Rail Transit Networks and Network Motifs: A Review and Research Agenda
Abstract
:1. Introduction
2. Materials and Methods
3. Intercity and Urban Rail Transit
3.1. Intercity Rail Transit
3.2. Urban Rail Transit
4. Rail Transit Networks
4.1. Global Rail Transit Network Structure
4.2. Local Rail Transit Network Structure
5. Network Motif Detection and Analysis
6. A Research Agenda of Network Motif Analysis in Rail Transit Networks
6.1. Unweighted Rail Transit Network Motifs Analysis
- Rail transit station networks, built with the Space-L representation.
- Rail transit line networks, defined with the Space-P representation.
- Multiplex transportation networks including rail transit, examining specifically network motifs related to transfers between means of transportation.
6.2. Weighted Rail Transit Network Motif Analysis
- Weighted rail transit station network.
- Weighted rail transit line network.
- Weighted multiplex transportation networks.
- Operation timetable, including frequency of service and timestamps of departure and arrival.
- Geographical distance between stations.
- Communication between different means of transportation in multiplex transportation networks.
6.3. Network Motif Decomposition and Analysis
- What is the impact of the potential prevalence of any of the five-node network motifs on resilience, reliability, and efficiency on rail transit networks?
- How does the prevalence of any of the five-node network motifs impact on passenger flows?
- What are the five-node network motifs that allow the most effective passenger flow on multiplex transportation networks?
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Murray, A.T. An overview of network vulnerability modeling approaches. GeoJournal 2013, 78, 209–221. [Google Scholar] [CrossRef]
- Yang, Y.; Liu, Y.; Zhou, M.; Li, F.; Sun, C. Robustness assessment of urban rail transit based on complex network theory: A case study of the Beijing Subway. Saf. Sci. 2015, 79, 149–162. [Google Scholar] [CrossRef]
- Li, M.; Wang, Y.; Jia, L.; Cui, Y. Risk propagation analysis of urban rail transit based on network model. Alex. Eng. J. 2020, 59, 1319–1331. [Google Scholar] [CrossRef]
- Liu, X.; Xia, H. Networking and sustainable development of urban spatial planning: Influence of rail transit. Sustain. Cities Soc. 2023, 99, 104865. [Google Scholar] [CrossRef]
- Jurković, Ž.; Hadzima-Nyarko, M.; Lovoković, D. Railway corridors in Croatian cities as factors of sustainable spatial and cultural development. Sustainability 2021, 13, 6928. [Google Scholar] [CrossRef]
- Chen, J.; Liu, J.; Peng, Q.; Yin, Y. Strategies to Enhance the Resilience of an Urban Rail Transit Network. Transp. Res. Rec. 2022, 2676, 342–354. [Google Scholar] [CrossRef]
- Chen, J.; Liu, J.; Peng, Q.; Yin, Y. Resilience assessment of an urban rail transit network: A case study of Chengdu subway. Phys. A Stat. Mech. Appl. 2022, 586, 126517. [Google Scholar] [CrossRef]
- Xu, X.; Xu, C.; Zhang, W. Research on the Destruction Resistance of Giant Urban Rail Transit Network from the Perspective of Vulnerability. Sustainability 2022, 14, 7210. [Google Scholar] [CrossRef]
- Yin, Y.; Huang, W.; Xie, A.; Li, H.; Gong, W.; Zhang, Y. Syncretic K-shell algorithm for node importance identification and invulnerability evaluation of urban rail transit network. Appl. Math. Model. 2023, 120, 400–419. [Google Scholar] [CrossRef]
- Feng, F.; Zou, Z.; Liu, C.; Zhou, Q.; Liu, C. Forecast of Short-Term Passenger Flow in Multi-Level Rail Transit Network Based on a Multi-Task Learning Model. Sustainability 2023, 15, 3296. [Google Scholar] [CrossRef]
- Chen, T.; Ma, J.; Zhu, Z.; Guo, X. Evaluation Method for Node Importance of Urban Rail Network Considering Traffic Characteristics. Sustainability 2023, 15, 3582. [Google Scholar] [CrossRef]
- Wang, X.; Shi, Z.; Chen, Z. Air traffic network motif Recognition and subgraph structure resilience evaluation. Acta Aeronaut. Astronaut. Sin. 2021, 42, 324715. [Google Scholar] [CrossRef]
