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Public transport network model based on layer operations

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  • Sui, Yi
  • Shao, Fengjing
  • Yu, Xiang
  • Sun, Rencheng
  • Li, Shujing

Abstract

Recent years have witnessed amount of studies on characteristics of public transport network and passengers’ flows, however, most of them are discussed independently. An important criterion for evaluating public transport network is whether it could satisfy travel demand well. Therefore, in this paper, we study the interaction between public transport network and passengers’ flow. Firstly, a layered network model is constructed for depicting public transport network, passengers’ Origin–Destination (OD) flows and transfer flows. Based on that, we propose layer operations to analyze interaction between them and to evaluate suitability between traffic supply (physical infrastructure) and travel demand (passengers’ OD trips and transfers). The proposed model is validated against empirical data from Chengdu and Qingdao, two metropolis cities in China. Results reveal the two cities have significant difference in suitability between travel demand and traffic supply. Although in contrast with Chengdu, Qingdao with advantages on transporting passengers by more straight lines and non-walking transfers, the average transfer flows and OD flows on stations in Qingdao are much heavier. The study demonstrates the importance of layer operations on exploring the interaction study of public transport network and traffic dynamics.

Suggested Citation

  • Sui, Yi & Shao, Fengjing & Yu, Xiang & Sun, Rencheng & Li, Shujing, 2019. "Public transport network model based on layer operations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 984-995.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:984-995
    DOI: 10.1016/j.physa.2019.04.269
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    References listed on IDEAS

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    2. Zhou, Yuyang & Zheng, Shuyan & Hu, Zhonghui & Chen, Yanyan, 2022. "Metro station risk classification based on smart card data: A case study in Beijing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    3. Canzler, Weert & Knie, Andreas, 2023. "The future of mobility: Winners and losers and new options in the public space," Discussion Papers, Research Group Digital Mobility and Social Differentiation SP III 2023-601, WZB Berlin Social Science Center.
    4. Chen, Haoqian & Sui, Yi & Shang, Wen-long & Sun, Rencheng & Chen, Zhiheng & Wang, Changying & Han, Chunjia & Zhang, Yuqian & Zhang, Haoran, 2022. "Towards renewable public transport: Mining the performance of electric buses using solar-radiation as an auxiliary power source," Applied Energy, Elsevier, vol. 325(C).
    5. Wang, Sicheng & Noland, Robert B., 2021. "What is the elasticity of sharing a ridesourcing trip?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 284-305.

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