认知城市轨道交通的韧性——概念、方法与趋势

魏运, 杨欣, 肖骁, 马智傲, 朱天雷, 豆飞, 吴建军, Anthony Chen, 高自友

工程(英文) ›› 2024, Vol. 41 ›› Issue (10) : 7-18.

PDF(2779 KB)
PDF(2779 KB)
工程(英文) ›› 2024, Vol. 41 ›› Issue (10) : 7-18. DOI: 10.1016/j.eng.2024.01.022
研究论文
Review

认知城市轨道交通的韧性——概念、方法与趋势

作者信息 +

Understanding the Resilience of Urban Rail Transit: Concepts, Reviews, and Trends

Author information +
History +

Abstract

As the scale of urban rail transit (URT) networks expands, the study of URT resilience is essential for safe and efficient operations. This paper presents a comprehensive review of URT resilience and highlights potential trends and directions for future research. First, URT resilience is defined by three primary abilities: absorption, resistance, and recovery, and four properties: robustness, vulnerability, rapidity, and redundancy. Then, the metrics and assessment approaches for URT resilience were summarized. The metrics are divided into three categories: topology-based, characteristic-based, and performance-based, and the assessment methods are divided into four categories: topological, simulation, optimization, and data-driven. Comparisons of various metrics and assessment approaches revealed that the current research trend in URT resilience is increasingly favoring the integration of traditional methods, such as conventional complex network analysis and operations optimization theory, with new techniques like big data and intelligent computing technology, to accurately assess URT resilience. Finally, five potential trends and directions for future research were identified: analyzing resilience based on multisource data, optimizing train diagram in multiple scenarios, accurate response to passenger demand through new technologies, coupling and optimizing passenger and traffic flows, and optimal line design.

Keywords

Urban rail transit / Resilience assessment / Resilience improvement / Network disruption

引用本文

导出引用
魏运, 杨欣, 肖骁. 认知城市轨道交通的韧性——概念、方法与趋势. Engineering. 2024, 41(10): 7-18 https://doi.org/10.1016/j.eng.2024.01.022