- Fu, X.; Passarella, A.; Quercia, D.; Sala, A.; Strufe, T. Online Social Networks. Comput. Commun. 2016, 73, 163–166. [Google Scholar] [CrossRef]
- Milo, R.; Shen-Orr, S.; Itzkovitz, S.; Kashtan, N.; Chklovskii, D.; Alon, U. Network Motifs: Simple Building Blocks of Complex Networks. Science 2002, 298, 824–827. [Google Scholar] [CrossRef] [PubMed]
- Shen-Orr, S.S.; Milo, R.; Mangan, S.; Alon, U. Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 2002, 31, 64–68. [Google Scholar] [CrossRef] [PubMed]
- Girvan, M.; Newman, M.E.J. Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 2002, 99, 7821–7826. [Google Scholar] [CrossRef] [PubMed]
- Boccaletti, S.; Latora, V.; Moreno, Y.; Chavez, M.; Hwang, D.; Complessi, S.; Fermi, L.E.; Catania, U.; Sofia, V.S.; Nucleare, F.; et al. Complex networks: Structure and dynamics. Phys. Rep. 2006, 424, 175–308. [Google Scholar] [CrossRef]
- Liu, L.; Wang, J.; Han, C. Homogeneous and heterogeneous building blocks in national emergency organizational collaboration networks. China Saf. Sci. J. 2017, 27, 1–6. [Google Scholar] [CrossRef]
- Zhang, J.; Wang, M. Transportation functionality vulnerability of urban rail transit networks based on movingblock: The case of Nanjing metro. Phys. A Stat. Mech. Appl. 2019, 535, 122367. [Google Scholar] [CrossRef]
- Ma, F.; Liang, Y.; Yuen, K.F.; Sun, Q.; Zhu, Y.; Wang, Y.; Shi, W. Assessing the vulnerability of urban rail transit network under heavy air pollution: A dynamic vehicle restriction perspective. Sustain. Cities Soc. 2020, 52, 101851. [Google Scholar] [CrossRef]
- Zhou, Y.; Kundu, T.; Qin, W.; Goh, M.; Sheu, J.B. Vulnerability of the worldwide air transportation network to global catastrophes such as COVID-19. Transp. Res. Part E Logist. Transp. Rev. 2021, 154, 102469. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Jin, X.; Zhang, Y.; Yao, D. Topological Analysis of Urban Transit Networks Using Bipartite Graph Model. Syst. Eng. Theory Pract. 2010, 59, 6689–6696. [Google Scholar]
- Ma, J.; Bai, Y.; Han, B. Characteristic analysis of basic unit and complex network for urban rail transit. J. Traffic Transp. Eng. 2010, 10, 65–70. [Google Scholar] [CrossRef]
- Wei, Z.; Gan, Y.; Zhao, P. Characteristic Research of Urban Complex Traffic Network. J. Transp. Syst. Eng. Inf. Technol. 2015, 15, 106–111. [Google Scholar] [CrossRef]
- Guo, X.; Wu, J.; Sun, H.; Yang, X.; Jin, J.G.; Wang, D.Z. Scheduling synchronization in urban rail transit networks: Trade-offs between transfer passenger and last train operation. Transp. Res. Part A Policy Pract. 2020, 138, 463–490. [Google Scholar] [CrossRef]
- Zhou, Y.; Kundu, T.; Goh, M.; Sheu, J.B. Multimodal transportation network centrality analysis for Belt and Road Initiative. Transp. Res. Part E Logist. Transp. Rev. 2021, 149, 102292. [Google Scholar] [CrossRef]
- Lin, P.; Weng, J.; Fu, Y.; Alivanistos, D.; Yin, B. Study on the topology and dynamics of the rail transit network based on automatic fare collection data. Phys. A Stat. Mech. Appl. 2020, 545, 123538. [Google Scholar] [CrossRef]
- Ahmed, C.; Nur, K.; Ochieng, W. GIS and genetic algorithm based integrated optimization for rail transit system planning. J. Rail Transp. Plan. Manag. 2020, 16, 100222. [Google Scholar] [CrossRef]
- Canca, D.; De-Los-Santos, A.; Laporte, G.; Mesa, J.A. Integrated Railway Rapid Transit Network Design and Line Planning problem with maximum profit. Transp. Res. Part E Logist. Transp. Rev. 2019, 127, 1–30. [Google Scholar] [CrossRef]
- Dey, A.K.; Gel, Y.R.; Poor, H.V. What network motifs tell us about resilience and reliability of complex networks. Proc. Natl. Acad. Sci. USA 2019, 116, 19368–19373. [Google Scholar] [CrossRef] [PubMed]
- Cugmas, M.; Ferligoj, A.; Škerlavaj, M.; Žiberna, A. Global structures and local network mechanisms of knowledge-flow networks. PLoS ONE 2021, 16, e0246660. [Google Scholar] [CrossRef] [PubMed]
