基于复杂网络和空间信息嵌入的常规公交-地铁复合网络脆弱性分析
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摘要
由于传统常规公交-地铁复合网络静态脆弱性研究未考虑常规公交-地铁耦合站点的关键作用,同时缺少明确的耦合站点脆弱性规则,基于复杂网络理论和空间信息嵌入,给出基于ArcGIS的耦合站点定量化判定规则和流程化处理方法,实现大规模公交网络耦合站点的批量精准识别.给出考虑常规公交站点和地铁站点差异性的耦合站点脆弱性规则,使复合网络更接近耦合系统,建立复合网络脆弱性改进分析模型.最后,对南京市常规公交-地铁复合网络脆弱性进行实例仿真分析,验证模型的可行性.研究结果表明:耦合站点能显著降低常规公交网络、地铁网络以及复合网络的脆弱性;随着耦合站点耦合半径的增大,复合网络及各子网络应对随机攻击和蓄意攻击的能力均增大;耦合站点对地铁网络应对蓄意攻击能力的提升优于常规公交网络;与网络有效性相比,网络最大连通率具有相似的公交网络脆弱性度量能力,且计算效率大幅提高,应被优先选用为大规模网络脆弱性度量指标.
The traditional vulnerability study of bus-metro composite networks does not consider the key role of bus-metro coupled stations, and lacks clear vulnerability rules of coupled stations. Based on the complex network theory and spatial information embedding, the ArcGIS software based quantitatively judging rule and processing method is proposed, realizing the batch identification of the coupled stations in a large-scale public transit network. Then, the coupled station vulnerability rule considering the difference between the bus station and the metro station is proposed to make the composite network closer to a coupled system, thereby establishing an improved vulnerability analysis model. Finally, a vulnerability simulation analysis of Nanjing's bus-metro composite network was carried out to verify the feasibility of the proposed model. The results show that the coupled station can significantly reduce the vulnerability of the bus network, metro network and composite network; with the increase of the coupled radius of coupled stations, the ability of the composite network and each sub-network to deal with random attacks and deliberate attacks increases; the effect of coupled stations on improving the ability of the metro network to deal with deliberate attacks is better than bus network. The network maximum connectivity ratio and network efficiency have a similar network vulnerability measurement capability, but the computational efficiency of the former is improved, thus it should be preferentially selected as a vulnerability indicator of large-scale networks.
The traditional vulnerability study of bus-metro composite networks does not consider the key role of bus-metro coupled stations, and lacks clear vulnerability rules of coupled stations. Based on the complex network theory and spatial information embedding, the ArcGIS software based quantitatively judging rule and processing method is proposed, realizing the batch identification of the coupled stations in a large-scale public transit network. Then, the coupled station vulnerability rule considering the difference between the bus station and the metro station is proposed to make the composite network closer to a coupled system, thereby establishing an improved vulnerability analysis model. Finally, a vulnerability simulation analysis of Nanjing's bus-metro composite network was carried out to verify the feasibility of the proposed model. The results show that the coupled station can significantly reduce the vulnerability of the bus network, metro network and composite network; with the increase of the coupled radius of coupled stations, the ability of the composite network and each sub-network to deal with random attacks and deliberate attacks increases; the effect of coupled stations on improving the ability of the metro network to deal with deliberate attacks is better than bus network. The network maximum connectivity ratio and network efficiency have a similar network vulnerability measurement capability, but the computational efficiency of the former is improved, thus it should be preferentially selected as a vulnerability indicator of large-scale networks.
引文
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[3] Estrada M,Mensión J,Aymamí J M,et al.Bus control strategies in corridors with signalized intersections[J].Transportation Research Part C:Emerging Technologies,2016,71:500-520.DOI:10.1016/j.trc.2016.08.013.
[4] He S X.An anti-bunching strategy to improve bus schedule and headway reliability by making use of the available accurate information[J].Computers & Industrial Engineering,2015,85:17-32.DOI:10.1016/j.cie.2015.03.004.
[5] Fonzone A,Schm?cker J D,Liu R H.A model of bus bunching under reliability-based passenger arrival patterns[J].Transportation Research Part C:Emerging Technologies,2015,59:164-182.DOI:10.1016/j.trc.2015.05.020.
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[7] Watts D J,Strogatz S H.Collective dynamics of ‘small-world’ networks[J].Nature,1998,393(6684):440-442.DOI:10.1038/30918.
[8] Barabási A.Emergence of scaling in random networks[J].Science,1999,286(5439):509-512.DOI:10.1126/science.286.5439.509.
[9] Albert R,Jeong H,Barabási A L.Error and attack tolerance of complex networks[J].Nature,2000,406(6794):378-382.DOI:10.1038/35019019.
[10] Mattsson L G,Jenelius E.Vulnerability and resilience of transport systems—A discussion of recent research[J].Transportation Research Part A:Policy and Practice,2015,81:16-34.DOI:10.1016/j.tra.2015.06.002.
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[13] Zhang L,Lu J,Fu B B,et al.A quantitatively controllable mesoscopic reliability model of an interdependent public transit network considering congestion,time-delay interaction and self-organization effects[J].Nonlinear Dynamics,2019,96(2):933-958.DOI:10.1007/s11071-019-04831-y.
[14] Zhang L,Lu J,Fu B B,et al.A cascading failures model of weighted bus transit route network under route failure perspective considering link prediction effect[J].Physica A:Statistical Mechanics and Its Applications,2019,523:1315-1330.DOI:10.1016/j.physa.2019.04.122.
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[18] 孙凤英,张志锋.基于复杂网络的地铁-地面公交网络特性分析[J].森林工程,2015,31(4):119-122.DOI:10.3969/j.issn.1001-005X.2015.04.026.Sun F Y,Zhang Z F.Analysis on the subway-ground bus network properties based on the complex network[J].Forest Engineering,2015,31(4):119-122.DOI:10.3969/j.issn.1001-005X.2015.04.026.(in Chinese)
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[22] Sienkiewicz J,Ho?yst J A.Statistical analysis of 22 public transport networks in Poland[J].Physical Review E,2005,72(4):046127.DOI:10.1103/physreve.72.046127.
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