhai station is 3.97%, 5.83%, and 5.43%, respectively; when the prediction step increases to 2, the MAPE shows an increase trend, which is 5.31%, 6.79%, and 7.62% for Guangzhou South station, Xiaolan station and Zhuhai station, respectively; when the prediction step exceeds 2, the MAPE is stable. In addition, comparative results with other passenger flow prediction methods, i.e., random forest (RF), support vector machine (SVM), gradient boosting (GB), and K-nearest neighbor (KNN) demonstrate that when the prediction step is 1, the SARIMA model performs slightly better; when the prediction step exceeds 2, the MAPE of RF, SVM, GB, and KNN increases dramatically, amounting several times that of the SARIMA model. Finally, the experiment results show that the SARIMA model can achieve a better performance than other models in terms of the multi-step prediction for passenger flow time series.

     

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