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Big data driven urban railway planning: Shenzhen metro case study

Author

Listed:
  • Wen Xu

    (Texas Woman’s University)

  • Caiwei Yuan

    (Harbin Institute of Technology (Shenzhen)
    Shenzhen Key Laboratory of Internet Information Collaboration)

  • Keke Peng

    (Shenzhen City Transportation Planning and Design Research Center)

  • Hongwei Du

    (Harbin Institute of Technology (Shenzhen)
    Shenzhen Key Laboratory of Internet Information Collaboration)

Abstract

Planning a successful metro railway system is challenging due to the fast development of urban cities and time-consuming construction. Recently big data reflecting city dynamics has become widely available, which enables us to solve this challenging problem from a data mining perspective. In this paper, we propose a framework to evaluate the traffic efficiency of metro railway systems from various factors such as the railway traffic flow, the structure of the traffic system and the spatial distribution of work-and-home. Based on the commute data (e.g., railway boarding) of Shenzhen rail transit reported by 28,000 passengers and electronic boarding card data provided by Shenzhen Railway Company, we assess the bottlenecks and congested areas of the system, understand passenger travel patterns, and observe organizational operations and deficiencies. The experimental results help us better understand how big data can help make Shenzhen metro railway more efficient and effective in terms of planning and management in the future.

Suggested Citation

  • Wen Xu & Caiwei Yuan & Keke Peng & Hongwei Du, 0. "Big data driven urban railway planning: Shenzhen metro case study," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-23.
    • Handle: RePEc:spr:jcomop:v::y::i::d:10.1007_s10878-019-00422-0
    DOI: 10.1007/s10878-019-00422-0
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    Cited by:

    1. Wen Xu & Caiwei Yuan & Keke Peng & Hongwei Du, 2021. "Big data driven urban railway planning: Shenzhen metro case study," Journal of Combinatorial Optimization, Springer, vol. 42(3), pages 593-615, October.

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