Resilience in multilayer transportation infrastructure networks: a review and conceptual framework for equity-based assessment

References

• Adjetey-Bahun, K., Birregah, B., Chatelet, E., & Laurens-Fonseca, E. (2014). A simulation-based approach to quantifying resilience indicators in a mass transportation system. 11th International Conference on Information Systems for Crisis Response and Management. Pennsylvania, USA.
   Google Scholar
• Adjetey-Bahun, K., Birregah, B., Châtelet, E., & Planchet, J.-L. (2016). A model to quantify the resilience of mass railway transportation systems. Reliability Engineering & System Safety, 153, 1–14. https://doi.org/10.1016/j.ress.2016.03.015
   Web of Science ®Google Scholar
• Ahmed, S., & Dey, K. (2020). Resilience modeling concepts in transportation systems: A compre- hensive review based on mode, and modeling techniques. Journal of Infrastructure Preservation and Resilience, 8(1). https://doi.org/10.1186/s43065-020-00008-9
   Google Scholar
• Alessandretti, L., Natera Orozco, L. G., Saberi, M., Szell, M., & Battiston, F. (2023). Multimodal urban mobility and multilayer transport networks. Environment and Planning B: Urban Analytics and City Science, 50(8), 2038–2070. https://doi.org/10.1177/23998083221108190
   Google Scholar
• Aleta, A., Meloni, S., & Moreno, Y. (2017). A multilayer perspective for the analysis of urban transportation systems. Scientific Reports, 7(1), 1–9. https://doi.org/10.1038/srep44359
   PubMedGoogle Scholar
• Alipour, A., & Shafei, B. (2016a). Assessment of postearthquake losses in a network of aging bridges. Journal of Infrastructure Systems, 22(2), 04015023. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000253
   Web of Science ®Google Scholar
• Alipour, A., & Shafei, B. (2016b). Seismic resilience of transportation networks with deteriorating components. Journal of Structural Engineering, 142(8), C4015015. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001399
   Web of Science ®Google Scholar
• Almoghathawi, Y., & Barker, K. (2019). Component importance measures for interdependent infrastructure network resilience. Computers & Industrial Engineering, 133, 153–164. https://doi.org/10.1016/j.cie.2019.05.001
   Web of Science ®Google Scholar
• Ansari Esfeh, M., Kattan, L., Lam, W. H. K., Salari, M., & Ansari Esfe, R. (2022). Road network vulnerability analysis considering the probability and consequence of disruptive events: A spatiotemporal incident impact approach. Transportation Research Part C: Emerging Technologies, 136, 103549. https://doi.org/10.1016/j.trc.2021.103549
   Web of Science ®Google Scholar
• Antony, E. (2017). Developing restoration schemes for a road transportation network in the event of a disaster. 56.
   Google Scholar
• Applegate, J. D., & Tilt, J. H. (2023). Using content and comparative analysis to contextualize the criteria for urban resiliency planning from international and US cities perspectives. Frontiers in Environmental Science, 11. https://doi.org/10.3389/fenvs.2023.1103115
   Web of Science ®Google Scholar
• ASCE. (2017). American infrastructure report card. American Society of Civil Engineers.
