Identification of Critical Nodes in Urban Transportation Network Through Network Topology and Server Routes

Shihong Jiang; Zheng Luo; Ze Yin; Zhen Wang; Songxin Wang; Chao Gao

doi:10.1007/978-3-030-82136-4_32

Identification of Critical Nodes in Urban Transportation Network Through Network Topology and Server Routes

Shihong Jiang, Zheng Luo, Ze Yin, Zhen Wang, Songxin Wang, Chao Gao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

The identification of critical nodes has great practical significance to the urban transportation network (UTN) due to its contribution to enhancing the efficient operation of UTN. Several existing studies have discovered the critical nodes from the perspectives of network topology or passenger flow. However, little attention has been paid to the perspective of service routes in the identification of critical stations, which reflects the closeness of the connection between stations. In order to address the above problem, we propose a two-layer network of UTN to characterize the effects of server routes and present a novel method of critical nodes identification (BMRank). BMRank is inspired by eigenvector centrality, which focuses on network topology and mutual enhancement relationship between stations and server routes, simultaneously. The extensive experiments on the UTN of Shanghai illustrate that BMRank performs better in the identification of critical stations compared with baseline methods. Specifically, the performance of BMRank increases by 12.4% over the best of baseline methods on a low initial failure scale.

Original language	English
Title of host publication	Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings
Editors	Han Qiu, Cheng Zhang, Zongming Fei, Meikang Qiu, Sun-Yuan Kung
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	395-407
Number of pages	13
ISBN (Print)	9783030821357
DOIs	https://doi.org/10.1007/978-3-030-82136-4_32
State	Published - 2021
Event	14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021 - Tokyo, Japan Duration: 14 Aug 2021 → 16 Aug 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12815 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021
Country/Territory	Japan
City	Tokyo
Period	14/08/21 → 16/08/21

Keywords

Critical nodes
Network topology
Server routes
Urban transportation network

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-82136-4_32

Cite this

Jiang, S., Luo, Z., Yin, Z., Wang, Z., Wang, S., & Gao, C. (2021). Identification of Critical Nodes in Urban Transportation Network Through Network Topology and Server Routes. In H. Qiu, C. Zhang, Z. Fei, M. Qiu, & S.-Y. Kung (Eds.), Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings (pp. 395-407). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12815 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-82136-4_32

Jiang, Shihong ; Luo, Zheng ; Yin, Ze et al. / Identification of Critical Nodes in Urban Transportation Network Through Network Topology and Server Routes. Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings. editor / Han Qiu ; Cheng Zhang ; Zongming Fei ; Meikang Qiu ; Sun-Yuan Kung. Springer Science and Business Media Deutschland GmbH, 2021. pp. 395-407 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Identification of Critical Nodes in Urban Transportation Network Through Network Topology and Server Routes",

abstract = "The identification of critical nodes has great practical significance to the urban transportation network (UTN) due to its contribution to enhancing the efficient operation of UTN. Several existing studies have discovered the critical nodes from the perspectives of network topology or passenger flow. However, little attention has been paid to the perspective of service routes in the identification of critical stations, which reflects the closeness of the connection between stations. In order to address the above problem, we propose a two-layer network of UTN to characterize the effects of server routes and present a novel method of critical nodes identification (BMRank). BMRank is inspired by eigenvector centrality, which focuses on network topology and mutual enhancement relationship between stations and server routes, simultaneously. The extensive experiments on the UTN of Shanghai illustrate that BMRank performs better in the identification of critical stations compared with baseline methods. Specifically, the performance of BMRank increases by 12.4% over the best of baseline methods on a low initial failure scale.",

keywords = "Critical nodes, Network topology, Server routes, Urban transportation network",

author = "Shihong Jiang and Zheng Luo and Ze Yin and Zhen Wang and Songxin Wang and Chao Gao",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021 ; Conference date: 14-08-2021 Through 16-08-2021",

year = "2021",

doi = "10.1007/978-3-030-82136-4_32",

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Jiang, S, Luo, Z, Yin, Z, Wang, Z, Wang, S & Gao, C 2021, Identification of Critical Nodes in Urban Transportation Network Through Network Topology and Server Routes. in H Qiu, C Zhang, Z Fei, M Qiu & S-Y Kung (eds), Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12815 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 395-407, 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021, Tokyo, Japan, 14/08/21. https://doi.org/10.1007/978-3-030-82136-4_32

