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Perspectives of Big Data Analysis in Urban Railway Planning: Shenzhen Metro Case Study

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Combinatorial Optimization and Applications (COCOA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10627))

Abstract

Urban railway system is of great importance to public transportation and economic development. However, due to the fast development of urban cities and time-consuming construction, it is quite challenging to plan a successful metro railway system beforehand. In this paper, we propose perspectives of evaluating traffic efficiency of metro railway systems from various factors such as the total railway traffic flow, the structure of the traffic system and the spatial distribution of work-and-home. We evaluate the implementation effect of Shenzhen railway system (particular the second phase construction) based on historical and real-time data reported by 28,000 passengers, which will provide insightful suggestions for Shenzhen metro construction in the future.

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Authors and Affiliations

  1. Shenzhen City Traffic Planning Design Research Center, Guangdong, China

    Keke Peng

  2. Department of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China

    Caiwei Yuan

  3. Department of Mathematics and Computer Science, Texas Woman’s University, Denton, Texas, 76204, USA

    Wen Xu

Authors
  1. Keke Peng
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  2. Caiwei Yuan
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  3. Wen Xu
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Corresponding author

Correspondence to Wen Xu .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Xiaofeng Gao

  2. Harbin Institute of Technology, Shenzhen, China

    Hongwei Du

  3. Kennesaw State University, Kennesaw, Georgia, USA

    Meng Han

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Peng, K., Yuan, C., Xu, W. (2017). Perspectives of Big Data Analysis in Urban Railway Planning: Shenzhen Metro Case Study. In: Gao, X., Du, H., Han, M. (eds) Combinatorial Optimization and Applications. COCOA 2017. Lecture Notes in Computer Science(), vol 10627. Springer, Cham. https://doi.org/10.1007/978-3-319-71150-8_32

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  • DOI: https://doi.org/10.1007/978-3-319-71150-8_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71149-2

  • Online ISBN: 978-3-319-71150-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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