- Cadarso, L.; Marín, A.; Maróti, G. Recovery of disruptions in rapid transit networks. Transp. Res. Part E Logist. Transp. Rev. 2013, 53, 15–33. [Google Scholar] [CrossRef]
- Fisch, C.; Block, J. Six tips for your (systematic) literature review in business and management research. Manag. Rev. Q. 2018, 68, 103–106. [Google Scholar] [CrossRef]
- Denyer, D.; Tranfield, D. Producing a systematic review. In The SAGE Handbook of Organizational Research Methods; Sage Publications Ltd.: Thousand Oaks, CA, USA, 2009; pp. 671–689. [Google Scholar]
- Cao, Z.; Wang, Y.; Yang, Z.; Chen, C.; Zhang, S. Timetable Rescheduling Using Skip-Stop Strategy for Sustainable Urban Rail Transit. Sustainability 2023, 15, 14511. [Google Scholar] [CrossRef]
- Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; Prisma Group. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Ann. Intern. Med. 2009, 151, 264–269. [Google Scholar] [CrossRef] [PubMed]
- Rao, S.H. Transportation synthetic sustainability indices: A case of Taiwan intercity railway transport. Ecol. Indic. 2021, 127, 107753. [Google Scholar] [CrossRef]
- Feng, J.; Li, X.; Mao, B.; Xu, Q.; Bai, Y. Weighted complex network analysis of the Beijing subway system: Train and passenger flows. Phys. A Stat. Mech. Appl. 2017, 474, 213–223. [Google Scholar] [CrossRef]
- Johnson, B.E. American intercity passenger rail must be truly high-speed and transit-oriented. J. Transp. Geogr. 2012, 22, 295–296. [Google Scholar] [CrossRef]
- Nordin, N.H.B.; Mohd Masirin, M.I.H.; Ghazali, M.I.B.; Azis, M.I.B. Appraisal on Rail Transit Development: A Review on Train Services and Safety. IOP Conf. Ser. Mater. Sci. Eng. 2017, 226, 012034. [Google Scholar] [CrossRef]
- Bian, M. Analysis on the Development of Tokyo Rail Transit and Its Enlightenment to Chengdu. OALib 2021, 8, 1–15. [Google Scholar] [CrossRef]
- Zhou, L.; Wang, F. Strategy for China Intercity-railway Operation Management Model Based on Varied Investors. Transp. Res. Procedia 2017, 25, 3808–3816. [Google Scholar] [CrossRef]
- Tan, J.; Liu, J.; Xu, Y. Quantitative Study on Relationship between Regional Spatial Structure and Regional Intercity Rail Transit Network. IOP Conf. Ser. Mater. Sci. Eng. 2018, 392, 062129. [Google Scholar] [CrossRef]
- Qin, C.; Huo, N.; Chong, Z. Intercity Rail Transit and Integrated Development of the Pearl River Delta Urban Cluster: Based on the Perspective of Network Analysis. Chin. J. Urban Environ. Stud. 2015, 3, 1550024. [Google Scholar] [CrossRef]
- Tao, L.; Shimou, Y.; Youhui, C. Layout Patterns of the Intercity Rail Transit of Urban Agglomerations in China. Prog. Geogr. 2010, 29, 249–256. [Google Scholar]
- Levinson, D.M. Accessibility impacts of high-speed rail. J. Transp. Geogr. 2012, 22, 288–291. [Google Scholar] [CrossRef]
- Kim, T.J. Effects of subways on urban form and structure. Transp. Res. 1978, 12, 231–239. [Google Scholar] [CrossRef]
- Verma, A.; Sudhira, H.S.; Rathi, S.; King, R.; Dash, N. Sustainable urbanization using high speed rail (HSR) in Karnataka, India. Res. Transp. Econ. 2013, 38, 67–77. [Google Scholar] [CrossRef]
- Chen, C.L.; Hall, P. The impacts of high-speed trains on British economic geography: A study of the UK’s InterCity 125/225 and its effects. J. Transp. Geogr. 2011, 19, 689–704. [Google Scholar] [CrossRef]
- Yang, X.Q.; Huang, H.J. Effects of HSR station location on urban spatial structure: A spatial equilibrium analysis for a two-city system. Transp. Res. Part E Logist. Transp. Rev. 2022, 166, 102888. [Google Scholar] [CrossRef]
- Yang, H.; Li, Y. Complex network theory on high-speed transportation systems. In Spatial Synthesis: Computational Social Science and Humanities; Springer: Berlin/Heidelberg, Germany, 2020; pp. 147–162. [Google Scholar]
- Zhang, J.; Hu, F.; Wang, S.; Dai, Y.; Wang, Y. Structural vulnerability and intervention of high speed railway networks. Phys. A Stat. Mech. Appl. 2016, 462, 743–751. [Google Scholar] [CrossRef]
