Review of urban resilient transportation systems: Assessment methods and optimization improvements

WEI Ya-xin; LI Kun; MU Chen; LI Ying; TENG Jing; MA Xiao-lei; WANG Jian; AN Yi-sheng; DU Yu-chuan; ZHAO Xiang-mo

doi:10.19818/j.cnki.1671-1637.2026.210

Volume 26 Issue 4

Apr. 2026

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WEI Ya-xin, LI Kun, MU Chen, LI Ying, TENG Jing, MA Xiao-lei, WANG Jian, AN Yi-sheng, DU Yu-chuan, ZHAO Xiang-mo. Review of urban resilient transportation systems: Assessment methods and optimization improvements[J]. Journal of Traffic and Transportation Engineering, 2026, 26(4): 230-258. doi: 10.19818/j.cnki.1671-1637.2026.210

Citation:

WEI Ya-xin, LI Kun, MU Chen, LI Ying, TENG Jing, MA Xiao-lei, WANG Jian, AN Yi-sheng, DU Yu-chuan, ZHAO Xiang-mo. Review of urban resilient transportation systems: Assessment methods and optimization improvements[J]. Journal of Traffic and Transportation Engineering, 2026, 26(4): 230-258. doi: 10.19818/j.cnki.1671-1637.2026.210

Citation:

WEI Ya-xin, LI Kun, MU Chen, LI Ying, TENG Jing, MA Xiao-lei, WANG Jian, AN Yi-sheng, DU Yu-chuan, ZHAO Xiang-mo. Review of urban resilient transportation systems: Assessment methods and optimization improvements[J]. Journal of Traffic and Transportation Engineering, 2026, 26(4): 230-258. doi: 10.19818/j.cnki.1671-1637.2026.210

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Review of urban resilient transportation systems: Assessment methods and optimization improvements

doi: 10.19818/j.cnki.1671-1637.2026.210

WEI Ya-xin^{1, 2},
LI Kun^{1, 2},
MU Chen^{1, 2},
LI Ying^{1, 2},
TENG Jing³,
MA Xiao-lei⁴,
WANG Jian⁵,
AN Yi-sheng^{1, 2
,
,},
DU Yu-chuan³,
ZHAO Xiang-mo^{1, 2}

1.
School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
2.
Xi'an Key Laboratory of Human-Machine Collaborative Cyber-Physical Systems, Chang'an University, Xi'an 710064, Shaanxi, China
3.
College of Transportation Engineering, Tongji University, Shanghai 201804, China
4.
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
5.
School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China

Funds:

National Natural Science Foundation of China 52572492

National Natural Science Foundation of China 52272330

Fundamental Research Funds for the Central Universities 300102242902

More Information

Corresponding author: AN Yi-sheng, professor, PhD, E-mail: aysm@chd.edu.cn
Received Date: 2025-07-31
Accepted Date: 2026-01-23
Rev Recd Date: 2026-01-06
Publish Date: 2026-04-28

Abstract

Abstract

To systematically review the assessment methods and optimization strategies of urban resilient transportation systems, an analytical framework centered on robustness and recoverability was constructed, and existing studies were synthesized and reviewed based on this. Regarding resilience assessment, four types of mainstream methods were systematically analyzed: graph theory and complex networks, probability statistical models, data-driven approaches, and multi-indicator assessment. Regarding resilience optimization, from single- and multi-dimensional perspectives, preventive strategies represented by network structure reinforcement and responsive strategies represented by emergency resource dispatching were investigated. By analyzing decision variables and objective functions, a theoretical mapping mechanism between assessment indicators and optimization strategies was constructed. The analysis results show that existing assessment methods are transforming from single physical topology measurement to intelligent assessment integrating spatiotemporal causal inference. Meanwhile, optimization strategies are evolving from static equilibrium of local road networks to dynamic games of cross-system coupling. The study clarifies the synergistic mechanism between assessment and optimization, and the identification of robustness boundaries and vulnerable nodes directly defines the solution space constraints for preventive optimization. In addition, the recoverability curve and performance loss quantification provide standardized benchmarks for constructing the objective functions of responsive optimization. Future research needs to focus on the deep integrated design of assessment and optimization, establishing closed-loop decision-making models capable of dynamic feedback and self-adjustment. Simultaneously, the integration of physical models and artificial intelligence methods should be strengthened to develop predictive assessment and proactive optimization technologies for complex scenarios, thus providing key theoretical and technical support for constructing intelligent and highly resilient urban transportation systems.
- urban traffic,
- resilient transportation system,
- review,
- resilience assessment and optimization,
- robustness and recoverability,
- data-driven,
- emergency dispatch