   Google Scholar
• Asgari, F., Sultan, A., Xiong, H., Gauthier, V., & El-Yacoubi, M. A. (2016). CT-Mapper: Mapping sparse multimodal cellular trajectories using a multilayer transportation network. Computer Communications, 95, 69–81. https://doi.org/10.1016/j.comcom.2016.04.014
   Web of Science ®Google Scholar
• Aydin, N. Y., Duzgun, H. S., Heinimann, H. R., Wenzel, F., & Gnyawali, K. R. (2018). Framework for improving the resilience and recovery of transportation networks under geohazard risks. International Journal of Disaster Risk Reduction, 31, 832–843. https://doi.org/10.1016/j.ijdrr.2018.07.022
   Web of Science ®Google Scholar
• Baggag, A., Abbar, S., Zanouda, T., & Srivastava, J. (2018). Resilience analytics: Coverage and robustness in multi-modal transportation networks. EPJ Data Science, 7(1), 14. https://doi.org/10.1140/epjds/s13688-018-0139-7
   Google Scholar
• Bah, A. S., Sanchez, T., Zhang, Y., Sasai, K., Conciatori, D., Chouinard, L., Power, G.J., & Zufferey, N. (2024). Assessing the condition state of a concrete bridge combining visual inspection and nonlinear deterioration model. Structure and Infrastructure Engineering, 20(2), 149–164. https://doi.org/10.1080/15732479.2022.2081987
   Web of Science ®Google Scholar
• Baroud, H., Barker, K., Ramirez-Marquez, J. E., & Rocco, C. M. (2014). Inherent costs and interdependent impacts of infrastructure network resilience. Risk Analysis, 35(4), 642–662. https://doi.org/10.1111/risa.12223
   PubMed Web of Science ®Google Scholar
• Bhatia, U., Kumar, D., Kodra, E., Ganguly, A. R., & Linkov, I. (2015). Network science based quantification of resilience demonstrated on the indian railways network. Public Library of Science ONE, 10(11), 10. https://doi.org/10.1371/journal.pone.0141890
   Google Scholar
• Bhavathrathan, B. K., & Patil, G. R. (2018). Algorithm to compute urban road network resilience. Transportation Research Record, 2672(48), 104–115. https://doi.org/10.1177/0361198118793329
   Web of Science ®Google Scholar
• Boakye, J., Guidotti, R., Gardoni, P., & Murphy, C. (2022). The role of transportation infrastructure on the impact of natural hazards on communities. Reliability Engineering & System Safety, 219, 108184. https://doi.org/10.1016/j.ress.2021.108184
   Web of Science ®Google Scholar
• Bolin, B., & Kurtz, L. C. (2018). Race, class, ethnicity, and disaster vulnerability. In Rodríguez, H., Donner, W., & Trainor, J. (Eds.), Handbook of disaster research (pp. 181–203). Springer.
   Google Scholar
• Brodka, P., Stawiak, P., & Kazienko, P. (2011). Shortest path discovery in the multi-layered social network. 2011 International Conference on Advances in Social Networks Analysis and Mining (pp. 497–501). Kaohsiung, Taiwan.
   Google Scholar
• Bruzzone, F., Cavallaro, F., & Nocera, S. (2023). The definition of equity in transport. Transportation Research Procedia, 69, 440–447. https://doi.org/10.1016/j.trpro.2023.02.193
   Google Scholar
• Bureau, U. S. C. (2017). Data profiles. Retrieved March 28, 2023, from https://www.census.gov/programs-surveys/acs/
   Google Scholar
• Cardillo, A., Zanin, M., Gómez-Gardeñes, J., Romance, M., García Del Amo, A., & Boccaletti, S. (2013). Modeling the multi-layer nature of the European air transport network: Resilience and passengers Re-scheduling under random failures. The European Physical Journal Special Topics, 215(1), 23–33. https://doi.org/10.1140/epjst/e2013-01712-8
   Web of Science ®Google Scholar
• Cavallaro, M., Asprone, D., Latora, V., Manfredi, G., & Nicosia, V. (2014). Assessment of urban ecosystem resilience through hybrid social–physical complex networks. Computer-Aided Civil and Infrastructure Engineering, 29(8), 608–625. https://doi.org/10.1111/mice.12080
   Web of Science ®Google Scholar
• Chandramouleeswaran, K. R., & Tran, H. T. (2018). Data-driven resilience quantification of the US air transportation network. 2018 Annual IEEE International Systems Conference (SysCon) (pp. 1–7). Vancouver, Canada.
   Google Scholar
• Chang, L., Elnashai, A. S., & Spencer, B. F., Jr. (2012). Post-earthquake modelling of transportation networks. Structure and Infrastructure Engineering, 8(10), 893–911.