- He, D.; Chen, Z.; Pei, T.; Zhou, J. The regional and local scale evolution of the spatial structure of high-speed railway networks—A case study focused on Beijing-Tianjin-Hebei urban agglomeration. ISPRS Int. J. Geo-Inf. 2021, 10, 543. [Google Scholar] [CrossRef]
- Wang, L.; An, M.; Zhang, Y.; Rana, K. Railway Network Reliability Analysis Based on Key Station Identification Using Complex Network Theory: A Real-World Case Study of High-Speed Rail. In Proceedings of the International Research Conference 2017: Shaping Tomorrow’s Built Environment, Manchester, UK, 11–12 September 2017; pp. 1–14. [Google Scholar]
- Gwilliam, K. Urban transport in developing countries. Transp. Rev. 2003, 23, 197–216. [Google Scholar] [CrossRef]
- Alexopoulos, K.; Wyrowski, L. Sustainable Urban Mobility and Public Transport in UNECE Capitals. In Transportation Trends Economic Series; United Nations: New York, NY, USA, 2015; pp. 1–155. [Google Scholar]
- Shi, J.; Wen, S.; Zhao, X.; Wu, G. Sustainable Development of Urban Rail Transit Networks: A Vulnerability Perspective. Sustainability 2019, 11, 1335. [Google Scholar] [CrossRef]
- Jones, P. The evolution of urban mobility: The interplay of academic and policy perspectives. IATSS Res. 2014, 38, 7–13. [Google Scholar] [CrossRef]
- Mohan, D. Mythologies, metro rail systems and future urban transport. Econ. Polit. Wkly. 2008, 43, 41–53. [Google Scholar]
- Hounsell, N.; Shrestha, B.; Piao, J.; McDonald, M. Review of urban traffic management and the impacts of new vehicle technologies. IET Intell. Transp. Syst. 2009, 3, 419–428. [Google Scholar] [CrossRef]
- Watts, D.J.; Strogatz, S.H. Collective dynamics of ‘small-world’ networks. Nature 1998, 393, 440–442. [Google Scholar] [CrossRef]
- Kurant, M.; Thiran, P. Extraction and analysis of traffic and topologies of transportation networks. Phys. Rev. E 2006, 74, 036114. [Google Scholar] [CrossRef]
- Lordan, O.; Sallan, J.M.; Simo, P. Study of the topology and robustness of airline route networks from the complex network approach: A survey and research agenda. J. Transp. Geogr. 2014, 37, 112–120. [Google Scholar] [CrossRef]
- Lordan, O.; Sallan, J.M.; Simo, P.; Gonzalez-Prieto, D. Robustness of the air transport network. Transp. Res. Part E Logist. Transp. Rev. 2014, 68, 155–163. [Google Scholar] [CrossRef]
- Barthélemy, M. Spatial networks. Phys. Rep. 2011, 499, 1–101. [Google Scholar] [CrossRef]
- Zhang, J.; Liang, Q.; He, X. Study on the complexity of Beijing metro network. J. Beijing Jiaotong Univ. 2013, 37, 78–84. [Google Scholar]
- Zhou, X.; Zhi, L. Research on Topology Structure of Urban Mass Transit Network. J. East China Jiaotong Univ. 2016, 33, 1–8. [Google Scholar] [CrossRef]
- Shen, L.; Xiang, Y.; Wang, Z.; Zhang, T.; Ji, X. Invulnerability Simulation Analysis of Urban Public Transit Compound System. Oper. Res. Manag. Sci. 2017, 26, 105–112. [Google Scholar] [CrossRef]
- Wang, Y.; Jin, C.; Du, J. Research on Accessibility of Nanjing Metro Based on Space-P Complex Network. Geogr. Geo-Inf. Sci. 2020, 36, 87–92. [Google Scholar] [CrossRef]
- Meng, Y.; Tian, X.; Li, Z.; Zhou, W.; Zhou, Z.; Zhong, M. Comparison analysis on complex topological network models of urban rail transit: A case study of Shenzhen Metro in China. Phys. A Stat. Mech. Appl. 2020, 559, 125031. [Google Scholar] [CrossRef]
- Meng, Y.; Tian, X.; Li, Z.; Zhou, W.; Zhou, Z.; Zhong, M. Exploring node importance evolution of weighted complex networks in urban rail transit. Phys. A Stat. Mech. Appl. 2020, 558, 124925. [Google Scholar] [CrossRef]
- Fang, F.; Wang, H.; Chen, K.; Khan, F. Risk analysis of Chongqing urban rail transit network. J. Loss Prev. Process Ind. 2020, 66, 104182. [Google Scholar] [CrossRef]
- Givoni, M.; Rietveld, P. Do cities deserve more railway stations? The choice of a departure railway station in a multiple-station region. J. Transp. Geogr. 2014, 36, 89–97. [Google Scholar] [CrossRef]
- Mandhani, J.; Nayak, J.K.; Parida, M. Interrelationships among service quality factors of Metro Rail Transit System: An integrated Bayesian networks and PLS-SEM approach. Transp. Res. Part A Policy Pract. 2020, 140, 320–336. [Google Scholar] [CrossRef]