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References(93)

References

[1]	ZHOU Y M, WANG J W, YANG H. Resilience of transportation systems: Concepts and comprehensive review [J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(12): 4262-4276. doi: 10.1109/TITS.2018.2883766
[2]	WEI S M, PAN J H. Resilience of urban network structure in China: The perspective of disruption [J]. ISPRS International Journal of Geo-Information, 2021, 10(12): 796. doi: 10.3390/ijgi10120796
[3]	WANG H, LIU Z, ZHOU Y. Assessing urban resilience in China from the perspective of socioeconomic and ecological sustainability [J]. Environmental Impact Assessment Review, 2023, 102: 107163. doi: 10.1016/j.eiar.2023.107163
[4]	YANG Yang, CHEN Jun-ting, YAO En-jian, et al. Resilience evaluation method of external transportation network of integrated transit hub cluster under time-varying demand[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 155-169. doi: 10.19818/j.cnki.1671-1637.2026.148
[5]	CAO Y P, ZHOU B, CHUNG C Y, et al. Resilience-oriented coordinated topology reconfiguration of electricity and drainage networks with distributed mobile emergency resources [J]. IEEE Transactions on Smart Grid, 2025, 16(1): 786-800. doi: 10.1109/TSG.2024.3419086
[6]	WEN Jia-nian, WEI Li-yan, ZHANG Wang-xin, et al. A post-earthquake repair decision method for bridges in regional Road networks considering power facility failures [J]. Journal of Traffic and Transportation Engineering, 2025, 25(6): 61-74. doi: 10.19818/j.cnki.1671-1637.2025.06.006
[7]	XU G Y, WANG J W, HUANG G Q, et al. Data-driven resilient fleet management for cloud asset-enabled urban flood control [J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(6): 1827-1838. doi: 10.1109/TITS.2017.2740438
[8]	VALI-SIAR M M, ROGHANIAN E. Sustainable, resilient and responsive mixed supply chain network design under hybrid uncertainty with considering COVID-19 pandemic disruption [J]. Sustainable Production and Consumption, 2022, 30: 278-300. doi: 10.1016/j.spc.2021.12.003
[9]	WANG J W, DOU R L, MUDDADA R R, et al. Management of a holistic supply chain network for proactive resilience: Theory and case study [J]. Computers & Industrial Engineering, 2018, 125: 668-677.
[10]	DATOLA G. Implementing urban resilience in urban planning: A comprehensive framework for urban resilience evaluation [J]. Sustainable Cities and Society, 2023, 98: 104821. doi: 10.1016/j.scs.2023.104821
[11]	HE Y X, ZIO E, YANG Z M, et al. A systematic resilience assessment framework for multi-state systems based on physics-informed neural network [J]. Reliability Engineering & System Safety, 2025, 257: 110866.
[12]	LENG J W, SHA W N, LIN Z S, et al. Blockchained smart contract pyramid-driven multi-agent autonomous process control for resilient individualised manufacturing towards Industry 5.0 [J]. International Journal of Production Research, 2023, 61(13): 4302-4321. doi: 10.1080/00207543.2022.2089929
[13]	PAN Y N, WU Y M, LAM H K. Security-based fuzzy control for nonlinear networked control systems with DoS attacks via a resilient event-triggered scheme [J]. IEEE Transactions on Fuzzy Systems, 2022, 30(10): 4359-4368. doi: 10.1109/TFUZZ.2022.3148875
[14]	MURRAY-TUITE P M. A comparison of transportation network resilience under simulated system optimum and user equilibrium conditions [C]//IEEE. Proceedings of the 2006 Winter Simulation Conference. New York: IEEE, 2006: 1398-1405.
[15]	WAN C P, YANG Z L, ZHANG D, et al. Resilience in transportation systems: A systematic review and future directions [J]. Transport Reviews, 2018, 38(4): 479-498. doi: 10.1080/01441647.2017.1383532