参考文献

[1]
S. Hayes, C. Desha, M. Burke, M. Gibbs, M. Chester. Leveraging socio-ecological resilience theory to build climate resilience in transport infrastructure. Transp Rev, 39 (2019), pp. 677-699.
[2]
J.S. Cañavera-Herrera, J. Tang, T. Nochta, J.M. Schooling. On the relation between ‘resilience’ and ‘smartness’: a critical review. Int J Disaster Risk Reduct, 75 (2022), Article 102970.
[3]
M. Lacinák. Resilience of the smart transport system—risks and aims. Transp Res Procedia, 55 (2021), pp. 1635-1640.
[4]
M. Hasselwander, T. Tamagusko, J.F. Bigotte, A. Ferreira, A. Mejia, E.J.S. Ferranti. Building back better: the COVID-19 pandemic and transport policy implications for a developing megacity. Sustain Cities Soc, 69 (2021), Article 102864.
[5]
J. Chakwizira. Stretching resilience and adaptive transport systems capacity in South Africa: imperfect or perfect attempts at closing COVID-19 policy and planning emergent gaps. Transp Policy, 125 (2022), pp. 127-150.
[6]
Z. Wu, Y. Zhang. Integrated network design and demand forecast for on-demand urban air mobility. Engineering, 7 (2021), pp. 473-487.
[7]
O. Cats, G.J. Koppenol, M. Warnier. Robustness assessment of link capacity reduction for complex networks: application for public transport systems. Reliab Eng Syst Saf, 167 (2017), pp. 544-553.
[8]
Z. Zeng, S. Wang, X. Qu. Consolidating bus charger deployment and feet management for public transit electrification: a life-cycle cost analysis framework. Engineering, 21 (2023), pp. 45-60.
[9]
S. Mudigonda, K. Ozbay, B. Bartin. Evaluating the resilience and recovery of public transit system using big data: case study from New Jersey. J Transp Saf Secur, 11 (2019), pp. 491-519.
[10]
K.C. Sinha, S. Labi, B.R.D.K. Agbelie. Transportation infrastructure asset management in the new millennium: continuing issues, and emerging challenges and opportunities. Transp Transp Sci, 13 (2017), pp. 591-606.
[11]
Y. Zhou, J. Wang, H. Yang. Resilience of transportation systems: concepts and comprehensive review. IEEE Trans Intell Transp Syst, 20 (2019), pp. 4262-4276.
[12]
S. Pan, H. Yan, J. He, Z. He. Vulnerability and resilience of transportation systems: a recent literature review. Phys Stat Mech Its Appl, 581 (2021), Article 126235.
[13]
N. Bešinović. Resilience in railway transport systems: a literature review and research agenda. Transp Rev, 40 (2020), pp. 457-478.
[14]
T. Weis-Fogh. Thermodynamic properties of resilin, a rubber-like protein. J Mol Biol, 3 (1961), pp. 520-531.
[15]
C.S. Holling. Resilience and stability of ecological systems. Annu Rev Ecol Syst, 4 (1973), pp. 1-23.
[16]
D.N.J. Mostert, S.H. Von Solms. A technique to include computer security, safety, and resilience requirements as part of the requirements specification. J Syst Softw, 31 (1995), pp. 45-53.
[17]
S. Farber. Economic resilience and economic policy. Ecol Econ, 15 (1995), pp. 105-107.
[18]
D.R. Godschalk. Urban hazard mitigation: creating resilient cities. Nat Hazards Rev, 4 (2003), pp. 136-143.
[19]
S. Meerow, J.P. Newell, M. Stults. Defining urban resilience: a review. Landsc Urban Plan, 147 (2016), pp. 38-49.
[20]
D.E. Alexander. Resilience and disaster risk reduction: an etymological journey. Nat Hazards Earth Syst Sci, 13 (2013), pp. 2707-2716.
[21]
R. Twumasi-Boakye, J.O. Sobanjo. A computational approach for evaluating post-disaster transportation network resilience. Sustain Resilient Infrastruct, 6 (2021), pp. 235-251.
[22]
P.I. Dimayuga, T. Galloway, M.J. Widener, S. Saxe. Air transportation as a central component of remote community resilience in northern Ontario, Canada. Sustain Resilient Infrastruct, 7 (2022), pp. 624-637.
