Group travel behavior in metro system and its relationship with house price

Kangli Zhu, Haodong Yin, YunChao Qu and Jianjun Wu

Physica A: Statistical Mechanics and its Applications, 2021, vol. 573, issue C

Abstract: Modeling travel behavior inter-relationship is important to traffic planning and management as well as spread modeling of infectious disease and information. Our ability to understand inter-traveler behavior has remained unsatisfactorily limited due to the lack of empirical travel-together data. Taking advantage of access to a large-scale smart-card dataset from the Beijing metro network, we investigate a KS test-based method to identify group travel and study the spatial–temporal distribution of group travel as well as their interaction with socio-economic attributes. Origin and destination stations, departure time, and travel dates are separately classified into several types according to the passenger flow adopting a tensor factorization technique. The results suggest that temporally passengers tend to travel in groups in the afternoon and on the weekend; spatially areas with moderate house prices are the least source for group demands, while areas with a relatively low house price are the most favorite place for group trips. This novel discovery can help to understand the impact of socio-economic attributes on intra-urban group/passenger flow movement.

Keywords: Metro system; Group travel behavior; Spatial–temporal pattern; Tensor factorization (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:573:y:2021:i:c:s0378437121002296

DOI: 10.1016/j.physa.2021.125957

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