文章编号： 1672-5328（2020）03-0051-11

何凌晖，余庆，李玮峰，李健，杨东援
(同济大学道路与交通工程教育部重点实验室，上海201804)

摘要： 运用百度迁徙数据分析新型冠状病毒肺炎疫情影响下的城际交通运输需求特征。一方面，对 城际交通运输需求的时空特征进行分析，了解疫情影响下城际客流的整体趋势；另一方面，运用奇 异值分解算法对城际客流的时空OD矩阵进行分解降维，对城际交通运输的客流构成进行识别。研 究结果表明，疫情期间的城际交通运输包括两个阶段，节前返乡客流的趋势和规模与往年基本一 致，而节后返程客流的需求释放缓慢，呈现长时间、分批次且逐步涉及疫情地区人员的特征。疫情 影响下的城际交通运输需求包括四个主要类型：日常城际交通运输需求、节前返乡需求、节后返程 需求和节前错峰返乡需求。其中，日常城际交通运输需求和节后返程需求受疫情影响较大。该分析 方法有助于动态了解不同时期城际交通运输需求的类型构成和时空分布，为实施差异化的疫情防控 策略提供定量决策依据。

关键词： 城际交通；奇异值分解；时空特征；需求结构；新冠肺炎

中图分类号： U491

文献标识码：A

Inter-City Transportation Demand Under the COVID-19 Pandemic

He Linghui, Yu Qing, LiWeifeng, Li Jian, Yang Dongyuan
(Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China)

Abstract： This paper analyzes the characteristics of inter-city transportation demand during the COVID-19 pandemic using Baidu migration dataset. On one hand, the spatialtemporal characteristics of inter- city transportation demand is analyzed to understand the overall trend of passenger flow under the influence of the pandemic. On the other hand, the singular value decomposition (SVD) algorithm is used to decompose and reduce the dimension of the spatiotemporal OD matrix, and identify passenger flow structure of intercity transportation. The result shows that the inter- city transportation during the pandemic includes two stages. The trend and scale of passenger flow before the festival are basically the same as those in previous years, while the passenger flow demand after the festival increases slowly, which is long-term, in batches, and gradually involving the citizens in pandemic areas. The inter-city transportation demand under the influence of the pandemic includes four main types: normal inter-city transportation demand, returning home demand before the festival, back to work demand after the festival, and returning home demand in nonpeak periods before the festival. Among them, normal inter-city transportation and back to work demand after the festival is greatly affected by the pandemic. This method can help to understand the structure and spatialtemporal distribution of inter-city transportation at different periods, and support decision-making of differential pandemic prevention and control strategies.

Keywords： inter- city transportation; singular value decomposition algorithm; spatiotemporal characteristics; demand structure; COVID-19