[16]	LI S P, ZHOU Y M, ZHANG F N. Interdependency in transportation system resilience: Review and discussion [J]. Transportation Research Part D: Transport and Environment, 2025, 140: 104618. doi: 10.1016/j.trd.2025.104618
[17]	BI W, MACASKILL K, SCHOOLING J. Old wine in new bottles? Understanding infrastructure resilience: Foundations, assessment, and limitations [J]. Transportation Research Part D: Transport and Environment, 2023, 120: 103793. doi: 10.1016/j.trd.2023.103793
[18]	CALVERT S C, SNELDER M. A methodology for road traffic resilience analysis and review of related concepts [J]. Transportmetrica A: Transport Science, 2018, 14(1/2): 130-154.
[19]	REGGIANI A, NIJKAMP P, LANZI D. Transport resilience and vulnerability: The role of connectivity [J]. Transportation Research Part A: Policy and Practice, 2015, 81: 4-15. doi: 10.1016/j.tra.2014.12.012
[20]	AYDIN N Y, DUZGUN H S, WENZEL F, et al. Integration of stress testing with graph theory to assess the resilience of urban road networks under seismic hazards [J]. Natural Hazards, 2018, 91(1): 37-68. doi: 10.1007/s11069-017-3112-z
[21]	CAI Ying-feng, WANG Cheng, SUN Xiao-qiang, et al. Towards a robustness-enhanced fuzzy predictive control method for heterogeneous commercial vehicle fleets [J]. Journal of Traffic and Transportation Engineering, 2026, 26(4): 288-303.
[22]	BRUNEAU M, CHANG S E, EGUCHI R T, et al. A framework to quantitatively assess and enhance the seismic resilience of communities [J]. Earthquake Spectra, 2003, 19(4): 733-752. doi: 10.1193/1.1623497
[23]	GAO Y, WANG J W. A resilience assessment framework for urban transportation systems [J]. International Journal of Production Research, 2021, 59(7): 2177-2192. doi: 10.1080/00207543.2020.1847339
[24]	MORELLI A B, CUNHA A L. Measuring urban road network vulnerability to extreme events: An application for urban floods [J]. Transportation Research Part D: Transport and Environment, 2021, 93: 102770. doi: 10.1016/j.trd.2021.102770
[25]	WEI M, XU J G. Assessing road network resilience in disaster areas from a complex network perspective: A real-life case study from China [J]. International Journal of Disaster Risk Reduction, 2024, 100: 104167. doi: 10.1016/j.ijdrr.2023.104167
[26]	ZHANG L, WEN H Y, LU J, et al. Vulnerability assessment and visualization of large-scale bus transit network under route service disruption [J]. Transportation Research Part D: Transport and Environment, 2020, 88: 102570. doi: 10.1016/j.trd.2020.102570
[27]	BELL M G H, KURAUCHI F, PERERA S, et al. Investigating transport network vulnerability by capacity weighted spectral analysis [J]. Transportation Research Part B: Methodological, 2017, 99: 251-266. doi: 10.1016/j.trb.2017.03.002
[28]	GANIN A A, MERSKY A C, JIN A S, et al. Resilience in intelligent transportation systems (ITS) [J]. Transportation Research Part C: Emerging Technologies, 2019, 100: 318-329. doi: 10.1016/j.trc.2019.01.014
[29]	XU P C, LU Q C, XIE C, et al. Modeling the resilience of interdependent networks: The role of function dependency in metro and bus systems [J]. Transportation Research Part A: Policy and Practice, 2024, 179: 103907. doi: 10.1016/j.tra.2023.103907
[30]	WANG B, SU Q, CHIN K S. Vulnerability assessment of China-Europe Railway Express multimodal transport network under cascading failures [J]. Physica A: Statistical Mechanics and Its Applications, 2021, 584: 126359. doi: 10.1016/j.physa.2021.126359
[31]	JIANG J C, WU L X, YU J Q, et al. Robustness of bilayer railway-aviation transportation network considering discrete cross-layer traffic flow assignment [J]. Transportation Research Part D: Transport and Environment, 2024, 127: 104071. doi: 10.1016/j.trd.2024.104071
