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- Publisher Website: 10.1109/TITS.2019.2909120
- Scopus: eid_2-s2.0-85082822443
- WOS: WOS:000523478400013
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Article: Characterizing the Connectivity of Railway Networks
Title | Characterizing the Connectivity of Railway Networks |
---|---|
Authors | |
Keywords | Complex network railway systems simulation |
Issue Date | 2020 |
Citation | IEEE Transactions on Intelligent Transportation Systems, 2020, v. 21, n. 4, p. 1491-1502 How to Cite? |
Abstract | How well stations are connected with each other is a critical performance assessment of railway systems. Network analysis is a highly intuitive and interpretable approach to characterizing the physical connectivity of railway systems. However, the physical connectivity is often limited in depicting transportation network dynamics due to the lack of traffic flow information. This paper first comprehensively reviews the physical connectivity metrics and applies these metrics to evaluate the connectivity of China's high-speed railway system. Then, through integrating the real-world network topology and travel demand data, we conduct the first data-driven research on comparing the physical connectivity with the logical connectivity of railway systems. The experiments demonstrate that the physical connectivity metrics cannot well represent the connectivity of railway systems, due to the neglect of the heterogeneous distribution and temporal patterns of the passenger flows. |
Persistent Identifier | http://hdl.handle.net/10722/330407 |
ISSN | 2023 Impact Factor: 7.9 2023 SCImago Journal Rankings: 2.580 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Xu, Zhongzhi | - |
dc.contributor.author | Zhang, Qingpeng | - |
dc.contributor.author | Chen, Dingjun | - |
dc.contributor.author | He, Yuxin | - |
dc.date.accessioned | 2023-09-05T12:10:19Z | - |
dc.date.available | 2023-09-05T12:10:19Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Intelligent Transportation Systems, 2020, v. 21, n. 4, p. 1491-1502 | - |
dc.identifier.issn | 1524-9050 | - |
dc.identifier.uri | http://hdl.handle.net/10722/330407 | - |
dc.description.abstract | How well stations are connected with each other is a critical performance assessment of railway systems. Network analysis is a highly intuitive and interpretable approach to characterizing the physical connectivity of railway systems. However, the physical connectivity is often limited in depicting transportation network dynamics due to the lack of traffic flow information. This paper first comprehensively reviews the physical connectivity metrics and applies these metrics to evaluate the connectivity of China's high-speed railway system. Then, through integrating the real-world network topology and travel demand data, we conduct the first data-driven research on comparing the physical connectivity with the logical connectivity of railway systems. The experiments demonstrate that the physical connectivity metrics cannot well represent the connectivity of railway systems, due to the neglect of the heterogeneous distribution and temporal patterns of the passenger flows. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Intelligent Transportation Systems | - |
dc.subject | Complex network | - |
dc.subject | railway systems | - |
dc.subject | simulation | - |
dc.title | Characterizing the Connectivity of Railway Networks | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TITS.2019.2909120 | - |
dc.identifier.scopus | eid_2-s2.0-85082822443 | - |
dc.identifier.volume | 21 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 1491 | - |
dc.identifier.epage | 1502 | - |
dc.identifier.eissn | 1558-0016 | - |
dc.identifier.isi | WOS:000523478400013 | - |