- Barabási, A.L.; Albert, R. Emergence of scaling in random networks. Science 1999, 286, 509–512. [Google Scholar] [CrossRef] [PubMed]
- Bababeik, M.; Khademi, N.; Chen, A. Increasing the resilience level of a vulnerable rail network: The strategy of location and allocation of emergency relief trains. Transp. Res. Part E Logist. Transp. Rev. 2018, 119, 110–128. [Google Scholar] [CrossRef]
- Guo, X.; Wang, D.Z.; Wu, J.; Sun, H.; Zhou, L. Mining commuting behavior of urban rail transit network by using association rules. Phys. A Stat. Mech. Appl. 2020, 559, 125094. [Google Scholar] [CrossRef]
- Jin, J.; Li, M.; Wang, Y.; Zhu, L.; Ping, L.; Wang, B.; Li, P. Importance Analysis of Urban Rail Transit Network Station Based on Passenger. J. Intell. Learn. Syst. Appl. 2013, 05, 232–236. [Google Scholar] [CrossRef]
- Ma, D. Evaluation method of importance for nodes in rail transit network based on complex network. Technol. Econ. Areas Commun. 2017, 19, 44–49. [Google Scholar] [CrossRef]
- Ju, Y.; Li, W.; Li, Z.; He, X.; Chen, X. Analysis of key points of urban rail transit network based on passenger flow loading. Technol. Econ. Areas Commun. 2020, 22, 26–31. [Google Scholar] [CrossRef]
- Repolho, H.M.; Church, R.L.; Antunes, A.P. Optimizing station location and fleet composition for a high-speed rail line. Transp. Res. Part E Logist. Transp. Rev. 2016, 93, 437–452. [Google Scholar] [CrossRef]
- Liu, L.; Xu, W.; Han, C. Motif and Superfamily in National Critical Transportation Networks. J. Tongji Univ. Sci. 2013, 41, 53–59. [Google Scholar] [CrossRef]
- Sen, P.; Dasgupta, S.; Chatterjee, A.; Sreeram, P.A.; Mukherjee, G.; Manna, S.S. Small-world properties of the Indian railway network. Phys. Rev. E 2003, 67, 036106. [Google Scholar] [CrossRef] [PubMed]
- Latora, V.; Marchiori, M. Efficient Behavior of Small-World Networks. Phys. Rev. Lett. 2001, 87, 198701. [Google Scholar] [CrossRef]
- Chowell, G.; Hyman, J.M.; Eubank, S.; Castillo-Chavez, C. Scaling laws for the movement of people between locations in a large city. Phys. Rev. E 2003, 68, 066102. [Google Scholar] [CrossRef] [PubMed]
- Montis, A.D.; Barth, M.; Chessa, A.; Vespignani, A.; Costruzioni, S. The structure of Inter-Urban traffic: A weighted network analysis. Environ. Plan. B Plan. Des. 2007, 34, 905–924. [Google Scholar] [CrossRef]
- Guimerà, R.; Díaz-Guilera, A.; Vega-Redondo, F.; Cabrales, A.; Arenas, A. Optimal Network Topologies for Local Search with Congestion. Phys. Rev. Lett. 2002, 89, 248701. [Google Scholar] [CrossRef] [PubMed]
- Barabási, A.L.; Bonabeau, E. Scale-free networks. Sci. Am. 2003, 288, 50–59. [Google Scholar] [CrossRef] [PubMed]
- Seaton, K.A.; Hackett, L.M. Stations, trains and small-world networks. Phys. A Stat. Mech. Appl. 2004, 339, 635–644. [Google Scholar] [CrossRef]
- Derrible, S.; Kennedy, C. The complexity and robustness of metro networks. Phys. A Stat. Mech. Appl. 2010, 389, 3678–3691. [Google Scholar] [CrossRef]
- Feng, S.; Xin, M.; Lv, T.; Hu, B. A novel evolving model of urban rail transit networks based on the local-world theory. Phys. A Stat. Mech. Appl. 2019, 535, 122227. [Google Scholar] [CrossRef]
- Luo, Y.; Qian, D. Construction of Subway and Bus Transport Networks and Analysis of the Network Topology Characteristics. J. Transp. Syst. Eng. Inf. Technol. 2015, 15, 39–44. [Google Scholar] [CrossRef]
- Xu, Z.; Harriss, R. Exploring the structure of the U.S. intercity passenger air transportation network: A weighted complex network approach. GeoJournal 2008, 73, 87–102. [Google Scholar] [CrossRef]
- Kivelä, M.; Arenas, A.; Barthelemy, M.; Gleeson, J.P.; Moreno, Y.; Porter, M.A. Multilayer networks. J. Complex Netw. 2014, 2, 203–271. [Google Scholar] [CrossRef]
- Gómez-Gardeñes, J.; Reinares, I.; Arenas, A.; Floría, L.M. Evolution of Cooperation in Multiplex Networks. Sci. Rep. 2012, 2, 620. [Google Scholar] [CrossRef] [PubMed]