[32]	WANG L J, ZHANG S C, SZŰCS G, et al. Identifying the critical nodes in multi-modal transportation network with a traffic demand-based computational method [J]. Reliability Engineering & System Safety, 2024, 244: 109956.
[33]	WANG Yong-gang, WANG Long-jian, LIU Zhi-gang, et al. Vulnerability metrics of multimodal composite transportation network [J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 195-207. doi: 10.19818/j.cnki.1671-1637.2023.01.015
[34]	NOGAL M, HONFI D. Assessment of road traffic resilience assuming stochastic user behaviour [J]. Reliability Engineering & System Safety, 2019, 185: 72-83.
[35]	NOGAL M, MORALES NÁPOLES O, O'CONNOR A. Structured expert judgement to understand the intrinsic vulnerability of traffic networks [J]. Transportation Research Part A: Policy and Practice, 2019, 127: 136-152. doi: 10.1016/j.tra.2019.07.006
[36]	GAO W, HU X W, WANG N H. Resilience analysis in road traffic systems to rainfall events: Road environment perspective [J]. Transportation Research Part D: Transport and Environment, 2024, 126: 104000. doi: 10.1016/j.trd.2023.104000
[37]	FARAHMAND H, YIN K, HSU C W, et al. Integrating climate projections and probabilistic network analysis into regional transport resilience planning [J]. Transportation Research Part D: Transport and Environment, 2024, 133: 104229. doi: 10.1016/j.trd.2024.104229
[38]	HOU Zong-hao, LI Gang. Evaluation of material receiving performance of airport hub after earthquake considering dynamic receiving efficiency [J/OL]. Journal of Traffic and Transportation Engineering, 2026, https://doi.org/10.19818/j.cnki.1671-1637.2026.028.
[39]	HU S H, WANG K L, LI L Y, et al. Multi-crowdsourced data fusion for modeling link-level traffic resilience to adverse weather events [J]. International Journal of Disaster Risk Reduction, 2024, 112: 104754. doi: 10.1016/j.ijdrr.2024.104754
[40]	PENG Q, BAKKAR Y, WU L P, et al. Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis [J]. Transportation Research Part A: Policy and Practice, 2024, 179: 103947. doi: 10.1016/j.tra.2023.103947
[41]	ZANG D, WEI C G, ZHAO J Y, et al. Predictive resilience assessment featuring diffusion reconstruction for road networks under rainfall disturbances [J]. Engineering Applications of Artificial Intelligence, 2024, 138: 109317. doi: 10.1016/j.engappai.2024.109317
[42]	ZANG D, DING Y J, ZHAO J Y, et al. Predictive resilience assessment of road networks based on dynamic multi-granularity graph neural network [J]. Neurocomputing, 2024, 601: 128207. doi: 10.1016/j.neucom.2024.128207
[43]	ZHANG Y C, ZHAO H P, QIU H Y, et al. Dynamic assessment of postdisaster road network vulnerability using crowdsourced traffic data [J]. Transportation Research Part D: Transport and Environment, 2024, 126: 104021. doi: 10.1016/j.trd.2023.104021
[44]	WANG H W, PENG Z R, WANG D S, et al. Evaluation and prediction of transportation resilience under extreme weather events: A diffusion graph convolutional approach [J]. Transportation Research Part C: Emerging Technologies, 2020, 115: 102619. doi: 10.1016/j.trc.2020.102619
[45]	GHADAMI A, DOERING C R, DRAKE J M, et al. Stability and resilience of transportation systems: Is a traffic jam about to occur? [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 10803-10814. doi: 10.1109/TITS.2021.3095897
[46]	TANG J Q, HEINIMANN H, HAN K, et al. Evaluating resilience in urban transportation systems for sustainability: A systems-based Bayesian network model [J]. Transportation Research Part C: Emerging Technologies, 2020, 121: 102840. doi: 10.1016/j.trc.2020.102840
