Hub Node Identification in Urban Rail Transit Network Evolution Using a Ridership-Weighted Network

被引:1
作者
Tian, Tian [1 ]
Cheng, Yanqiu [1 ]
Liang, Yichen [1 ]
Ma, Chen [1 ]
Chen, Kuanmin [1 ]
Hu, Xianbiao [2 ]
机构
[1] Changan Univ, Coll Transportat Engn, Xian, Shaanxi, Peoples R China
[2] Penn State Univ, Dept Civil & Environm Engn, University Pk, PA USA
基金
中国国家自然科学基金;
关键词
planning and analysis; transportation planning analysis and application; decision tools; project selection; public transportation; planning and development; station; VULNERABILITY; RESILIENCE;
D O I
10.1177/03611981231217500
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
With the development of the urban rail transit network (URTN), the network structure and performance have changed, and the node importance has also been redistributed. However, little research has been done on how hub nodes change as the network develops over a lengthy period. Moreover, most hub node identification methods only focus on the analysis of topological networks or single-dimension measurements, resulting in inaccurate identification results. To overcome the above limitations, a novel method of hub node identification is proposed. Based on the ridership-weighted network model, the node centrality and reliability are aggregated to quantify the weighted comprehensive importance of the nodes. Furthermore, network invulnerability measurement is used to demonstrate the effectiveness of the proposed method. This method is applied to the Xi'an Urban Rail Transit Network (XURTN) from 2011 to 2021. With the XURTN's development, its connectivity, balance, and fault tolerance have improved. After the basic network skeleton was formed, the number and proportion of hub nodes increased steadily. By comparing the spatial characteristics of the identified hub nodes over two successive periods, it can be found that the evolution direction of the hub nodes is correlated with the type of new lines and coincides also with the development direction of the urban area. In addition, the node orders of the proposed method have a greater impact on the network vulnerability, in which the network-weighted efficiency E-w decreases faster and more dramatically, that is, 1.17%-45.75% more than that of other methods. Overall, this study provides a basis for the URTN and station planning and management.
引用
收藏
页码:549 / 569
页数:21
相关论文
共 47 条
[31]   Reducing an urban rail transit network's passenger-oriented vulnerability by adding turn-back tracks [J].
Liu, Jie ;
Schonfeld, Paul M. ;
Zhan, Shuguang ;
Wang, Kelvin C. P. ;
Peng, Qiyuan .
TRANSPORTMETRICA B-TRANSPORT DYNAMICS, 2022, 10 (01) :667-692
[32]   Structural characteristics analysis and cascading failure impact analysis of urban rail transit network: From the perspective of multi-layer network [J].
Yin, Dezhi ;
Huang, Wencheng ;
Shuai, Bin ;
Liu, Hongyi ;
Zhang, Yue .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2022, 218
[33]   Vulnerability Analysis of Urban Rail Transit Network within Multi-Modal Public Transport Networks [J].
Lu, Qing-Chang ;
Lin, Shan .
SUSTAINABILITY, 2019, 11 (07)
[34]   Modeling network vulnerability of urban rail transit under cascading failures: A Coupled Map Lattices approach [J].
Lu, Qing-Chang ;
Zhang, Lei ;
Xu, Peng-Cheng ;
Cui, Xin ;
Li, Jing .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2022, 221
[35]   Modelling cascade dynamics of passenger flow congestion in urban rail transit network induced by train delay [J].
Li, Man ;
Zhou, Xinyi ;
Wang, Yanhui ;
Jia, Limin ;
An, Min .
ALEXANDRIA ENGINEERING JOURNAL, 2022, 61 (11) :8797-8807
[36]   Robustness assessment of urban rail transit based on complex network theory: A case study of the Beijing Subway [J].
Yang, Yuhao ;
Liu, Yongxue ;
Zhou, Minxi ;
Li, Feixue ;
Sun, Chao .
SAFETY SCIENCE, 2015, 79 :149-162
[37]   Simulation-Based Method for the Calculation of Passenger Flow Distribution in an Urban Rail Transit Network Under Interruption [J].
Su, Guanghui ;
Si, Bingfeng ;
Zhi, Kun ;
Zhao, Ben ;
Zheng, Xuanchuan .
URBAN RAIL TRANSIT, 2023, 9 (02) :110-126
[38]   Assessing Customer Satisfaction of Urban Rail Transit Network in Tianjin Based on Intuitionistic Fuzzy Group Decision Model [J].
Wang, Yuning ;
Zhang, Zhe ;
Sun, Hui .
DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2018, 2018
[39]   Assessing the vulnerability of urban rail transit network under heavy air pollution: A dynamic vehicle restriction perspective [J].
Ma, Fei ;
Liang, Yuan ;
Yuen, Kum Fai ;
Sun, Qipeng ;
Zhu, Yujie ;
Wang, Yixuan ;
Shi, Wenjing .
SUSTAINABLE CITIES AND SOCIETY, 2020, 52
[40]   Comparison analysis on complex topological network models of urban rail transit: A case study of Shenzhen Metro in China [J].
Meng, Yangyang ;
Tian, Xiangliang ;
Li, Zhongwen ;
Zhou, Wei ;
Zhou, Zhijie ;
Zhong, Maohua .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2020, 559