- Li, M.g.; Du, P.; Zhu, Y.t.; Shi, R.j.; Peng, X.j. Effect of Urban Rail Transit Transfer Nodes on Network Performance. J. Transp. Syst. Eng. Inf. Technol. 2015, 15, 48–53. [Google Scholar] [CrossRef]
- Qiao, K.; Zhao, P.; Yao, X.M. Performance Analysis of Urban Rail Transit Network. J. Transp. Syst. Eng. Inf. Technol. 2012, 12, 115–121. [Google Scholar] [CrossRef]
- Oltvai, Z.N.; Barabási, A.L. Life’s Complexity Pyramid. Science 2002, 298, 763–764. [Google Scholar] [CrossRef] [PubMed]
- Vázquez, A.; Dobrin, R.; Sergi, D.; Eckmann, J.P.; Oltvai, Z.N.; Barabási, A.L. The topological relationship between the large-scale attributes and local interaction patterns of complex networks. Proc. Natl. Acad. Sci. USA 2004, 101, 17940–17945. [Google Scholar] [CrossRef] [PubMed]
- Iovanovici, A.; Pellegrini, L.; Moscovici, A.M.; Leba, M. Network motifs uncovering hidden characteristics of urban public transportation. In Proceedings of the 2019 IEEE 15th International Scientific Conference on Informatics, Poprad, Slovakia, 20–22 November 2019; pp. 143–147. [Google Scholar] [CrossRef]
- Ma, Y.; Sallan, J.M.; Lordan, O. Motif analysis of urban rail transit network. Phys. A Stat. Mech. Appl. 2023, 625, 129016. [Google Scholar] [CrossRef]
- Pellegrini, L.; Leba, M.; Iovanovici, A. Characterization of Urban Transportation Networks Using Network Motifs. Acta Electrotech. Inform. 2020, 20, 3–9. [Google Scholar] [CrossRef]
- Wang, Y.; Zhu, D.; Yin, G.; Huang, Z.; Liu, Y. A unified spatial multigraph analysis for public transport performance. Sci. Rep. 2020, 10, 9573. [Google Scholar] [CrossRef] [PubMed]
- Lei, D.; Chen, X.; Cheng, L.; Zhang, L.; Ukkusuri, S.V.; Witlox, F. Inferring temporal motifs for travel pattern analysis using large scale smart card data. Transp. Res. Part C Emerg. Technol. 2020, 120, 102810. [Google Scholar] [CrossRef]
- Alon, U. Network motifs: Theory and experimental approaches. Nat. Rev. Genet. 2007, 8, 450–461. [Google Scholar] [CrossRef] [PubMed]
- Koike, H.; Takayasu, H.; Takayasu, M. Bifurcation and hysteresis in a nonlinear transport model on network motifs. Phys. Rev. Res. 2024, 6, 013059. [Google Scholar] [CrossRef]
- Kashani, Z.R.M.; Ahrabian, H.; Elahi, E.; Nowzari-Dalini, A.; Ansari, E.S.; Asadi, S.; Mohammadi, S.; Schreiber, F.; Masoudi-Nejad, A. Kavosh: A new algorithm for finding network motifs. BMC Bioinform. 2009, 10, 318. [Google Scholar] [CrossRef] [PubMed]
- Ribeiro, P.; Silva, F. G-Tries: A data structure for storing and finding subgraphs. Data Min. Knowl. Discov. 2014, 28, 337–377. [Google Scholar] [CrossRef]
- Khakabimamaghani, S.; Sharafuddin, I.; Dichter, N.; Koch, I.; Masoudi-Nejad, A. QuateXelero: An Accelerated Exact Network Motif Detection Algorithm. PLoS ONE 2013, 8, e68073. [Google Scholar] [CrossRef] [PubMed]
- Wang, T.; Peng, J.; Peng, Q.; Wang, Y.; Chen, J. FSM: Fast and scalable network motif discovery for exploring higher-order network organizations. Methods 2020, 173, 83–93. [Google Scholar] [CrossRef]
- Omidi, S.; Schreiber, F.; Masoudi-Nejad, A. MODA: An efficient algorithm for network motif discovery in biological networks. Genes Genet. Syst. 2009, 84, 385–395. [Google Scholar] [CrossRef]
- Kashtan, N.; Itzkovitz, S.; Milo, R.; Alon, U. Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 2004, 20, 1746–1758. [Google Scholar] [CrossRef] [PubMed]
- Schreiber, F.; Schwobbermeyer, H. MAVisto: A tool for the exploration of network motifs. Bioinformatics 2005, 21, 3572–3574. [Google Scholar] [CrossRef] [PubMed]
- Batagelj, V.; Mrvar, A. Pajek—Analysis and Visualization of Large Networks; Springer: Berlin/Heidelberg, Germany, 2002; pp. 477–478. [Google Scholar] [CrossRef]
- Wernicke, S.; Rasche, F. FANMOD: A tool for fast network motif detection. Bioinformatics 2006, 22, 1152–1153. [Google Scholar] [CrossRef]
- Wernicke, S. Efficient Detection of Network Motifs. IEEE/ACM Trans. Comput. Biol. Bioinforma. 2006, 3, 347–359. [Google Scholar] [CrossRef]
