• ISSN 0258-2724
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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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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 11.8 %其他: 11.8 %其他: 0.8 %其他: 0.8 %Hyōgo: 0.1 %Hyōgo: 0.1 %上海: 2.6 %上海: 2.6 %东莞: 0.7 %东莞: 0.7 %临汾: 0.2 %临汾: 0.2 %乐山: 1.1 %乐山: 1.1 %佛山: 0.1 %佛山: 0.1 %兰州: 0.5 %兰州: 0.5 %内江: 0.4 %内江: 0.4 %北京: 4.4 %北京: 4.4 %十堰: 0.1 %十堰: 0.1 %南京: 2.3 %南京: 2.3 %南宁: 0.2 %南宁: 0.2 %合肥: 0.4 %合肥: 0.4 %咸阳: 0.1 %咸阳: 0.1 %哥伦布: 0.5 %哥伦布: 0.5 %唐山: 0.2 %唐山: 0.2 %嘉兴: 0.2 %嘉兴: 0.2 %埃德蒙顿: 0.1 %埃德蒙顿: 0.1 %大连: 0.1 %大连: 0.1 %天津: 0.8 %天津: 0.8 %宁德: 0.2 %宁德: 0.2 %宁波: 0.4 %宁波: 0.4 %安康: 0.6 %安康: 0.6 %宜春: 0.1 %宜春: 0.1 %宣城: 0.2 %宣城: 0.2 %常州: 0.1 %常州: 0.1 %平顶山: 0.1 %平顶山: 0.1 %广州: 0.4 %广州: 0.4 %延安: 0.1 %延安: 0.1 %张家口: 2.3 %张家口: 2.3 %怀化: 0.2 %怀化: 0.2 %意法半: 0.8 %意法半: 0.8 %成都: 3.2 %成都: 3.2 %扬州: 0.4 %扬州: 0.4 %揭阳: 0.4 %揭阳: 0.4 %昆明: 0.4 %昆明: 0.4 %曲靖: 0.1 %曲靖: 0.1 %杭州: 1.0 %杭州: 1.0 %格兰特县: 0.2 %格兰特县: 0.2 %桂林: 0.1 %桂林: 0.1 %武汉: 0.1 %武汉: 0.1 %江门: 0.5 %江门: 0.5 %沈阳: 0.8 %沈阳: 0.8 %洛阳: 1.1 %洛阳: 1.1 %深圳: 1.6 %深圳: 1.6 %清远: 0.1 %清远: 0.1 %温州: 0.1 %温州: 0.1 %湖州: 0.1 %湖州: 0.1 %漯河: 1.1 %漯河: 1.1 %澳门: 0.1 %澳门: 0.1 %焦作: 0.2 %焦作: 0.2 %盐城: 0.2 %盐城: 0.2 %石家庄: 5.0 %石家庄: 5.0 %福州: 0.2 %福州: 0.2 %绵阳: 0.2 %绵阳: 0.2 %罗马: 0.2 %罗马: 0.2 %芒廷维尤: 7.7 %芒廷维尤: 7.7 %芜湖: 0.2 %芜湖: 0.2 %芝加哥: 1.1 %芝加哥: 1.1 %苏州: 0.2 %苏州: 0.2 %衢州: 0.1 %衢州: 0.1 %襄阳: 0.1 %襄阳: 0.1 %西宁: 26.1 %西宁: 26.1 %西安: 1.3 %西安: 1.3 %贵阳: 0.4 %贵阳: 0.4 %运城: 0.6 %运城: 0.6 %邯郸: 0.1 %邯郸: 0.1 %郑州: 3.2 %郑州: 3.2 %重庆: 1.2 %重庆: 1.2 %长春: 0.2 %长春: 0.2 %长沙: 4.3 %长沙: 4.3 %阳泉: 0.2 %阳泉: 0.2 %青岛: 1.1 %青岛: 1.1 %驻马店: 0.1 %驻马店: 0.1 %黄石: 0.2 %黄石: 0.2 %其他其他Hyōgo上海东莞临汾乐山佛山兰州内江北京十堰南京南宁合肥咸阳哥伦布唐山嘉兴埃德蒙顿大连天津宁德宁波安康宜春宣城常州平顶山广州延安张家口怀化意法半成都扬州揭阳昆明曲靖杭州格兰特县桂林武汉江门沈阳洛阳深圳清远温州湖州漯河澳门焦作盐城石家庄福州绵阳罗马芒廷维尤芜湖芝加哥苏州衢州襄阳西宁西安贵阳运城邯郸郑州重庆长春长沙阳泉青岛驻马店黄石

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