考虑动态波动性的轨道交通站点短时客流预测方法.docVIP

考虑动态波动性的轨道交通站点短时客流预测方法 段金肖 丁川 鹿应荣 马晓磊 北京航空航天大学交通科学与工程学院 北京航空航天大学车路协同与安全控制北京市重点实验室 X 关注成功！ 加关注后您将方便地在 我的关注中得到本文献的被引频次变化的通知！ 新浪微博 腾讯微博 人人网 开心网 豆瓣网 网易微博 摘????要： 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。 关键词： 城市交通; 动态波动性; ARIMA-GARCH模型; 短时客流; 对称性; 作者简介：段金肖 (1994-) , 硕士研究生.研究方向:交通行为建模与分析.E-mail:duanjinxiao@ 作者简介：丁川 (1986-) , 博士, 讲师.研究方向:交通行为建模与分析.E-mail:cding@ 收稿日期：2016-12-16 基金：国家自然科学基金项目 U1564212) 资助 A Prediction Approach of Short-term Passenger Flow of Rail Transit Considering Dynamic Volatility DUAN Jinxiao DING Chuan LU Yingrong MA Xiaolei School of Transportation Science and Engineering, Beihang Universtiy; Abstract： 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)