摘要
2020年上半年,从居家隔离到复工复产,北京城市轨道交通客流呈现先突降后缓慢恢复的态势.本文首先对城市轨道交通客流进行时空特征分析,了解疫情影响下的整体客流趋势.通过数据分析,得出疫情大幅抑制了城市轨道交通出行需求,严重影响了人们的工作生活,同时疫情对工作日全天各时段均有较大影响,对办公类车站影响较大等结论.通过建立ARIMA预测模型,对城市轨道交通客流恢复情况进行预判.研究结果表明,ARIMA(1,1,1)模型可有效对客流恢复情况进行预测,预测误差率为2.9%,剔除恶劣高温天气对客运量的影响,预测误差率为1.98%,预测模型和结果可为疫情防控提供定量决策依据.
In the first half of 2020,from isolation at home to returning to work,the passenger flow of urban rail transit in Beijing shows a trend of sudden drop first and then a slow recovery.This paper first analyzes the temporal and spatial characteristics of urban rail transit passenger flow to understand the overall trend of passenger flow under the influence of COVID-19.Through data analysis,it is concluded that the epidemic situation has greatly inhibited the travel demand of urban rail transit and seriously affected people s work and life.At the same time,the epidemic has a great impact on all time periods of the working day,and has a greater impact on stations in business districts.The ARIMA prediction model is employed to predict the passenger flow recovery of urban rail transit.The results show that ARIMA(1,1,1)model can effectively predict the recovery of passenger flow,and the prediction error rate is 2.9%.Excluding the impact of severe high temperature weather on passenger volume,the prediction error rate is 1.98%.The prediction model and results can provide a quantitative decision-making basis for epidemic prevention and control.
作者
光志瑞
牛燕斌
石旭
吴雁军
陈冉
GUANG Zhirui;NIU Yanbin;SHI Xu;WU Yanjun;CHEN Ran(Technical Innovation Research Institute of Beijing Mass Transit Railway Operation Co.,Ltd.,Beijing 100044,China;Beijing Key Laboratory of Subway Operation Safety Technology,Beijing 100044,China;Beijing Infrastructure Investment Co.,Ltd.,Beijing 100101,China)
出处
《交通工程》
2022年第1期51-59,66,共10页
Journal of Transportation Engineering
关键词
新冠肺炎疫情
北京城市轨道交通
客运量
统计分析
客流预测
COVID-19 pandemic
Beijing urban rail transit
passenger capacity
statistical analysis
passenger flow forecast