城市轨道交通站点客流不确定性研究

为提高城市轨道交通站点客流预测的可靠性,在分析客流不确定性影响因素的基础上,基于ARIMA-GARCH模型,依据南京地铁珠江路站点客流数据对客流不确定性进行建模和预测,并从预测置信区间和无效覆盖率两方面与传统的时间序列进行对比分析,研究结果表明,ARIMA-GARCH能够较好地拟合客流波动情况,为城市轨道交通运营与管理提供理论依据。

A Study on Uncertainty of Urban Rail Transit Passenger Flow

Urban rail transit passenger flow influenced by many factors has the significant fluctuation, in order to improve the reliability when predicting the passenger flow of urban rail transit, the paper firstly analyses the random factors, then use passenger flow data collected in Zhuangjianglu Station in Nanjing Metro to model and forecast the passenger flow uncertainty based on ARIMA-GARCH Model. From the view of the predictive confidence interval and kickoff percentage which are compared to that of the traditional time series, research results show that the ARIMA-GARCH model can fit the fluctuations of passenger flow more. So ARIMA-GARCH could lay a more reliable foundation for the passenger flow uncertainty research and also provide a theoretical basis for the operation and management of urban rail transit.