- Fruchterman, T.M.J.; Reingold, E.M. Graph drawing by force-directed placement. Softw. Pract. Exp. 1991, 21, 1129–1164. [Google Scholar] [CrossRef]
- Chen, J.; Hsu, W.; Lee, M.L.; Ng, S.K. NeMoFinder: Dissecting Genome-Wide Protein-Protein Interactions with Meso-Scale Network Motifs. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD ’06, Philadelphia, PA, USA, 20–23 August 2006; p. 106. [Google Scholar] [CrossRef]
- Chen, J.; Hsu, W.; Lee, M.L.; Ng, S.K. Labeling network motifs in protein interactomes for protein function prediction. In Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering, Istanbul, Turkey, 15–20 April 2007; pp. 546–555. [Google Scholar] [CrossRef]
- Grochow, J.A.; Kellis, M. Network Motif Discovery Using Subgraph Enumeration and Symmetry-Breaking. In Research in Computational Molecular Biology; Springer: Berlin/Heidelberg, Germany, 2007; pp. 92–106. [Google Scholar] [CrossRef]
- Li, X.; Stones, R.J.; Wang, H.; Deng, H.; Liu, X.; Wang, G. NetMODE: Network Motif Detection without Nauty. PLoS ONE 2012, 7, e50093. [Google Scholar] [CrossRef]
- Meira, L.A.A.; Maximo, V.R.; Fazenda, A.L.; da Conceicao, A.F. Accelerated Motif Detection Using Combinatorial Techniques. In Proceedings of the 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems, Sorrento, Italy, 25–29 November 2012; pp. 744–753. [Google Scholar] [CrossRef]
- Bavelas, A. A Mathematical Model for Group Structures. Hum. Organ. 1948, 7, 16–30. [Google Scholar] [CrossRef]
- Leavitt, H.J. Some effects of certain communication patterns on group performance. J. Abnorm. Soc. Psychol. 1951, 46, 38–50. [Google Scholar] [CrossRef]
- Miao, L.; Han, C.; Liu, L.; Cao, J. Using motif to characterize building block of scientific collaboration networks. Stud. Sci. Sci. 2012, 30, 1468–1475. [Google Scholar]
- Liu, L.; Luo, T.; Cao, J. A study of the multi-scale scientific collaboration patterns based on complex networks. Sci. Res. Manag. 2019, 40, 191–198. [Google Scholar] [CrossRef]
- Wu, Y.; Tan, Y.; He, X. The Measurement of Network Structure Complexity Based on Motify. Sci. Technol. Manag. Res. 2015, 7, 204–208. [Google Scholar] [CrossRef]
- Liu, L. Building blocks in collaboration network of national emergency management working groups. China Saf. Sci. J. 2016, 26, 133–137. [Google Scholar] [CrossRef]
- Liu, L.; Han, C.; Xu, W. Motifs and superfamilies in critical infrastructure engineering network. Syst. Eng. Theory Pract. 2013, 33, 1335–1344. [Google Scholar]
- Cao, J.; Ding, C.; Shi, B. Motif-based functional backbone extraction of complex networks. Phys. A Stat. Mech. Appl. 2019, 526, 121123. [Google Scholar] [CrossRef]
- Husain, S.S.; Sharma, K.; Kukreti, V.; Chakraborti, A. Identifying the global terror hubs and vulnerable motifs using complex network dynamics. Phys. A Stat. Mech. Appl. 2020, 540, 123113. [Google Scholar] [CrossRef]
- Jin, Y.; Wei, Y.; Xiu, C.; Song, W.; Yang, K. Study on Structural Characteristics of China’s Passenger Airline Network Based on Network Motifs Analysis. Sustainability 2019, 11, 2484. [Google Scholar] [CrossRef]
- Ge, J.; Fu, Q.; Zhang, Q.; Wan, Z. Regional operating patterns of world container shipping network: A perspective from motif identification. Phys. A Stat. Mech. Appl. 2022, 607, 128171. [Google Scholar] [CrossRef]
- Xiao, Y.; Sha, Z. Robust design of complex socio-technical systems against seasonal effects: A network motif-based approach. Des. Sci. 2022, 8, e2. [Google Scholar] [CrossRef]
- Shen, G.; Zhu, D.; Chen, J.; Kong, X. Motif discovery based traffic pattern mining in attributed road networks. Knowl.-Based Syst. 2022, 250, 109035. [Google Scholar] [CrossRef]
- Milo, R.; Itzkovitz, S.; Kashtan, N.; Levitt, R.; Shen-Orr, S.; Ayzenshtat, I.; Sheffer, M.; Alon, U. Superfamilies of Evolved and Designed Networks. Science 2004, 303, 1538–1542. [Google Scholar] [CrossRef] [PubMed]