[23]
Z. Ma, X. Yang, J. Wu, A. Chen, Y. Wei, Z. Gao. Measuring the resilience of an urban rail transit network: a multi-dimensional evaluation model. Transp Policy, 129 (2022), pp. 38-50.
[24]
A. Kaviani, R.G. Thompson, A. Rajabifard. Improving regional road network resilience by optimised traffic guidance. Transp Transp Sci, 13 (2017), pp. 794-828.
[25]
D.V. Achillopoulou, S.A. Mitoulis, S.A. Argyroudis, Y. Wang. Monitoring of transport infrastructure exposed to multiple hazards: a roadmap for building resilience. Sci Total Environ, 746 (2020), Article 141001.
[26]
A. Azadeh, V. Salehi, M. Kianpour. Performance evaluation of rail transportation systems by considering resilience engineering factors: Tehran railway electrification system. Transp Lett, 10 (2018), pp. 12-25.
[27]
R. Faturechi, E. Miller-Hooks. Measuring the performance of transportation infrastructure systems in disasters: a comprehensive review. J Infrastruct Syst, 21 (2015), p. 04014025.
[28]
A. Reggiani, P. Nijkamp, D. Lanzi. Transport resilience and vulnerability: the role of connectivity. Transp Res Part Policy Pract, 81 (2015), pp. 4-15.
[29]
M.Z. Serdar, M. Koç, S.G. Al-Ghamdi. Urban transportation networks resilience: indicators, disturbances, and assessment methods. Sustain Cities Soc, 76 (2022), Article 103452.
[30]
L.G. Mattsson, E. Jenelius. Vulnerability and resilience of transport systems—a discussion of recent research. Transp Res Part Policy Pract, 81 (2015), pp. 16-34.
[31]
Y. Meng, X. Tian, Z. Li, W. Zhou, Z. Zhou, M. Zhong. Comparison analysis on complex topological network models of urban rail transit: a case study of Shenzhen metro in China. Phys Stat Mech Its Appl, 559 (2020), Article 125031.
[32]
D. Zhang, F. Du, H. Huang, F. Zhang, B.M. Ayyub, M. Beer. Resiliency assessment of urban rail transit networks: Shanghai metro as an example. Saf Sci, 106 (2018), pp. 230-243.
[33]
F. Ma, Y. Liang, K.F. Yuen, Q. Sun, Y. Zhu, Y. Wang, et al. Assessing the vulnerability of urban rail transit network under heavy air pollution: a dynamic vehicle restriction perspective. Sustain Cities Soc, 52 (2020), Article 101851.
[34]
B. Berche, C. Von Ferber, T. Holovatch, Yu. Holovatch. Resilience of public transport networks against attacks. Eur Phys J B, 71 (2009), pp. 125-137.
[35]
M. Liu, J. Agarwal, D. Blockley. Vulnerability of road networks. Civ Eng Environ Syst, 33 (2016), pp. 147-175.
[36]
S. Derrible, C. Kennedy. The complexity and robustness of metro networks. Phys Stat Mech Its Appl, 389 (2010), pp. 3678-3691.
[37]
Y. Zhang, B.M. Ayyub, Y. Saadat, D. Zhang, H. Huang. A double-weighted vulnerability assessment model for metrorail transit networks and its application in Shanghai metro. Int J Crit Infrastruct Prot, 29 (2020), Article 100358.
[38]
Q.C. Lu, S. Lin. Vulnerability analysis of urban rail transit network within multi-modal public transport networks. Sustainability, 11 (2019), p. 2109.
[39]
Li M, Wang H, Wang H. Resiliency assessment of urban rail transit networks: a case study of Shanghai metro. 2017 IEEE 20th International Conference on Intelligent Transportation System (ITSC); 2017 Oct 16-19; Yokohama, Japan. IEEE; 2017, p. 620-5.
[40]
Y. Chen, K. An. Integrated optimization of bus bridging routes and timetables for rail disruptions. Eur J Oper Res, 295 (2021), pp. 484-498.
[41]
X. Wang, J.G. Jin, L. Sun. Real-time dispatching of operating buses during unplanned disruptions to urban rail transit system. Transp Res Part C Emerg Technol, 139 (2022), Article 103696.
[42]