   Web of Science ®Google Scholar
• Commission, P. V. P. (2018). Community resilience in the Pioneer Valley. Retrieved April 27, 2023, from https://pioneervalleydata.org/community-resilience-in-the-pioneer-valley-region/
   Google Scholar
• Cutter, S. (1995). Race, class, and environmental justice. Progress in Human Geography, 19(1), 111–122. https://doi.org/10.1177/030913259501900111
   Web of Science ®Google Scholar
• Danziger, M. M., Bonamassa, I., Boccaletti, S., & Havlin, S. (2019). Dynamic interdependence and competition in multilayer networks. Nature Physics, 15(2), 178–185. https://doi.org/10.1038/s41567-018-0343-1
   Web of Science ®Google Scholar
• Das, R. (2020). Approach for measuring transportation network resiliency: A case study on Dhaka, Bangladesh. Case Studies on Transport Policy, 8(2), 586–592. https://doi.org/10.1016/j.cstp.2020.04.001
   Web of Science ®Google Scholar
• De Domenico, M., Porter, M. A., & Arenas, A. (2015). MuxViz: A tool for multilayer analysis and visualization of networks. Journal of Complex Networks, 3(2), 159–176. https://doi.org/10.1093/comnet/cnu038
   Web of Science ®Google Scholar
• Domenico, M. D., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M. A., Gómez, S., & Arenas, A. (2013). Mathematical formulation of multilayer networks. Physical Review X, 3(4), 041022. https://doi.org/10.1103/PhysRevX.3.041022
   Web of Science ®Google Scholar
• Donovan, B., & Work, D. B. (2017). Empirically quantifying city-scale transportation system resilience to extreme events. Transportation Research Part C: Emerging Technologies, 79, 333–346. https://doi.org/10.1016/j.trc.2017.03.002
   Web of Science ®Google Scholar
• Du, W.-B., Zhou, X.-L., Lordan, O., Wang, Z., Zhao, C., & Zhu, Y.-B. (2016). Analysis of the Chinese airline network as multi-layer networks. Transportation Research Part E: Logistics & Transportation Review, 89, 108–116. https://doi.org/10.1016/j.tre.2016.03.009
   Web of Science ®Google Scholar
• Dvořák, Z., Sventeková, E., Řehák, D., & Čekerevac, Z. (2017). Assessment of critical infrastructure elements in transport. Procedia Engineering, 187, 548–555. https://doi.org/10.1016/j.proeng.2017.04.413
   Google Scholar
• Ensor, J. E., Mohan, T., Forrester, J., Khisa, U. K., Karim, T., & Howley, P. (2021). Opening space for equity and justice in resilience: A subjective approach to household resilience assessment. Global Environmental Change, 68, 102251. https://doi.org/10.1016/j.gloenvcha.2021.102251
   Web of Science ®Google Scholar
• Fang, Y.-P., Pedroni, N., & Zio, E. (2016). Resilience-based component importance measures for critical infrastructure network systems. IEEE Transactions on Reliability, 65(2), 502–512. https://doi.org/10.1109/TR.2016.2521761
   Web of Science ®Google Scholar
• Fan, C., Jiang, X., Lee, R., & Mostafavi, A. (2022). Equality of access and resilience in urban population-facility networks. npj Urban Sustainability, 2(1), 1–12. https://doi.org/10.1038/s42949-022-00051-3
   Google Scholar
• Federal Highway Administration. (2017). National bridge inventory. Retrieved August 12, 2021, from https://www.fhwa.dot.gov/bridge/nbi/ascii2017.cfm
   Google Scholar
• Field, C., Sutley, E., Naderpajouh, N., van de Lindt, J. W., Butry, D., Keenan, J. M., Smith-Colin, J., & Koliou, M. (2022). Incorporating socioeconomic metrics in civil engineering projects: The resilience perspective. Natural Hazards Review, 23(1), 04021064. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000537
   Web of Science ®Google Scholar
• Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. https://doi.org/10.2307/3033543
   Google Scholar
• Gagliardi, V., Tosti, F., Bianchini Ciampoli, L., Battagliere, M. L., D’Amato, L., Alani, A. M., & Benedetto, A. (2023). Satellite remote sensing and non-destructive testing methods for transport infrastructure monitoring: Advances, challenges and perspectives. Remote Sensing, 15(2), 418. https://doi.org/10.3390/rs15020418
   Web of Science ®Google Scholar
• Gao, W., Hu, X., & Wang, N. (2024). Resilience analysis in road traffic systems to rainfall events: Road environment perspective. Transportation Research Part D: Transport & Environment, 126, 104000. https://doi.org/10.1016/j.trd.2023.104000
   Web of Science ®Google Scholar
• Ghorbanzadeh, M., Koloushani, M., Ozguven, E. E., Vanli, A., & Arghandeh, R. (2022). City transportation network vulnerability to disasters: The case of hurricane hermine in Florida. Environmental Hazards, 21(3), 199–217. https://doi.org/10.1080/17477891.2021.1933885
   Google Scholar
• Gleich, D. F. (2015). PageRank beyond the web. SIAM Review, 57(3), 321–363. https://doi.org/10.1137/140976649
   Web of Science ®Google Scholar
• González, A. D., DueñDueñAs-Osorio, L., Sánchez-Silva, M., & Medaglia, A. L. (2016). The interdependent network design problem for optimal infrastructure system restoration. Computer- Aided Civil and Infrastructure Engineering, 31(5), 334–350. https://doi.org/10.1111/mice.12171
   Web of Science ®Google Scholar
• Guo, J., Zhu, X., Liu, C., Ge, S., & Rodino, L. (2021). Resilience modeling method of airport network affected by global public health events. Mathematical Problems in Engineering, 2021, 1–13. https://doi.org/10.1155/2021/6622031
   Web of Science ®Google Scholar
• Hamida, Z., & Goulet, J.-A. (2020). Modeling infrastructure degradation from visual inspections using network-scale state-space models. Structural Control and Health Monitoring, 27(9), e2582. https://doi.org/10.1002/stc.2582
   Web of Science ®Google Scholar
• Henry, E., Furno, A., & Faouzi, N.-E.-E. (2021). Approach to quantify the impact of disruptions on traffic conditions using dynamic weighted resilience metrics of transport networks. Transportation Research Record, 2675(4), 61–78. https://doi.org/10.1177/0361198121998663
   Web of Science ®Google Scholar
• Hollnagel, E., Pariès, J., Woods, D., & Wreathall, J. (2010). Resilience engineering in practice (reprint). Ashgate Publishing, Ltd., 2012.
   Google Scholar
• Hong, C., Lam, J. S. L., & Nan, L. (2018). Strategic investment in enhancing port–hinterland container transportation network resilience: A network game theory approach. Transportation Research Part B: Methodological, 111, 83–112. https://doi.org/10.1016/j.trb.2018.03.004
   Web of Science ®Google Scholar
• Hosseini, S., Barker, K., & Ramirez-Marquez, J. E. (2016). A review of definitions and measures of system resilience. Reliability Engineering & System Safety, 145, 47–61. https://doi.org/10.1016/j.ress.2015.08.006
   Web of Science ®Google Scholar
• Ilbeigi, M. (2019). Statistical process control for analyzing resilience of transportation networks. International Journal of Disaster Risk Reduction, 33, 155–161. https://doi.org/10.1016/j.ijdrr.2018.10.002
   Web of Science ®Google Scholar
• Ip, W., & Wang, D. (2009). Resilience evaluation approach of transportation networks. Proceedings of the International Joint Conference on Computational Sciences and Optimization (Vol. 2. pp. 618–622). Sanya, China.
