Analysis of the spatial-temporal characteristics of residents' commuting trips under the impact of COVID-19: a case study in Shenzhen

Conference on Electronic Information Engineering and Data Processing Pub Date : 2023-05-26 DOI:10.1117/12.2682316

Jiasong Zhu, Pengyu Hong, Chunmei Zhao, Xiang Lin, Bowen Zhao

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Abstract

During the outbreak of COVID-19, many cities adopted strict travel measures to quickly cut off the transmission chain. However, these measures hindered the development of the urban economy as time passed. In the face of the possible recurrence of the epidemic, how to develop reasonable operational measures to ensure commute trips and guarantee the normal operation of the urban economy has become an important issue facing the current transportation system. Therefore, this paper is based on the smart card data to explore the spatial-temporal characteristic changes of residents' commuting under the influence of the epidemic. Firstly, the temporal characteristics of each epidemic period are described from three dimensions. Then, travel hotspots are identified and the spatial distribution changes of them in each epidemic period are explored. Next, the correlation coefficient between hotspot metro stations and surrounding land use is calculated. Finally, the rules are summarized, and operational suggestions are proposed. This article analyzes the commuting characteristics of residents based on smart card data, which can provide data support for optimizing metro operation measures.

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新冠肺炎疫情影响下居民通勤出行时空特征分析——以深圳市为例

在疫情期间，许多城市采取了严格的出行措施，迅速切断了传播链。然而，随着时间的推移，这些措施阻碍了城市经济的发展。面对疫情可能再次发生的情况，如何制定合理的运行措施，保障通勤出行，保障城市经济正常运行，成为当前交通系统面临的重要问题。因此，本文以智能卡数据为基础，探讨疫情影响下居民通勤的时空特征变化。首先，从三个维度描述了每个流行期的时间特征。然后，确定旅游热点，并探讨其在各流行期的空间分布变化。其次，计算热点地铁站与周边土地利用的相关系数。最后，对规则进行了总结，并提出了操作建议。本文分析了基于智能卡数据的居民通勤特征，为优化地铁运营措施提供数据支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Conference on Electronic Information Engineering and Data Processing

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