Z. Liu, H. Chen, E. Liu, Q. Zhang. Evaluating the dynamic resilience of the multi-mode public transit network for sustainable transport. J Clean Prod, 348 (2022), Article 131350.
[43]
A. De-Los-Santos, G. Laporte, J.A. Mesa, F. Perea. Evaluating passenger robustness in a rail transit network. Transp Res Part C Emerg Technol, 20 (2012), pp. 34-46.
[44]
W. Jing, X. Xu, Y. Pu. Route redundancy-based approach to identify the critical stations in metro networks: a mean-excess probability measure. Reliab Eng Syst Saf, 204 (2020), Article 107204.
[45]
X. Xu, A. Chen, S. Jansuwan, C. Yang, S. Ryu. Transportation network redundancy: complementary measures and computational methods. Transp Res Part B Methodol, 114 (2018), pp. 68-85.
[46]
D. Sun, Y. Zhao, Q.C. Lu. Vulnerability analysis of urban rail transit networks: a case study of Shanghai, China. Sustainability, 7 (2015), pp. 6919-6936.
[47]
C. Ying, A.H.F. Chow, H.T.M. Nguyen, K.S. Chin. Multi-agent deep reinforcement learning for adaptive coordinated metro service operations with flexible train composition. Transp Res Part B Methodol, 161 (2022), pp. 36-59.
[48]
S. Zhang, H.K. Lo. Metro disruption management: optimal initiation time of substitute bus services under uncertain system recovery time. Transp Res Part C Emerg Technol, 97 (2018), pp. 409-427.
[49]
M. Li, X. Zhou, Y. Wang, L. Jia, M. An. Modelling cascade dynamics of passenger flow congestion in urban rail transit network induced by train delay. Alex Eng J, 61 (2022), pp. 8797-8807.
[50]
W. Huang, B. Zhou, Y. Yu, H. Sun, P. Xu. Using the disaster spreading theory to analyze the cascading failure of urban rail transit network. Reliab Eng Syst Saf, 215 (2021), Article 107825.
[51]
M. D’Lima, F. Medda. A new measure of resilience: an application to the London underground. Transp Res Part Policy Pract, 81 (2015), pp. 35-46.
[52]
G. Nian, F. Chen, Z. Li, Y. Zhu, D.Sun (Jian). Evaluating the alignment of new metro line considering network vulnerability with passenger ridership. Transp Transp Sci, 15 (2019), pp. 1402-1418.
[53]
J. Chen, J. Liu, Q. Peng, Y. Yin. Resilience assessment of an urban rail transit network: a case study of Chengdu subway. Phys Stat Mech Its Appl, 586 (2022), Article 126517.
[54]
M. Bruneau, S.E. Chang, R.T. Eguchi, G.C. Lee, T.D. O’Rourke, A.M. Reinhorn, et al. A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq Spectra, 19 (2003), pp. 733-752.
[55]
J. Tang, L. Xu, C. Luo, T.S.A. Ng. Multi-disruption resilience assessment of rail transit systems with optimized commuter flows. Reliab Eng Syst Saf, 214 (2021), Article 107715.
[56]
C. Cong, X. Li, S. Yang, Q. Zhang, L. Lu, Y. Shi.Impact estimation of unplanned urban rail disruptions on public transport aassengers: a multi-agent based simulation approach. Int J Environ Res Public Health, 19 (2022), p. 9052.
[57]
J. Jia, Y. Chen, Y. Wang, T. Li, Y. Li.A new global method for identifying urban rail transit key station during COVID-19: a case study of Beijing, China. Phys Stat Mech Its Appl, 565 (2021), Article 125578.
[58]
L. Hong, Y. Yan, M. Ouyang, H. Tian, X. He. Vulnerability effects of passengers’ intermodal transfer distance preference and subway expansion on complementary urban public transportation systems. Reliab Eng Syst Saf, 158 (2017), pp. 58-72.
[59]
A. Nagurney, Q. Qiang. A relative total cost index for the evaluation of transportation network robustness in the presence of degradable links and alternative travel behavior. Int Trans Oper Res, 16 (2009), pp. 49-67.
[60]
