考虑动态波动性的轨道交通站点短时客流预测方法

A Prediction Approach of Short-term Passenger Flow of Rail Transit Considering Dynamic Volatility

轨道交通站点客流预测研究缺乏对短时客流动态波动性的考虑,不能预测短时客流区间。以北京市典型轨道交通站点为例开展实证,构建ARIMA-GARCH模型对误差项建模分析,拟合短时客流的随机波动特征。不同于以往的ARIMA-GARCH模型,研究还通过t分布揭示了客流的"尖峰后尾"效应,通过2种非对称GARCH模型识别了短时客流的非对称波动特征。模型结果表明,相比传统ARIMA模型,ARIMA-GARCH混合模型降低了20%以上的客流平均置信区间长度(MPIL),同时提高了1%左右的置信区间覆盖率(PICP);周内客流波动性大于周末客流,而非高峰时段的客流不具有波动性。值得指出的是ARIMA-GARCH模型没有明显降低客流预测的平均绝对误差,尽管如此,混合模型可以在保证客流单点预测的前提下,准确地预测地铁客流区间。

Previous methods on forecasting passenger flow of rail transit lacks consideration of dynamic volatility,and cannot predict the range of short-term passenger flow.Taking typical rail transit stations in Beijing as a case study,an ARIMA-GARCH model is established to simulate the prediction interval(PI),and fit the stochastic volatility of shortterm passenger flow.The effect of 'sharp peak and heavy tail' is analyzed by using t distribution.The asymmetry volatility effects are addressed by using T-GARCH and E-GARCH models.Results show that the integrated ARIMA-GARCH models can significantly reduce the mean prediction interval length(MPIL)in forecasting passenger flow by more than20%,and improve the prediction interval coverage probability(PICP)by about 1%.It is also found that volatility of passenger flow in weekdays is larger than weekends,while no evident volatility exists during non-peak hours.Note that,an ARIMA-GARCH model will not significantly reduce mean absolute prediction error(MAPE).However,the hybrid models can accurately forecast the range of passenger flow of rail transit under the premise of ensuring single-point forecasting.