   Google Scholar
• Ip, W. H., & Wang, D. (2011). Resilience and friability of transportation networks: Evaluation, analysis and optimization. IEEE Systems Journal, 5(2), 189–198. https://doi.org/10.1109/JSYST.2010.2096670
   Web of Science ®Google Scholar
• Jenelius, E., Petersen, T., & Mattsson, L.-G. (2006). Importance and exposure in road network vulnerability analysis. Transportation Research Part A: Policy and Practice, 40(7), 537–560. https://doi.org/10.1016/j.tra.2005.11.003
   Web of Science ®Google Scholar
• Jeong, H., Kim, H., Kim, K., & Kim, H. (2017). Prediction of flexible pavement deterioration in relation to climate change using fuzzy logic. Journal of Infrastructure Systems, 23(4), 04017008. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000363
   Web of Science ®Google Scholar
• Kameshwar, S., Misra, S., & Padgett, J. E. (2020). Decision tree based bridge restoration models for extreme event performance assessment of regional road networks. Structure and Infrastructure Engineering, 16(3), 431–451. https://doi.org/10.1080/15732479.2019.1668026
   Web of Science ®Google Scholar
• Kermanshah, A., & Derrible, S. (2017). Robustness of road systems to extreme flooding: Using elements of GIS, travel demand, and network science. Natural Hazards, 86(1), 151–164. https://doi.org/10.1007/s11069-016-2678-1
   Web of Science ®Google Scholar
• Kilanitis, I., & Sextos, A. (2019). Impact of earthquake-induced bridge damage and time evolving traffic demand on the road network resilience. Journal of Traffic & Transportation Engineering, 6(1), 35–48. https://doi.org/10.1016/j.jtte.2018.07.002
   Google Scholar
• Kobayashi, K., Kaito, K., & Lethanh, N. (2012). A statistical deterioration forecasting method using hidden Markov model for infrastructure management. Transportation Research Part B: Methodological, 46(4), 544–561. https://doi.org/10.1016/j.trb.2011.11.008
   Web of Science ®Google Scholar
• Kurant, M., & Thiran, P. (2006). Layered complex networks. Physical Review Letters, 96(13), 138701. https://doi.org/10.1103/PhysRevLett.96.138701
   PubMed Web of Science ®Google Scholar
• Lambrou, N., & Loukaitou-Sideris, A. (2021). Resilience plans in the US: An evaluation. Journal of Environmental Planning and Management, 65(5), 809–832. https://doi.org/10.1080/09640568.2021.1904849
   Web of Science ®Google Scholar
• Leu, G., Abbass, H., & Curtis, N. (2010). Resilience of ground transportation networks: A case study on Melbourne. World Transit Research.
   Google Scholar
• Li, Q., Dong, S., Mostafavi, A., & Lozano, S. (2019). Modeling of inter-organizational coordination dynamics in resilience planning of infrastructure systems: A multilayer network simulation framework. Public Library of Science ONE, 14(11), e0224522. https://doi.org/10.1371/journal.pone.0224522
   Google Scholar
• Lin, P., Yuan, X.-X., & Tovilla, E. (2019). Integrative modeling of performance deterioration and maintenance effectiveness for infrastructure assets with missing condition data. Computer- Aided Civil and Infrastructure Engineering, 34(8), 677–695. https://doi.org/10.1111/mice.12452
   Web of Science ®Google Scholar
• Li, S., & Zhou, Y. (2024). Integrating equity and efficiency into urban logistics resilience under emergency lockdowns. Transportation Research Part E: Logistics & Transportation Review, 183, 103446. https://doi.org/10.1016/j.tre.2024.103446
   Web of Science ®Google Scholar
• Logan, T. M., & Guikema, S. D. (2020). Reframing resilience: Equitable access to essential services. Risk Analysis, 40(8), 1538–1553. https://doi.org/10.1111/risa.13492
   PubMed Web of Science ®Google Scholar
• Matin, N., Forrester, J., & Ensor, J. (2018). What is equitable resilience? World Development, 109, 197–205. https://doi.org/10.1016/j.worlddev.2018.04.020
   PubMed Web of Science ®Google Scholar