R. Zimmerman, C.E. Restrepo, J. Sellers, A. Amirapu, T.R. Pearson, H.B. Kates. Multimodal transit connectivity for flexibility in extreme Events. Transp Res Rec J Transp Res Board, 2532 (2015), pp. 64-73.
[61]
E. Frutos Bernal,A. Martín Del Rey. Study of the structural and robustness characteristics of Madrid metro network. Sustainability, 11 (2019), p. 3486.
[62]
Z. Wu, J. Sun, R. Xu. Calculating vulnerability index of urban metro systems based on satisfied route. Phys Stat Mech Its Appl, 531 (2019), Article 121722.
[63]
K. Qiao, P. Zhao, X. Yao. Performance analysis of urban rail transit network. J Transp Syst Eng Inf Technol, 12 (2012), pp. 115-121.
[64]
P. Angeloudis, D. Fisk. Large subway systems as complex networks. Phys Stat Mech Its Appl, 367 (2006), pp. 553-558.
[65]
H.Y. Chan, A. Chen, G. Li, X. Xu, W. Lam. Evaluating the value of new metro lines using route diversity measures: the case of Hong Kong’s mass transit railway system. J Transp Geogr, 91 (2021), Article 102945.
[66]
Y. Yang, Y. Liu, M. Zhou, F. Li, C. Sun. Robustness assessment of urban rail transit based on complex network theory: a case study of the Beijing subway. Saf Sci, 79 (2015), pp. 149-162.
[67]
X. Yang, A. Chen, B. Ning, T. Tang. Measuring route diversity for urban rail transit networks: a case study of the Beijing metro network. IEEE Trans Intell Transp Syst, 18 (2017), pp. 259-268.
[68]
H. Cai, J. Zhu, C. Yang, W. Fan, T. Xu.Vulnerability analysis of metro network incorporating flow impact and capacity constraint after a disaster. J Urban Plan Dev, 143 (2017), p. 04016031.
[69]
X. Xiao, L. Jia, Y. Wang, C. Zhang. Topological characteristics of metro networks based on transfer constraint. Phys Stat Mech Its Appl, 532 (2019), Article 121811.
[70]
O. Cats, P. Krishnakumari. Metropolitan rail network robustness. Phys Stat Mech Its Appl, 549 (2020), Article 124317.
[71]
J. Zhang, H. Che, F. Chen, W. Ma, Z. He. Short-term origin-destination demand prediction in urban rail transit systems: a channel-wise attentive split-convolutional neural network method. Transp Res Part C Emerg Technol, 124 (2021), Article 102928.
[72]
W. Zhu, K. Liu, M. Wang, X. Yan. Enhancing robustness of metro networks using strategic defense. Phys Stat Mech Its Appl, 503 (2018), pp. 1081-1091.
[73]
E. Hassannayebi, A. Sajedinejad, S. Mardani. Urban rail transit planning using a two-stage simulation-based optimization approach. Simul Model Pract Theory, 49 (2014), pp. 151-166.
[74]
Y. Fan, F. Zhang, S. Jiang, C. Gao, Z. Du, Z. Wang, et al. Dynamic robustness analysis for subway network with spatiotemporal characteristic of passenger flow. IEEE Access, 8 (2020), pp. 45544-45555.
[75]
L. Cadarso, Á. Marín, G. Maróti. Recovery of disruptions in rapid transit networks. Transp Res Part E Logist Transp Rev, 53 (2013), pp. 15-33.
[76]
J.G. Jin, L.C. Tang, L. Sun, D.H. Lee. Enhancing metro network resilience via localized integration with bus services. Transp Res Part E Logist Transp Rev, 63 (2014), pp. 17-30.
[77]
J. Chen, J. Liu, Q. Peng, Y. Yin. Strategies to enhance the resilience of an urban rail transit network. Transp Res Rec J Transp Res Board, 2676 (2022), pp. 342-354.
[78]
Y. Shen, G. Ren, B. Ran. Cascading failure analysis and robustness optimization of metro networks based on coupled map lattices: a case study of Nanjing, China. Transportation, 48 (2021), pp. 537-553.
[79]
H. Sun, J. Wu, L. Wu, X. Yan, Z. Gao. Estimating the influence of common disruptions on urban rail transit networks. Transp Res Part Policy Pract, 94 (2016), pp. 62-75.
[80]
D. Li, B. Fu, Y. Wang, G. Lu, Y. Berezin, H.E. Stanley, et al. Percolation transition in dynamical traffic network with evolving critical bottlenecks. Proc Natl Acad Sci, 112 (2015), pp. 669-672.
