苏州地铁客流波动特性分析

为研究轨道交通客流的波动性,提出使用SARIMA+GARCH这一随机结构作为轨道交通客流的综合时间序列模型。在这个随机结构中,SARIMA模型描述客流时间序列的一阶状态,即均值特征;GARCH模型获得客流时间序列的二阶状态,即条件异方差特征。采用苏州地铁全网客流数据作为分析实例,对5 min、15 min和1 h汇集度的工作日和休息日客流共6组客流数据进行波动性建模、预测与分析,结果表明,SARIMA+GARCH模型具有较好的预测性能。基于各组客流数据的分析结果,分别对工作日与休息日以及不同时间汇集度之间的客流波动特性进行对比,结果表明:休息日客流的波动性强于工作日客流;时间汇集度小的情况下,客流的波动性会更强。

Characteristics of Passenger Flow Volatility of Suzhou Railway

To study the volatility of passenger flow,the random structure of the SARIMA + GARCH model as a comprehensive time-series model for railway passenger flow is proposed.In this structure,the SARIMA model describes the first-order state,i.e.the mean feature,and the GARCH model obtains the second-order state,i.e.the conditional heteroskedasticity.Taking the passenger data of Suzhou railway as an example,the volatility of passenger flow in 5 min,15 min,and 1 h time intervals and rest days is modeled,forecasted,and analyzed.It is found that the SARIMA + GARCH model can provide a good prediction performance.Based on the analysis results of each group,the passenger flow volatility between the working and rest days,as well as between different time intervals,is compared.The results show that the volatility of passenger flow on rest days is stronger than that on working days.At the same time,when the time interval is shorter,the volatility of passenger flow is stronger.