交通信息与安全2017，Vol.35Issue(5)：62-69,8.DOI:10.3963/j.issn.1674-4861.2017.05.008
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
摘要
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 than 20％,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.关键词
城市交通/动态波动性/ARIMA-GARCH模型/短时客流/对称性Key words
urban traffic/dynamic volatility/ARIMA-GARCH model/short-term passenger flow/symmetry分类
交通工程引用本文复制引用
段金肖,丁川,鹿应荣,马晓磊..考虑动态波动性的轨道交通站点短时客流预测方法[J].交通信息与安全,2017,35(5):62-69,8.基金项目
国家自然科学基金项目(71503018,U1564212)资助 （71503018,U1564212）