[81]
G. Zeng, D. Li, S. Guo, L. Gao, Z. Gao, H.E. Stanley, et al. Switch between critical percolation modes in city traffic dynamics. Proc Natl Acad Sci, 116 (2019), pp. 23-28.
[82]
G. Zeng, J. Gao, L. Shekhtman, S. Guo, W. Lv, J. Wu, et al. Multiple metastable network states in urban traffic. Proc Natl Acad Sci, 117 (2020), pp. 17528-17534.
[83]
J. Wang, Y. Li, J. Liu, K. He, P. Wang.Vulnerability analysis and passenger source prediction in urban rail transit networks. PLOS ONE, 8 (2013), p. 8.
[84]
L. Sun, Y. Huang, Y. Chen, L. Yao.Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China. Transp Res Part Policy Pract, 108 (2018), pp. 12-24.
[85]
Y. Deng, Q. Li, Y. Lu. A research on subway physical vulnerability based on network theory and FMECA. Saf Sci, 80 (2015), pp. 127-134.
[86]
Q.C. Lu. Modeling network resilience of rail transit under operational incidents. Transp Res Part Policy Pract, 117 (2018), pp. 227-237.
[87]
J. Yin, X. Ren, R. Liu, T. Tang, S. Su. Quantitative analysis for resilience-based urban rail systems: a hybrid knowledge-based and data-driven approach. Reliab Eng Syst Saf, 219 (2022), Article 108183.
[88]
Z. Xu, S.S. Chopra. Network-based assessment of metro infrastructure with a spatial-temporal resilience cycle framework. Reliab Eng Syst Saf, 223 (2022), Article 108434.
[89]
M. Müller-Hannemann, R. Rückert, A. Schiewe, A. Schöbel. Estimating the robustness of public transport schedules using machine learning. Transp Res Part C Emerg Technol, 137 (2022), Article 103566.
[90]
Y. Li, S.R. Nan, Y. Guo, C.H. Zhu, D. Li. Detection and analysis of transfer time in urban rail transit system using WIFI data. Transp Lett, 15 (2023), pp. 634-644.
[91]
F. Leurent, X. Xie. Exploiting smartcard data to estimate distributions of passengers’ walking speed and distances along an urban rail transit line. Transp Res Procedia, 22 (2017), pp. 45-54.
[92]
A. Esposito Amideo, S. Starita, M.P. Scaparra. Assessing protection strategies for urban rail transit systems: a case-study on the central London underground. Sustainability, 11 (2019), p. 6322.
[93]
X. Yang, Q. Xue, M. Ding, J. Wu, Z. Gao. Short-term prediction of passenger volume for urban rail systems: a deep learning approach based on smart-card data. Int J Prod Econ, 231 (2021), Article 107920.
[94]
J. Zhang, Z. Wang, S. Wang, W. Shao, X. Zhao, W. Liu. Vulnerability assessments of weighted urban rail transit networks with integrated coupled map lattices. Reliab Eng Syst Saf, 214 (2021), Article 107707.
[95]
Q. Zhan, Y. Jia, Z. Zheng, Q. Zhang, L. Luo. Associations of land use around rail transit stations with jobs—housing distribution of rail commuters from smart-card data. Geo-Spat Inf Sci, 26 (2023), pp. 346-361.
[96]
D. Li, T. Zhang, X. Dong, Y. Yin, J. Cao. Trade-off between efficiency and fairness in timetabling on a single urban rail transit line under time-dependent demand condition. Transp B Transp Dyn, 7 (2019), pp. 1203-1231.
[97]
Y. He, Y. Zhao, K.L. Tsui.Short-term forecasting of origin-destination matrix in transit system via a deep learning approach. Transp Transp Sci, 19 (2023), p. 2033348.
[98]
Y. Lu, L. Yang, K. Yang, Z. Gao, H. Zhou, F. Meng, et al. A distributionally robust optimization method for passenger flow control strategy and train scheduling on an urban rail transit line. Engineering, 12 (2022), pp. 202-220.
[99]
S. Saidi, S.C. Wirasinghe, L. Kattan, S. Esmaeilnejad. A generalized framework for complex urban rail transit network analysis. Transp Transp Sci, 13 (2017), pp. 874-892.
