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Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

PUBLISHED
01.02.2021
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Copyright (c) 2024 Guang Yuan, Dewen Kong, Lishan Sun, Wei Luo, Yan Xu

Connectivity Contribution to Urban Hub Network Based on Super Network Theory – Case Study of Beijing

Authors:

Guang Yuan
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China

Dewen Kong
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China

Lishan Sun
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China

Wei Luo
Beijing University of Civil Engineering and Architecture, Beijing, China

Yan Xu
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China

Keywords:super network, super-edge, transportation hub, connectivity

Abstract

With the rapid development of urbanization in China, the number of travel modes and urban passenger transportation hubs has been increasing, gradually forming multi-level and multi-attribute transport hub networks in the cities. At the same time, Super Network Theory (SNT) has advantages in displaying the multi-layer transport hubs. The aim of this paper is to provide a new perspective to study connectivity contribution of potential hubs. Urban transport hubs are ranked through topological features based on Hub Super Network (HSN). This paper proposes two indexes based on Super-Edge (SE), Zero Hub Degree of SE (ZHDoSE) and a number of shared SEes (NSSE), respectively. Then, a case study was conducted in Beijing, which considers four combinations to study the influence of transport modes and subway lines on connectivity. The results show that no-normalization strengthens the contribution of transport modes and subway lines on connectivity. Besides, the transport mode contributes a lot to the connectivity. However, elements normalization strengthens the subway lines under ZHDoSE reciprocal. In addition, various weights of ZHDoSE and NSSE have different influences on the recognition results of SEes in HSN.

 

References

  1. Shen RG, Pei YL. A Parking Demand Forecasting Method for Urban Comprehensive Passenger Transport Hub Oriented High-Speed Rail. Adv Mater Res. 2012;524-527: 828-31. DOI: 10.4028/www.scientific.net/AMR.524-527.828

    Paredes R, Dueñas-Osorio L, Meel KS, Vardi MY. Principled Network Reliability Approximation: A Counting-Based Approach. Reliab Eng Syst Saf. 2019;191: 106472. DOI: 10.1016/j.ress.2019.04.025

    Li Z, Jin C, Hu P, Wang C. Resilience-based transportation network recovery strategy during emergency recovery phase under uncertainty. Reliab Eng Syst Saf. 2019;188: 503-514. DOI: 10.1016/j.ress.2019.03.052

    Leobons CM, Gouvêa Campos VB, De Mello Bandeira RA. Assessing Urban Transportation Systems Resilience: A Proposal of Indicators. Transp Res Procedia. 2019;37: 322-329. DOI: 10.1016/j.trpro.2018.12.199

    Sun L, Huang Y, Chen Y, Yao L. Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China. Transp Res Part A Policy Pract. 2018;108: 12-24.

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How to Cite
Yuan, G. (et al.) 2021. Connectivity Contribution to Urban Hub Network Based on Super Network Theory – Case Study of Beijing. Traffic&Transportation Journal. 33, 1 (Feb. 2021), 35-47. DOI: https://doi.org/10.7307/ptt.v33i1.3536.

SPECIAL ISSUE IS OUT

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Marko Matulin, PhD, Dario Babić, PhD, Marko Ševrović, PhD


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