Approximating actual flows in physical infrastructure networks: the case of the Yangtze River Delta high-speed railway network

被引:10
|
作者
Zhang, Weiyang [1 ]
Derudder, Ben [1 ]
Wang, Jianghao [2 ]
Witlox, Frank [1 ]
机构
[1] Univ Ghent, Dept Geog, Krijgslaan 281-S8, B-9000 Ghent, Belgium
[2] Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Datun Rd 11 A, Beijing 100101, Peoples R China
关键词
railway networks; passenger flows; dwell time; high-speed railway; Yangtze River Delta;
D O I
10.1515/bog-2016-0010
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
Previous empirical research on urban networks has used data on infrastructure networks to guesstimate actual inter-city flows. However, with the exception of recent research on airline networks in the context of the world city literature, relatively limited attention has been paid to the degree to which the outline of these infrastructure networks reflects the actual flows they undergird. This study presents a method to improve our estimation of urban interaction in and through infrastructure networks by focusing on the example of passenger railways, which is arguably a key potential data source in research on urban networks in metropolitan regions. We first review common biases when using infrastructure networks to approximate actual inter-city flows, after which we present an alternative approach that draws on research on operational train scheduling. This research has shown that 'dwell time' at train stations reflects the length of the alighting and boarding process, and we use this insight to estimate actual interaction through the application of a bimodal network projection function. We apply our method to the high-speed railway (HSR) network within the Yangtze River Delta (YRD) region, discuss the difference between our modelled network and the original network, and evaluate its validity through a systemic comparison with a benchmark dataset of actual passenger flows. (C) 2016 Nicolaus Copernicus University. All rights reserved.
引用
收藏
页码:145 / 160
页数:16
相关论文
共 31 条
  • [21] Does high-speed rail connection really promote local economy? Evidence from China's Yangtze River Delta
    Gao, Yanyan
    Song, Shunfeng
    Sun, Jun
    Zang, Leizhen
    REVIEW OF DEVELOPMENT ECONOMICS, 2020, 24 (01) : 316 - 338
  • [22] How does high-speed rail affect off-site investments? Evidence from the Yangtze River Delta, China
    Jiao, Jingjuan
    Zhao, Hongyu
    Lyu, Guowei
    TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, 2024, 181
  • [23] The Impacts of High-Speed Rail on Producer Service Industry Agglomeration: Evidence from China's Yangtze River Delta Urban Agglomeration
    Jin, Yanan
    Ou, Guoli
    SUSTAINABILITY, 2023, 15 (04)
  • [24] The spatial spillover effect of technological innovation network in cities: a case of the high-tech industry of Yangtze River Delta
    Song, Young-Hyun
    Kim, Jung Wook
    INTERNATIONAL JOURNAL OF URBAN SCIENCES, 2023, 27 (03) : 414 - 441
  • [25] The Effect of Transport Infrastructure on Land-use Change: The Case of Toll Road and High-Speed Railway Development in West Java']Java
    Salim, Wilmar
    Faoziyah, Uly
    JOURNAL OF REGIONAL AND CITY PLANNING, 2022, 33 (01): : 48 - 65
  • [26] High-speed railway development and its impact on urban economy and population: A case study of nine provinces along the Yellow River, China
    Wang, Fang
    Liu, Zhao
    Xue, Pengcheng
    Dang, Anrong
    SUSTAINABLE CITIES AND SOCIETY, 2022, 87
  • [27] Analysis of Coupling Coordination Relationship between the Accessibility and Economic Linkage of a High-Speed Railway Network Case Study in Hunan, China
    Zou, Mengzhi
    Li, Changyou
    Xiong, Yanni
    SUSTAINABILITY, 2022, 14 (13)
  • [28] Urban Agglomeration High-Speed Railway Backbone Network Planning: A Case Study of Beijing-Tianjin-Hebei Region, China
    Zhao, Jun
    Rong, Wenyu
    Liu, Di
    SUSTAINABILITY, 2023, 15 (08)
  • [29] Network characteristics of inter-city tourist flows in the Yangtze River Delta of China: case study of the May Day Holiday based on Tencent migration big data
    Wu, Peipei
    Zhu, Xiaochuan
    Feng, Xiang
    Liu, Huimin
    Dong, Jianing
    ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2023, 27 (5) : 10041 - 10058
  • [30] Scatter-GNN: A Scatter Graph Neural Network for Prediction of High-Speed Railway Station-A Case Study of Yinchuan-Chongqing HSR
    Ma, Manfu
    Zhang, Yiding
    Li, Yong
    Li, Xiaoxue
    Liu, Yiping
    APPLIED SCIENCES-BASEL, 2023, 13 (01):