- Palla, G.; Derényi, I.; Farkas, I.; Vicsek, T. Uncovering the overlapping community structure of complex networks in nature and society. Nature 2005, 435, 814–818. [Google Scholar] [CrossRef] [PubMed]
- Jamakovic, A.; Mahadevan, P.; Vahdat, A.; Boguna, M.; Krioukov, D. How small are building blocks of complex networks. arXiv 2015, arXiv:0908.1143. [Google Scholar]
- Liu, Y.; Liu, L.; Luo, T.; Cao, J. Family Identification of Cooperative Network of Scientists Based on Subgraph. Sci. Technol. Manag. Res. 2019, 7, 7–8. [Google Scholar] [CrossRef]
- Chen, Y.; Xie, N.; Xu, H.; Chen, X.; Lee, D.H. A Multi-Context Aware Human Mobility Prediction Model Based on Motif-Preserving Travel Preference Learning. IEEE Trans. Intell. Transp. Syst. 2024, 25, 2139–2152. [Google Scholar] [CrossRef]
- Shi, S.; Wang, L.; Wang, X. Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition. Phys. A Stat. Mech. Appl. 2022, 606, 128142. [Google Scholar] [CrossRef]
- Miciukiewicz, K.; Vigar, G. Mobility and social cohesion in the splintered city: Challenging technocentric transport research and policy-making practices. Urban Stud. 2012, 49, 1941–1957. [Google Scholar] [CrossRef]
- Barrat, A.; Barthelemy, M.; Vespignani, A. Dynamical Processes on Complex Networks; Cambridge University Press: Cambridge, UK, 2008. [Google Scholar]
- Pu, S.; Zhan, S. Two-stage robust railway line-planning approach with passenger demand uncertainty. Transp. Res. Part E Logist. Transp. Rev. 2021, 152, 102372. [Google Scholar] [CrossRef]
- Zhang, P.; Sun, H.; Qu, Y.; Yin, H.; Jin, J.G.; Wu, J. Model and algorithm of coordinated flow controlling with station-based constraints in a metro system. Transp. Res. Part E Logist. Transp. Rev. 2021, 148, 102274. [Google Scholar] [CrossRef]
- Huang, D.; Liu, Z.; Liu, P.; Chen, J. Optimal transit fare and service frequency of a nonlinear origin-destination based fare structure. Transp. Res. Part E Logist. Transp. Rev. 2016, 96, 1–19. [Google Scholar] [CrossRef]
- Xue, X.; Jia, L.; Wang, Y. Correlation between heterogeneity and vulnerability of subway networks based on passenger flow. J. Rail Transp. Plan. Manag. 2018, 8, 145–157. [Google Scholar] [CrossRef]
- López-López, Á.J.; Pecharromán, R.R.; Fernández-Cardador, A.; Cucala, A.P. Assessment of energy-saving techniques in direct-current-electrified mass transit systems. Transp. Res. Part C Emerg. Technol. 2014, 38, 85–100. [Google Scholar] [CrossRef]
- Watson, I.; Ali, A.; Bayyati, A. The station location and sustainability of high-speed railway systems. Infrastruct. Asset Manag. 2021, 9, 60–72. [Google Scholar] [CrossRef]
- Kim, J.S.; Shin, N. Planning for Railway Station Network Sustainability Based on Node–Place Analysis of Local Stations. Sustainability 2021, 13, 4778. [Google Scholar] [CrossRef]
Motif Detection Methods | Motif Detection Size | References | |
---|---|---|---|
Motif detection tools | Pajek | Three-node motifs | [114] |
Mfinder | 3–7-node motifs | [112] | |
MAVisto | 3–5-node motifs | [113] | |
Fanmod | 3–8-node motifs | [115] | |
Motif detection algorithms | NeMoFinder | 3–12-node motifs | [118] |
LaMoFinder | 3–20-node motifs | [119] | |
Grochow-Kellis | 3–15-node motifs | [120] | |
MODA | 3–9-node motifs | [111] | |
Kavosh | No restrictions | [107] | |
G-Tries | 3–9-node motifs | [108] | |
NetMODE | 3–6-node motifs | [121] | |
Acc-MOTIF | 3–4-node motifs | [122] | |
QuateXelero | 3–13-node motifs | [109] | |
FSM | 5–8-node motifs | [110] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ma, Y.; Sallan, J.M.; Lordan, O. Rail Transit Networks and Network Motifs: A Review and Research Agenda. Sustainability 2024, 16, 3641. https://doi.org/10.3390/su16093641
Ma Y, Sallan JM, Lordan O. Rail Transit Networks and Network Motifs: A Review and Research Agenda. Sustainability. 2024; 16(9):3641. https://doi.org/10.3390/su16093641
Chicago/Turabian StyleMa, Yunfang, Jose M. Sallan, and Oriol Lordan. 2024. "Rail Transit Networks and Network Motifs: A Review and Research Agenda" Sustainability 16, no. 9: 3641. https://doi.org/10.3390/su16093641