• Mattsson, L.-G., & Jenelius, E. (2015). Vulnerability and resilience of transport systems – a discussion of recent research. Transportation Research Part A: Policy and Practice, 81, 16–34. https://doi.org/10.1016/j.tra.2015.06.002
   Web of Science ®Google Scholar
• Mavrouli, M., Mavroulis, S., Lekkas, E., & Tsakris, A. (2023). An emerging health crisis in Turkey and Syria after the earthquake disaster on 6 february 2023: Risk factors, prevention and management of infectious diseases. Healthcare, 11(7), 1022. https://doi.org/10.3390/healthcare11071022
   Web of Science ®Google Scholar
• Medina, P. A., González, F. J. L., & Todisco, L. (2022). Data-driven prediction of long-term deterioration of RC bridges. Construction and Building Materials, 317, 125790. https://doi.org/10.1016/j.conbuildmat.2021.125790
   Web of Science ®Google Scholar
• Meerow, S., Pajouhesh, P., & Miller, T. R. (2019). Social equity in urban resilience planning. Local Environment, 24(9), 793–808. https://doi.org/10.1080/13549839.2019.1645103
   Web of Science ®Google Scholar
• Morcous, G., Rivard, H., & Hanna, A. M. (2002). Case-based reasoning system for modeling infrastructure deterioration. Journal of Computing in Civil Engineering, 16(2), 104–114. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:2(104)
   Web of Science ®Google Scholar
• Murray-Tuite, P. M. (2006). A comparison of transportation network resilience under simulated system optimum and user equilibrium conditions. Proceedings of the 2006 Winter Simulation Conference (pp. 1398–1405). Proceedings of the 2006 Winter Simulation Conference. Monterey, CA, USA.
   Google Scholar
• Nejat, A., Solitare, L., Pettitt, E., & Mohsenian-Rad, H. (2022). Equitable community resilience: The case of winter storm Uri in Texas. International Journal of Disaster Risk Reduction, 77, 103070. https://doi.org/10.1016/j.ijdrr.2022.103070
   Web of Science ®Google Scholar
• Nipa, T. J., Kermanshachi, S., & Pamidimukkala, A. (2023). Identification of resilience dimensions in critical transportation infrastructure networks. Journal of Legal Affairs and Dis- Pute Resolution in Engineering and Construction, 15(2), 03122001. https://doi.org/10.1061/JLADAH.LADR-870
   Google Scholar
• Ouyang, M. (2014). Review on modeling and simulation of interdependent critical infrastructure systems. Reliability Engineering & System Safety, 121, 43–60. https://doi.org/10.1016/j.ress.2013.06.040
   Web of Science ®Google Scholar
• Pant, R., Barker, K., & Zobel, C. W. (2014). Static and dynamic metrics of economic resilience for interdependent infrastructure and industry sectors. Reliability Engineering & System Safety, 125, 92–102. https://doi.org/10.1016/j.ress.2013.09.007
   Web of Science ®Google Scholar
• Pearce, D. M., Matsunaka, R., & Oba, T. (2021). Comparing accessibility and connectivity metrics derived from dedicated pedestrian networks and street networks in the context of Asian cities. Asian Transport Studies, 7, 100036. https://doi.org/10.1016/j.eastsj.2021.100036
   Google Scholar
• Peng, P., Claramunt, C., Cheng, S., Yang, Y., & Lu, F. (2023). A multi-layer modelling approach for mining versatile ports of a global maritime transportation network. International Journal of Digital Earth, 16(1), 2129–2151. https://doi.org/10.1080/17538947.2023.2220614
   Web of Science ®Google Scholar
• Pirlone, F., Spadaro, I., & Candia, S. (2020). More resilient cities to face higher risks. The case of Genoa. Sustainability, 12(12), 1–21. https://doi.org/10.3390/su12124825
   PubMed Web of Science ®Google Scholar
• Potts, J., Tiedmann, H. R., Stephens, K. K., Faust, K. M., & Castellanos, S. (2024). Enhancing power system resilience to extreme weather events: A qualitative assessment of winter storm Uri. International Journal of Disaster Risk Reduction, 103, 104309. https://doi.org/10.1016/j.ijdrr.2024.104309
   Web of Science ®Google Scholar
• Qiang, Y., & Xu, J. (2019). Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data. International Journal of Geographical Information Science, 0(0), 1–17.
