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

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  • Zhu, Kangli
  • Yin, Haodong
  • Qu, YunChao
  • Wu, Jianjun

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.

Suggested Citation

  • Zhu, Kangli & Yin, Haodong & Qu, YunChao & Wu, Jianjun, 2021. "Group travel behavior in metro system and its relationship with house price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    • Handle: RePEc:eee:phsmap:v:573:y:2021:i:c:s0378437121002296
    DOI: 10.1016/j.physa.2021.125957
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    Cited by:

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    2. Zhang, Yongping & Manley, Ed & Martens, Karel & Batty, Michael, 2024. "A metro smart card data-based analysis of group travel behaviour in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 114(C).
    3. Zhou, Mingzhi & Zhou, Jiali & Zhou, Jiangping & Lei, Shuyu & Zhao, Zhan, 2023. "Introducing social contacts into the node-place model: A case study of Hong Kong," Journal of Transport Geography, Elsevier, vol. 107(C).
    4. Cankun Wei & Meichen Fu & Li Wang & Hanbing Yang & Feng Tang & Yuqing Xiong, 2022. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data," Land, MDPI, vol. 11(3), pages 1-30, February.
    5. Li, Xueyan & Qiu, Heting & Yang, Yanni & Zhang, Hankun, 2022. "Differentiated fares depend on bus line and time for urban public transport network based on travelers’ day-to-day group behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    6. Hui Zhang & Yu Cui & Jianmin Jia, 2024. "Mining Multimodal Travel Mobilities with Big Ridership Data: Comparative Analysis of Subways and Taxis," Sustainability, MDPI, vol. 16(10), pages 1-17, May.

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