In this paper, we proposed a new method to extract travel patterns for transit riders from different public transportation systems based on temporal motif, which is an emerging notion in network science literature. We then developed a scalable algorithm to recognize temporal motifs from daily trip sub-sequences extracted from two smart card datasets. Our method shows its benefits in uncovering the potential correlation between varying topologies of trip combinations and specific activity chains. Commuting, different types of transfer, and other travel behaviors have been identified. Besides, varying travel-activity chains like “Home$\to$Work$\to$Post-work activity (for dining or shopping)$\to$Back home” and the corresponding travel motifs have been distinguished by incorporating the land use information in the GIS data. The analysis results contribute to our understanding of transit riders’ travel behavior. We also present application examples of the travel motif to demonstrate the practicality of the proposed approach. Our methodology can be adapted to travel pattern analysis using different data sources and lay the foundation for other travel-pattern related studies.

在本文中，我们提出了一种新的方法，该方法基于时间主题从不同的公共交通系统中提取过境乘客的出行方式，这是网络科学文献中的一个新兴概念。然后，我们开发了一种可伸缩的算法来识别从两个智能卡数据集中提取的每日行程子序列中的时间主题。我们的方法在揭示旅行组合的不同拓扑与特定活动链之间的潜在关联方面显示出其优势。通勤，不同类型的转移和其他旅行行为已被确定。此外，各种旅行活动链，例如“家庭$\to$工作$\to$下班后活动（用于餐饮或购物）$\to$通过将土地利用信息整合到GIS数据中来区分“回家”和相应的旅行主题。分析结果有助于我们了解公交乘客的旅行行为。我们还将介绍旅行主题的应用示例，以证明所提出方法的实用性。我们的方法可以适用于使用不同数据源的出行模式分析，并为其他出行模式相关研究奠定了基础。