   Google Scholar
• Rashedi, R., & Hegazy, T. (2016). Holistic analysis of infrastructure deterioration and rehabilitation using system dynamics. Journal of Infrastructure Systems, 22(1), 04015016. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000273
   Web of Science ®Google Scholar
• Ro, B., & Garfin, G. (2023). Building urban flood resilience through institutional adaptive capacity: A case study of Seoul, South Korea. International Journal of Disaster Risk Reduction, 85, 103474. https://doi.org/10.1016/j.ijdrr.2022.103474
   Web of Science ®Google Scholar
• Santos, T., Fernandes, V. A., Cardoso, M., & da Silva, M. (2023). Resilience and vulnerability of urban mobility systems in developing countries: A case study of Rio De Janeiro’s transportation fare policy.
   Google Scholar
• Sediek, O. A., El-Tawil, S., & McCormick, J. (2022). Modeling interdependencies between the building portfolio, transportation network, and healthcare system in community resilience. Natural Hazards Review, 23(1), 04021060. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000538
   Web of Science ®Google Scholar
• Serulle, N. U., Heaslip, K., Brady, B., Louisell, W. C., & Collura, J. (2011). Resiliency of transportation network of Santo Domingo, Dominican Republic: Case study. Transportation Research Record, 2234(1), 22–30. https://doi.org/10.3141/2234-03
   Google Scholar
• Škrlj, B., Kralj, J., & Lavrač, N. (2019). Py3plex toolkit for visualization and analysis of multilayer networks. Applied Network Science, 4(1), 94. https://doi.org/10.1007/s41109-019-0203-7
   Google Scholar
• Soleimani, F., & Hajializadeh, D. (2022). Bridge seismic hazard resilience assessment with ensemble machine learning. Structures, 38, 719–732. https://doi.org/10.1016/j.istruc.2022.02.013
   Web of Science ®Google Scholar
• Solé-Ribalta, A., Gómez, S., & Arenas, A. (2016). Congestion induced by the structure of multiplex networks. Physical Review Letters, 116(10), 108701. https://doi.org/10.1103/PhysRevLett.116.108701
   PubMed Web of Science ®Google Scholar
• Stewart, M. G., Wang, X., & Nguyen, M. N. (2011). Climate change impact and risks of concrete infrastructure deterioration. Engineering Structures, 33(4), 1326–1337. https://doi.org/10.1016/j.engstruct.2011.01.010
   Web of Science ®Google Scholar
• Tabassum, A., Chinthavali, S., Chen, L., & Prakash, A. (2018). Data mining critical infrastructure systems: Models and tools. IEEE Intelligent Informatics Bulletin, 19(2), 1727–5997. https://www.osti.gov/biblio/1503981
   Google Scholar
• Taghizadeh, M., Mahsuli, M., & Poorzahedy, H. (2023). Probabilistic framework for evaluating the seismic resilience of transportation systems during emergency medical response. Reliability Engineering & System Safety, 236, 109255. https://doi.org/10.1016/j.ress.2023.109255
   Web of Science ®Google Scholar
• Tamima, U., & Chouinard, L. (2016). Development of evacuation models for moderate seismic zones: A case study of Montreal. International Journal of Disaster Risk Reduction, 16, 167–179. https://doi.org/10.1016/j.ijdrr.2016.02.003
   Web of Science ®Google Scholar
• Tingting, Z., & Yu, Z. (2020). Transportation infrastructure restoration optimization considering mobility and accessibility in resilience measures. Transportation Research Part C: Emerging Technologies, 117, 102700. https://doi.org/10.1016/j.trc.2020.102700
   Web of Science ®Google Scholar
• Tzortzinis, G., Knickle, B. T., Bardow, A., Breña, S. F., & Gerasimidis, S. (2021). Strength evaluation of deteriorated girder ends. I: Experimental study on naturally corroded I-beams. Thin-Walled Structures, 159, 107220. https://doi.org/10.1016/j.tws.2020.107220
   Web of Science ®Google Scholar
• Uddin, W., Hudson, W. R., & Haas, R. (2013). Public Infrastructure Asset Management. Retrieved April 25, 2023, from https://www.accessengineeringlibrary.com/content/book/9780071820110
   Google Scholar
• Wang, N., Wu, M., & Yuen, K. F. (2023). A novel method to assess urban multimodal transportation system resilience considering passenger demand and infrastructure supply. Reliability Engineering & System Safety, 238, 109478. https://doi.org/10.1016/j.ress.2023.109478
   Web of Science ®Google Scholar
• Wang, N., & Yuen, K. F. (2022). Resilience assessment of waterway transportation systems: Combining system performance and recovery cost. Reliability Engineering & System Safety, 226, 108673. https://doi.org/10.1016/j.ress.2022.108673
   Web of Science ®Google Scholar
• Weilant, S., Strong, A., & Miller, B. M. (2019). Incorporating resilience into transportation planning and assessment (No. RR-3038-TRB). RAND.
