Home Journal of Tsinghua University (Science and Technology) Article
Published in Chinese. Use 
AI Chat Paper for AI reading assistance in English.
PDF (1.3 MB)
Cite
EndNote(RIS) BibTeX
Collect
Collect
AI Chat Paper Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Publishing Language: Chinese
Yun JING1( 
Show Author Information
Abstract
China's high-speed railway network is becoming more developed and urban agglomerations are gradually expanding with significantly more new urban agglomerations. Passenger transportation modes need to be further studied to meet the needs of passengers traveling across urban agglomerations on high-speed rail networks. This study used an improved multinational logit (MNL) model to calculate the passenger flow loss rate of various transportation modes and constructed a model for optimizing high-speed railway network passenger transport based on the need for cross-urban group travel. The core nodes in the network were identified using a network node measurement algorithm based on the entropy weight technique for order preference by similarity to ideal solution (TOPSIS) algorithm. Transition nodes in the passenger flow were identified using a transition multiplication selection algorithm based on network centrality. The model was then applied to the network composed of 22 cities with node measurement values above 0.15 to analyze full and partial network connections. The results show that the total passenger flow rate on the high-speed railway networks for cross-city travel can be increased by 11.54% with 90 fewer trains.
Keywords
centralityhigh-speed railway networkinter-city travelthrough modetransfer mode
CLC number: U292.7 Document code: A Article ID: 1000-0054(2022)07-1151-12
References
[1]
GREENE W H, HENSHER D A. A latent class model for discrete choice analysis: Contrasts with mixed logit[J]. Transportation Research Part B: Methodological, 2003, 37(8): 681-698.
[2]
BIAN C Z, LU H P. Passenger flow-split model in comprehensive transportation corridor[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2009, 33(4): 611-614. (in Chinese)
[3]
WANG J, CUI Z Y, CHEN P H. Study on the choice of passenger travel modes in Beijing-Shanghai passage[C]// Proceedings of World Transport Convention 2019. Beijing, China: China Highway & Transportation Society. (in Chinese)
[4]
GHOSEIRI K, SZIDAROVSZKY F, ASGHARPOUR M J. A multi-objective train scheduling model and solution[J]. Transportation Research Part B: Methodological, 2004, 38(10): 927-952.
[5]
ALBERTO C, LEO K, PAOLO T. Wiley encyclopedia of operations research and management science[M]. Hoboken: Wiley Press, 2011.
[6]
QIN J, TAN Y C, ZHANG W, et al. Train planning optimization for intercity railway based on space-time network[J]. Journal of the China Railway Society, 2020, 42(2): 1-10. (in Chinese)
[7]
ZHANG P. Research on the compilation and evaluation of high-speed railway train operation schemes for long-distance passenger flow[D]. Shijiazhuang: Shijiazhuang Tiedao University, 2020. (in Chinese)
[8]
WANG M M. High-speed railway product design based on transfer transportation organization mode[D]. Chengdu: Southwest Jiaotong University, 2016. (in Chinese)
[9]
LI Q, HUANG P. Study on passenger transfer of passenger-dedicated lines[J]. Railway Transport and Economy, 2009, 31(1): 23-26. (in Chinese)
[10]
LIU X X, LI B. A study on the scheme selection of transfer activities within the high-speed railway hub[J]. Railway Transport and Economy, 2018, 40(11): 32-37, 63. (in Chinese)
[11]
SUN X F, LV H X, LI S D. Analysis of the choice of passenger transport mode in Chengdu-Xi'an channel[J]. Railway Economics Research, 2018(5): 42-47. (in Chinese)
[12]
ZHENG X J. Study on users' transportation choice of transportation corridor model based on travel chain[D]. Xi'an: Chang'an University, 2016. (in Chinese)
[13]
ZHANG N. Research on reasonable running distance of high speed railway trains[D]. Chengdu: Southwest Jiaotong University, 2019. (in Chinese)
[14]
HECKMAN J J, STIXRUD J, URZUA S. The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior[J]. Journal of Labor Economics, 2006, 24(3): 411-482.
[15]
BAO J J. Discussion on the opening mode of medium and long distance train based on economic benefit calculation[J]. China Transportation Review, 2018, 40(3): 61-65. (in Chinese)
[16]
LIU Z G. Research on risk evaluation of cold chain logistics distribution system based on entropy weighted TOPSIS and fuzzy Bayesian network[D]. Nanchang: Jiangxi University of Finance and Economics, 2020. (in Chinese)
[17]
SIMINI F, GONZÁLEZ M C, MARITAN A, et al. A universal model for mobility and migration patterns[J]. Nature, 2012, 484(7392): 96-100.
[18]
WANG W X. Research on evaluation and adjustment of high-speed railway line plan based on dynamic demand[D]. Chengdu: Southwest Jiaotong University, 2017. (in Chinese)
Journal of Tsinghua University (Science and Technology)
Volume 62 Issue 7,July 2022
Pages 1151-1162
Cite this article:
JING Y, LI K, WANG X, et al. Passenger flow transport modes of inter-city groups with a high-speed railway network. Journal of Tsinghua University (Science and Technology), 2022, 62(7): 1151-1162. https://doi.org/10.16511/j.cnki.qhdxxb.2022.26.015
Received: 29 October 2021
Published: 15 July 2022
© Journal of Tsinghua University (Science and Technology). All rights reserved.
 at Amazon coupons and deals at Dealighted)