[100]
H. Jin, S. Chen, X. Ran, G. Liu, S. Liu. Column generation-based optimum crew scheduling incorporating network representation for urban rail transit systems. Comput Ind Eng, 169 (2022), Article 108155.
[101]
Z. Cao, A. Ceder Avi, D. Li, S. Zhang. Robust and optimized urban rail timetabling using a marshaling plan and skip-stop operation. Transp Transp Sci, 16 (2020), pp. 1217-1249.
[102]
L. Zhu, S. Li, Y. Hu, B. Jia. Robust collaborative optimization for train timetabling and short-turning strategy in urban rail transit systems. Transp B Transp Dyn, 11 (2023), pp. 147-173.
[103]
N. Vishnu, S. Kameshwar, J.E. Padgett. Road transportation network hazard sustainability and resilience: correlations and comparisons. Struct Infrastruct Eng, 19 (2021), pp. 345-365.
[104]
J.G. Jin, K.M. Teo, A.R. Odoni. Optimizing bus bridging services in response to disruptions of urban transit rail networks. Transp Sci, 50 (2016), pp. 790-804.
[105]
P. Shang, L. Yang, Y. Yao Carol, L. Tong, S. Yang, X. Mi. Integrated optimization model for hierarchical service network design and passenger assignment in an urban rail transit network: a Lagrangian duality reformulation and an iterative layered optimization framework based on forward-passing and backpropagation. Transp Res Part C Emerg Technol, 144 (2022), Article 103877.
[106]
P. Zhang, X. Yang, J. Wu, H. Sun, Y. Wei, Z. Gao. Coupling analysis of passenger and train flows for a large-scale urban rail transit system. Front Eng Manag, 10 (2023), pp. 250-261.
[107]
M.E. Nieves-Meléndez, J.M. De La Garza. Resilience frameworks instantiated to vehicular traffic applications. Sustain Resilient Infrastruct, 2 (2017), pp. 75-85.
[108]
N. Bešinović, R. Ferrari Nassar, C. Szymula. Resilience assessment of railway networks: combining infrastructure restoration and transport management. Reliab Eng Syst Saf, 224 (2022), Article 108538.
[109]
S. Somy, R. Shafaei, R. Ramezanian. Resilience-based mathematical model to restore disrupted road-bridge transportation networks. Struct Infrastruct Eng, 18 (2022), pp. 1334-1349.
[110]
J.T. Aparicio, E. Arsenio, R. Henriques. Assessing robustness in multimodal transportation systems: a case study in Lisbon. Eur Transp Res Rev, 14 (2022), p. 28.
[111]
C. Malandri, L. Mantecchini, M.N. Postorino. A comprehensive approach to assess transportation system resilience towards disruptive events. Case study on airside airport systems. Transp Policy, 139 (2023), pp. 109-122.
[112]
S.A. Markolf, C. Hoehne, A. Fraser, M.V. Chester, B.S. Underwood. Transportation resilience to climate change and extreme weather events—beyond risk and robustness. Transp Policy, 74 (2019), pp. 174-186.
[113]
A. Kizhakkedath, K. Tai. Vulnerability analysis of critical infrastructure network. Int J Crit Infrastruct Prot, 35 (2021), Article 100472.
[114]
E. Borowski, J. Soria, J. Schofer, A. Stathopoulos. Does ridesourcing respond to unplanned rail disruptions? A natural experiment analysis of mobility resilience and disparity. Cities, 140 (2023), Article 104439.
[115]
E. Hassannayebi, S.H. Zegordi, M.R. Amin-Naseri, M. Yaghini. Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach. Oper Res, 17 (2017), pp. 435-477.
[116]
B. Van Wee. Accessible accessibility research challenges. J Transp Geogr, 51 (2016), pp. 9-16.
[117]
J.B. Ingvardson, O.A. Nielsen. How urban density, network topology and socio-economy influence public transport ridership: empirical evidence from 48 European metropolitan areas. J Transp Geogr, 72 (2018), pp. 50-63.
PDF(2779 KB)

Accesses

Citation

Detail

段落导航
相关文章

/