[47]	WANG N X, WU M, YUEN K F, et al. Urban transportation system long-term resilience assessment using multi-dimensional dynamic Bayesian network [J]. Transportation Research Part D: Transport and Environment, 2024, 136: 104427. doi: 10.1016/j.trd.2024.104427
[48]	YIN K, WU J J, WANG W P, et al. An integrated resilience assessment model of urban transportation network: A case study of 40 cities in China [J]. Transportation Research Part A: Policy and Practice, 2023, 173: 103687. doi: 10.1016/j.tra.2023.103687
[49]	FANG C, CHU Y Z, FU H R, et al. On the resilience assessment of complementary transportation networks under natural hazards [J]. Transportation Research Part D: Transport and Environment, 2022, 109: 103331. doi: 10.1016/j.trd.2022.103331
[50]	YANG Z H, WANG H, CHEN B. Assessment of urban waterlogging-induced road traffic safety risk and identification of its driving factors: A case study of Beijing [J]. Transportation Research Part A: Policy and Practice, 2024, 183: 104080. doi: 10.1016/j.tra.2024.104080
[51]	NICKDOOST N, SHOOSHTARI M J, CHOI J, et al. A composite index framework for quantitative resilience assessment of road infrastructure systems [J]. Transportation Research Part D: Transport and Environment, 2024, 131: 104180. doi: 10.1016/j.trd.2024.104180
[52]	NAN C, SANSAVINI G. A quantitative method for assessing resilience of interdependent infrastructures [J]. Reliability Engineering & System Safety, 2017, 157: 35-53.
[53]	GANIN A A, KITSAK M, MARCHESE D, et al. Resilience and efficiency in transportation networks [J]. Science Advances, 2017, 3(12): e1701079. doi: 10.1126/sciadv.1701079
[54]	XU Peng-cheng, LU Qing-chang, LI Jing, et al. Analyzing congestion characteristic of expressway network from perspective of resilience [J]. China Journal of Highway and Transport, 2024, 37(10): 196-208.
[55]	LIU B, LIU X Y, YANG Y, et al. Resilience assessment framework toward interdependent bus-rail transit network: Structure, critical components, and coupling mechanism [J]. Communications in Transportation Research, 2023, 3: 100098. doi: 10.1016/j.commtr.2023.100098
[56]	DU Q, ZONG X Y, LI Y, et al. Resilience optimization of bus-metro double-layer network against extreme weather events [J]. Transportation Research Part D: Transport and Environment, 2024, 135: 104378. doi: 10.1016/j.trd.2024.104378
[57]	WANG N X, WU M, YUEN K F. A novel method to assess urban multimodal transportation system resilience considering passenger demand and infrastructure supply [J]. Reliability Engineering & System Safety, 2023, 238: 109478.
[58]	MA W X, LIN S C, CI Y S, et al. Resilience evaluation and improvement of post-disaster multimodal transportation networks [J]. Transportation Research Part A: Policy and Practice, 2024, 189: 104243. doi: 10.1016/j.tra.2024.104243
[59]	LU Qing-chang, LIU Peng, XU Biao, et al. Resilience-based protection decision optimization for metro network under operational incidents [J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 209-220. doi: 10.19818/j.cnki.1671-1637.2023.03.016
[60]	YIN Y Q, XU X R, WANG D J, et al. Two-stage recoverable robust optimization for an integrated location–allocation and evacuation planning problem [J]. Transportation Research Part B: Methodological, 2024, 182: 102906. doi: 10.1016/j.trb.2024.102906
[61]	WANG Y, WANG J W. Measuring and maximizing resilience of transportation systems for emergency evacuation [J]. IEEE Transactions on Engineering Management, 2020, 67(3): 603-613. doi: 10.1109/TEM.2019.2949098
[62]	CHENG Chi-yao, MU Neng-ye. Resilience optimization of urban rail transit network based on posteriorly adding algorithm [J]. Journal of Transportation Engineering and Information, 2022, 20(4): 88-99.
