基于AFC数据的大型活动期间城市轨道交通客流预测

准确预测大型活动期间城市轨道交通客流,是城市轨道交通管理与运营部门制定运输组织计划的重要依据,也是实现活动期间交通保障的关键.在分析大型活动期间城市轨道交通历史客流特征的基础上,针对活动期间的客流成分,分别构建活动客流与背景客流预测模型,以实现对未来大型活动期间城市轨道交通客流的预测.基于城市轨道交通自动检票系统(AFC)采集到的客流数据,分析大型活动期间的历史客流数据的变化规律,并依据其客流特征进行成分分解.针对活动客流,构建基于小波分解与重构的GM-ARIMA客流预测模型,针对背景客流则采用ARIMA模型与底特律法进行预测.基于广州地铁在2011—2014年广交会期间的历史AFC客流数据,对提出的方法进行验证.结果表明:该方法能够捕捉大型活动期间的客流特征,并可实现对大型活动期间城市轨道交通客流的预测.

Urban rail transit passenger flow forecasting for large special event based on AFC data

Accurately forecasting the urban rail transit (URT) passenger flow during the large special event is the foundation of preparing transport organization plan for the URT management and operation department,and also the key to guarantee the passenger transportation during the event.Based on the analyses of the URT history passenger flow during the event,two forecasting models for the two passenger flow components (event related and background passenger flow) are respectively proposed to realize the passenger flow forecasting.The characteristics of passenger flow is analyzed based on the data collected by Automated Fare Collection(AFC) system,and is decomposed into two components.A wavelet decomposed and reconstructed based GM-ARIMA forecasting model is proposed to forecast event-related passnger flow,and ARIMA model and Detroit method is used to forecast the background passenger flow.The proposed models are testified with the AFC data collected from Guangzhou Metro system from 2011 to 2014's China Canton Fair.The results show that the proposed models could capture the characteristics of the passenger flow during the event,which has good forecasting performances.