   Google Scholar
• Wu, Y., & Chen, S. (2023a). Resilience modeling and pre-hazard mitigation planning of transportation network to support post-earthquake emergency medical response. Reliability Engineering & System Safety, 230, 108918. https://doi.org/10.1016/j.ress.2022.108918
   Web of Science ®Google Scholar
• Wu, Y., & Chen, S. (2023b). Traffic resilience modeling for post-earthquake emergency medical response and planning considering disrupted infrastructure and dislocated residents. International Journal of Disaster Risk Reduction, 93, 103754. https://doi.org/10.1016/j.ijdrr.2023.103754
   Web of Science ®Google Scholar
• Wu, Y., & Chen, S. (2023c). Transportation resilience modeling and bridge reconstruction planning based on time-evolving travel demand during post-earthquake recovery period. Sustainability, 15(17), 12751. https://doi.org/10.3390/su151712751
   Web of Science ®Google Scholar
• Wu, Y., Hou, G., & Chen, S. (2021). Post-earthquake resilience assessment and long-term restoration prioritization of transportation network. Reliability Engineering & System Safety, 211, 107612. https://doi.org/10.1016/j.ress.2021.107612
   Web of Science ®Google Scholar
• Wu, B., Lin, Z., Liang, Y., Zhou, Z., & Lu, H. (2023). An effective prediction method for bridge long-gauge strain under moving trainloads with experimental verification. Mechanical Systems and Signal Processing, 186, 109855. https://doi.org/10.1016/j.ymssp.2022.109855
   Web of Science ®Google Scholar
• Wu, J., Pu, C., Li, L., & Cao, G. (2020). Traffic dynamics on multilayer networks. Digital Communications and Networks, 6(1), 58–63. https://doi.org/10.1016/j.dcan.2018.10.011
   Web of Science ®Google Scholar
• Zhang, W., & Wang, N. (2016). Resilience-based risk mitigation for road networks. Structural Safety, 62, 57–65. https://doi.org/10.1016/j.strusafe.2016.06.003
   Web of Science ®Google Scholar
• Zhang, W., Wang, N., & Nicholson, C. (2017). Resilience-based post-disaster recovery strategies for road-bridge networks. Structure and Infrastructure Engineering, 13(11), 1404–1413. https://doi.org/10.1080/15732479.2016.1271813
   Web of Science ®Google Scholar
• Zhu, Y., Xie, K., Ozbay, K., Zuo, F., & Yang, H. (2017). Data-driven spatial modeling for quantifying networkwide resilience in the aftermath of hurricanes Irene and Sandy. Transportation Research Record, 2604(1), 9–18. https://doi.org/10.3141/2604-02
   Google Scholar
• Zou, Q., & Chen, S. (2021). Resilience-based recovery scheduling of transportation network in mixed traffic environment: A deep-ensemble-assisted active learning approach. Reliability Engineering & System Safety, 215, 107800. https://doi.org/10.1016/j.ress.2021.107800
   Web of Science ®Google Scholar