[63]	LI Z X, YIN J T, CHAI S M, et al. Optimization of system resilience in urban rail systems: Train rescheduling considering congestions of stations [J]. Computers & Industrial Engineering, 2023, 185: 109657.
[64]	TAO W, WANG Z Z, LIU C J, et al. Resilience assessment and enhancement of urban road networks subject to traffic accidents: A network-scale optimization strategy [J]. Journal of Intelligent Transportation Systems, 2024, 28(4): 494-510. doi: 10.1080/15472450.2022.2141119
[65]	HOSSEINI Y, KARAMI MOHAMMADI R, YANG T Y. Resource-based seismic resilience optimization of the blocked urban road network in emergency response phase considering uncertainties [J]. International Journal of Disaster Risk Reduction, 2023, 85: 103496. doi: 10.1016/j.ijdrr.2022.103496
[66]	SUN H L, LAN H J, HE Z L, et al. Dynamic resilience assessment and multi-objective optimization decision-making for urban roadway tunnel system in the face of fire disaster [J]. Tunnelling and Underground Space Technology, 2025, 155: 106120. doi: 10.1016/j.tust.2024.106120
[67]	MA Fei, ZHAO Cheng-yong, SUN Qi-peng, et al. Integrated resilience of urban rail transit network with active passenger flow restriction under major public health disasters [J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 208-221. doi: 10.19818/j.cnki.1671-1637.2023.01.016
[68]	ZAMANIFAR M, HARTMANN T. Decision attributes for disaster recovery planning of transportation networks; A case study [J]. Transportation Research Part D: Transport and Environment, 2021, 93: 102771. doi: 10.1016/j.trd.2021.102771
[69]	SASAI K, CHOUINARD L E, POWER G J, et al. Accounting for traffic disturbance in road infrastructure management: Optimal maintenance and rehabilitation planning for the society [J]. Transportation Research Part A: Policy and Practice, 2024, 183: 104040. doi: 10.1016/j.tra.2024.104040
[70]	WU Y Y, HOU G Y, CHEN S R. Post-earthquake resilience assessment and long-term restoration prioritization of transportation network [J]. Reliability Engineering & System Safety, 2021, 211: 107612.
[71]	PEI S S, ZHAI C H, HU J, et al. Resilience assessment and enhancement of interdependent transportation-healthcare system: A spatial accessibility approach [J]. Transportation Research Part D: Transport and Environment, 2024, 128: 104090. doi: 10.1016/j.trd.2024.104090
[72]	ZHANG M Y, YANG X J, ZHANG J, et al. Post-earthquake resilience optimization of a rural "road-bridge" transportation network system [J]. Reliability Engineering & System Safety, 2022, 225: 108570.
[73]	ZOU Q L, CHEN S R. Resilience-based recovery scheduling of transportation network in mixed traffic environment: A deep-ensemble-assisted active learning approach [J]. Reliability Engineering & System Safety, 2021, 215: 107800.
[74]	MAO Xin-hua, WANG Jian-wei, YUAN Chang-wei, et al. Restoration scheduling for post-disaster road networks based on resilience optimization [J]. China Journal of Highway and Transport, 2022, 35(6): 289-298.
[75]	YAIBOK C, SUWANNO P, PORNBUNYANON T, et al. Synergizing flood risk and road network dynamics for optimized evacuation strategies [J]. Asian Transport Studies, 2024, 10: 100137. doi: 10.1016/j.eastsj.2024.100137
[76]	LIU P, KOROVIN I, GORBACHEV S, et al. Multi-agent based optimal equilibrium selection with resilience constraints for traffic flow [J]. Neural Networks, 2022, 155: 308-317. doi: 10.1016/j.neunet.2022.08.013
[77]	HAO Xin-jun, MAO Xin-hua, TAN Xiao-yan, et al. Post-disaster highway network restoration decision based on reinforcement learning [J]. China Journal of Highway and Transport, 2023, 36(8): 292-304.
[78]	GUO X Q, DU Q, LI Y, et al. Cascading failure and recovery of metro-bus double-layer network considering recovery propagation [J]. Transportation Research Part D: Transport and Environment, 2023, 122: 103861. doi: 10.1016/j.trd.2023.103861
