Inferring temporal motifs for travel pattern analysis using large scale smart card data

Document Type

Journal Article

Publication Date

2020

Subject Area

planning - methods, technology - passenger information, technology - geographic information systems, ridership - behaviour, ridership - commuting

Keywords

Temporal network, Smart card data, Travel pattern, Public transportation, Travel-activity chain, Travel regularity

Abstract

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→" style="border: 0px none currentcolor; box-sizing: border-box; direction: ltr; display: inline-block; float: none; font-size: 16.2px; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding: 0px; position: relative; white-space: nowrap; word-spacing: normal;">→Work→" style="border: 0px none currentcolor; box-sizing: border-box; direction: ltr; display: inline-block; float: none; font-size: 16.2px; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding: 0px; position: relative; white-space: nowrap; word-spacing: normal;">→Post-work activity (for dining or shopping)→" style="border: 0px none currentcolor; box-sizing: border-box; direction: ltr; display: inline-block; float: none; font-size: 16.2px; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding: 0px; position: relative; white-space: nowrap; word-spacing: normal;">→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.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part C Home Page:

http://www.sciencedirect.com/science/journal/0968090X

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