文章编号： 1672-5328（2020）03-0033-09

阚长城1，马琦伟2，党安荣2
(1. 百度时代网络技术(北京)有限公司，北京100085；2. 清华大学建筑学院，北京100084)

摘要： 在新型冠状病毒肺炎疫情的影响下，北京市的公共交通通勤出行需求显著下降，公共交通系 统运营状况与正常情景存在较大差异，亟须科学测算通勤客流的空间分布和数量特征。基于百度地 图慧眼提供的时空大数据，针对公共交通通勤出行群体，推算通勤OD分布。借助路径规划算法对 每条公共汽车和轨道交通线路的通勤客流强度进行分区段测算。进而使用头尾分割法对公共汽车和 轨道交通线路的重要性和保障等级进行划分。最后，提出疫情时期城市公共交通系统运营优化的策 略建议。

关键词： 公共交通；新冠肺炎；通勤客流；保障等级；运营优化；时空大数据；北京市

中图分类号： U491.1+2

文献标识码：A

Predicting and Optimizing Commuting Traffic by Public Transit Under the COVID- 19 Pandemic: A Case Study of Beijing

Kan Changcheng1, Ma Qiwei2, Dang Anrong2
(1.Baidu.com Times Technology (Beijing) Co., Ltd., Beijing 100085, China; 2.School of Architecture, Tsinghua University, Beijing 100084, China)

Abstract： The commuting by public transit in Beijing greatly dropped due to the COVID- 19 outbreak, which makes the public transit operation very different from the operation under normal circumstances. It is urgent to estimate the spatial distribution and volume of commuting travel. Based on the big temporal and spatial data from huiyan.baidu.com, this paper estimates the commuting OD distribution of public transit users. The intensity of commuting traffic of bus and rail transit lines within different zones are estimated using the route planning algorithm. The paper classifies the importance and level of security for bus and rail transit lines based on the head-tail division method. Finally, the paper provides suggestions on improving the operation of urban public transit system during the pandemic.

Keywords： public transportation; COVID- 19; commuting traffic; level of security; operation improvement; big temporal and spatial data; Beijing