Identification of Critical Nodes in Urban Transportation Network Through Network Topology and Server Routes. / Jiang, Shihong; Luo, Zheng; Yin, Ze et al.
Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings. ed. / Han Qiu; Cheng Zhang; Zongming Fei; Meikang Qiu; Sun-Yuan Kung. Springer Science and Business Media Deutschland GmbH, 2021. p. 395-407 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12815 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Identification of Critical Nodes in Urban Transportation Network Through Network Topology and Server Routes

AU - Jiang, Shihong

AU - Luo, Zheng

AU - Yin, Ze

AU - Wang, Zhen

AU - Wang, Songxin

AU - Gao, Chao

PY - 2021

Y1 - 2021

N2 - The identification of critical nodes has great practical significance to the urban transportation network (UTN) due to its contribution to enhancing the efficient operation of UTN. Several existing studies have discovered the critical nodes from the perspectives of network topology or passenger flow. However, little attention has been paid to the perspective of service routes in the identification of critical stations, which reflects the closeness of the connection between stations. In order to address the above problem, we propose a two-layer network of UTN to characterize the effects of server routes and present a novel method of critical nodes identification (BMRank). BMRank is inspired by eigenvector centrality, which focuses on network topology and mutual enhancement relationship between stations and server routes, simultaneously. The extensive experiments on the UTN of Shanghai illustrate that BMRank performs better in the identification of critical stations compared with baseline methods. Specifically, the performance of BMRank increases by 12.4% over the best of baseline methods on a low initial failure scale.

AB - The identification of critical nodes has great practical significance to the urban transportation network (UTN) due to its contribution to enhancing the efficient operation of UTN. Several existing studies have discovered the critical nodes from the perspectives of network topology or passenger flow. However, little attention has been paid to the perspective of service routes in the identification of critical stations, which reflects the closeness of the connection between stations. In order to address the above problem, we propose a two-layer network of UTN to characterize the effects of server routes and present a novel method of critical nodes identification (BMRank). BMRank is inspired by eigenvector centrality, which focuses on network topology and mutual enhancement relationship between stations and server routes, simultaneously. The extensive experiments on the UTN of Shanghai illustrate that BMRank performs better in the identification of critical stations compared with baseline methods. Specifically, the performance of BMRank increases by 12.4% over the best of baseline methods on a low initial failure scale.

KW - Critical nodes

KW - Network topology

KW - Server routes

KW - Urban transportation network

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U2 - 10.1007/978-3-030-82136-4_32

DO - 10.1007/978-3-030-82136-4_32

M3 - 会议稿件

AN - SCOPUS:85113767469

SN - 9783030821357

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 395

EP - 407

BT - Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings

A2 - Qiu, Han

A2 - Zhang, Cheng

A2 - Fei, Zongming

A2 - Qiu, Meikang

A2 - Kung, Sun-Yuan

PB - Springer Science and Business Media Deutschland GmbH

T2 - 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021

Y2 - 14 August 2021 through 16 August 2021

ER -

Jiang S, Luo Z, Yin Z, Wang Z, Wang S, Gao C. Identification of Critical Nodes in Urban Transportation Network Through Network Topology and Server Routes. In Qiu H, Zhang C, Fei Z, Qiu M, Kung SY, editors, Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 395-407. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-82136-4_32

Identification of Critical Nodes in Urban Transportation Network Through Network Topology and Server Routes

Abstract

Publication series

Conference

Keywords

UN SDGs

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