[79]	YAO K S, CHEN L, CHEN S R. Time-evolving traffic resilience performance forecasting during hazardous weather toward proactive intervention [J]. Reliability Engineering & System Safety, 2025, 253: 110521.
[80]	YANG Zhong-zhen, ZHANG Er-zhuo, DU Yi-ying, et al. Review of multimodal transport logistics based on bibliometrics [J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 1-23. doi: 10.19818/j.cnki.1671-1637.2026.071
[81]	ZOU Q L, CHEN S R. Enhancing resilience of interdependent traffic-electric power system [J]. Reliability Engineering & System Safety, 2019, 191: 106557.
[82]	ZOU X, WANG Y, STRBAC G. A resilience-oriented pre-positioning approach for electric vehicle routing and scheduling in coupled energy and transport sectors [J]. Sustainable Energy, Grids and Networks, 2024, 39: 101484. doi: 10.1016/j.segan.2024.101484
[83]	ZHANG L, XU M, WANG S A. Quantifying bus route service disruptions under interdependent cascading failures of a multimodal public transit system based on an improved coupled map lattice model [J]. Reliability Engineering & System Safety, 2023, 235: 109250.
[84]	GUO J N, DU Q, HE Z G. A method to improve the resilience of multimodal transport network: Location selection strategy of emergency rescue facilities [J]. Computers & Industrial Engineering, 2021, 161: 107678.
[85]	YANG X, BAN X J, MITCHELL J. Modeling multimodal transportation network emergency evacuation considering evacuees' cooperative behavior [J]. Transportation Research Part A: Policy and Practice, 2018, 114: 380-397. doi: 10.1016/j.tra.2018.01.037
[86]	GAN W, WEN J F, YAN M Y, et al. Enhancing resilience with electric vehicles charging redispatching and vehicle-to-grid in traffic-electric networks [J]. IEEE Transactions on Industry Applications, 2024, 60(1): 953-965. doi: 10.1109/TIA.2023.3272870
[87]	ABBASI M, CONSILVIO A, GIGLIO D. A multi-objective optimization approach for disruption management in an intermodal freight transport network [J]. Transportation Research Procedia, 2024, 78: 410-417. doi: 10.1016/j.trpro.2024.02.052
[88]	GUO F, XU Y, HUANG Z H, et al. Collaborative optimization of routing and storage strategy of multi-period multimodal transport in an uncertain environment [J]. Computers & Operations Research, 2024, 167: 106676.
[89]	ZHANG H F, YANG K, DONG J J, et al. Two-stage robust multimodal hub network design under budgeted demand uncertainty: A Benders decomposition approach and a case study [J]. Computers & Operations Research, 2025, 174: 106882.
[90]	ZHANG Y C, DING Z L. Full-time scale resilience enhancement strategies for an integrated electricity-gas-transportation coupled system [J]. Applied Mathematical Modelling, 2023, 117: 63-86. doi: 10.1016/j.apm.2022.12.020
[91]	CHEN Chang-kun, WANG Si-qi, SUN Feng-lin, et al. A resilience assessment model for multilayer networks of urban interdependent infrastructure [J]. China Safety Science Journal, 2024, 34(5): 204-213.
[92]	XUE Y S, YU X H. Beyond smart grid: Cyber-physical-social system in energy future [point of view] [J]. Proceedings of the IEEE, 2017, 105(12): 2290-2292. doi: 10.1109/JPROC.2017.2768698
[93]	LIU Z S, YU B, ZHANG L, et al. Resilience enhancement of multi-modal public transportation system via electric bus network redesign [J]. Transportation Research Part E: Logistics and Transportation Review, 2025, 193: 103810. doi: 10.1016/j.tre.2024.103810

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Review of urban resilient transportation systems: Assessment methods and optimization improvements

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