论文推送 | 利用公共交通智能卡数据揭示群体出行行为模式

引  言

以往针对出行特征的研究多基于孤立个体的假设，而忽略了出行者之间的人际关系。Yongping Zhang, Karel Martens, Ying Long 利用公共交通智能卡数据揭示群体出行行为模式。该研究以北京市为研究范围，利用地铁系统数据，通过制定简单的方法，揭示群体出行行为模式。感兴趣的你快来一起学习吧！（全文链接见“阅读原文”）

Revealing group travel behavior patterns with public transit smart card data

 Yongping Zhang, Karel Martens, Ying Long

ABSTRACT：

Most analyses of travel patterns are based on the assumption of isolated individuals and ignore interpersonal relationships between travelers. In this paper, we develop a straightforward method to identify group travel behavior (GTB), defined as two or more persons intentionally traveling together from a single origin to a single destination, with public transit smart card data based on proxemics theory. We apply our method to Beijing to reveal the patterns of GTB, using all records generated by the subway system during a one-week period in 2010. Our data and method do not allow a reliable estimate of GTB share in overall travel, but do enable a description of the characteristics and the spatiotemporal pattern of GTB. The results reveal that the group size and GTB frequency follow a long tail distribution: far more people travel in small groups than in large groups and far more group travelers can be observed carrying out only one group trip than travelers making multiple group trips. Group trips tend to occur in weekends, in afternoons, and during public holidays. Furthermore, stations and lines serving leisure destinations show the highest GTB scores. We conclude that the GTB pattern is distinctly different from the pattern of individual travel in terms of both time and space, and is essentially influenced by urban land uses surrounding subway stations.

KEYWORDS：

Group travel behavior

Smart card data

Proxemics

Identification

Beijing

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