Post-Event Restoration Sequence Optimization for Road Networks Leveraging Graph Attention Networks

YAO Yitao, JIA Bin, ZHAO Tingting

Journal of Systems Science and Mathematical Sciences ›› 2025, Vol. 45 ›› Issue (4) : 1013-1030.

PDF(5527 KB)
中国科学院数学与系统科学研究院期刊网
PDF(5527 KB)
Journal of Systems Science and Mathematical Sciences ›› 2025, Vol. 45 ›› Issue (4) : 1013-1030. DOI: 10.12341/jssms240089

Post-Event Restoration Sequence Optimization for Road Networks Leveraging Graph Attention Networks

  • YAO Yitao, JIA Bin, ZHAO Tingting
Author information +
History +

Abstract

Identifying key segments within road networks is crucial for selecting repair objectives and optimizing repair sequences during the post-disaster recovery phase. Traditional methods for identifying key segments have not fully explored the interactions between multiple segments, particularly the significance of studying road network vulnerability under simultaneous disruptions of multiple links. To tackle this issue, this study introduces a machine learning model called transportation graph attention networks for criticality analysis (TGAT) to identify key road segments when facing multiple disruptions. This model is trained on data samples that include scenarios of multiple segment failures, utilizing the graph attention network to evaluate the influence weights between segments and calculating the criticality of each segment based on these weights. The model, trained using mean squared error as the loss function, is capable of identifying segments that play a crucial role in the performance of the road network. Taking the Kunshan City road network as an example, this paper compares the effectiveness of the TGAT method with three other methods:Degree centrality, weighted betweenness centrality, and eigenvector centrality, in optimizing repair sequences during the post-recovery phase. Experimental results indicate that the TGAT method is more effective in identifying key segments within the road network compared to the other three methods, and the repair sequence optimized using TGAT further enhances the repair performance of the road network.

Key words

Urban road network / restoration strategy / resilience / multiple-link failures / machine learning / graph attention network

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
YAO Yitao , JIA Bin , ZHAO Tingting. Post-Event Restoration Sequence Optimization for Road Networks Leveraging Graph Attention Networks. Journal of Systems Science and Mathematical Sciences, 2025, 45(4): 1013-1030 https://doi.org/10.12341/jssms240089

References

[1] Goncalves L A P J, Ribeiro P J G. Resilience of urban transportation systems. Concept, characteristics, and methods. Journal of Transport Geography, 2020, 85: 102727.
[2] Zhou Y M, Wang J W, Yang H. Resilience of transportation systems: Concepts and comprehensive review. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(12): 4262–4276.
[3] 刘洁, 张丽佳, 石振武, 等. 交通运输系统韧性研究综述. 科技和产业, 2020, 20(2): 47–52. (Liu J, Zhang L J, Shi Z W, et al. Summary of research on the resilience of transportation system. Network & Science Technology and Industry, 2020, 20(2): 47–52.)
[4] Freckleton D, Heaslip K, Louisell W, et al. Evaluation of resiliency of transportation networks after disasters. Transportation Research Record, 2012, 2284(1): 109–116.
[5] 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. Natural Hazards, 2017, 91(1): 37–68.
[6] Aydin N Y, Duzgun H S, Heinimann H R, et al. Framework for improving the resilience and recovery of transportation networks under geohazard risks. Natural Hazards, 2017, 91(1): 37–68.
[7] Hu F Y, Yeung C H, Yang S N, et al. Recovery of infrastructure networks after localised attacks. Scientific Reports, 2016, 6: 24522.
[8] Yadav N, Chatterjee S, Ganguly A R. Resilience of urban transport network-of-networks under intense flood hazards exacerbated by targeted attacks. Scientific Reports, 2020, 10(1): 10350.
[9] Dehghani M S, Sherali H D. A resource allocation approach for managing critical network-based infrastructure systems. IIE Transactions, 2016, 48(9): 826–837.
[10] Chen L C, Miller-Hooks E. Resilience: An indicator of recovery capability in intermodal freight transport. Transportation Science, 2011, 46(1): 109–123.
[11] Kaviani A, Thompson R G, Rajabifard A, et al. A model for multi-class road network recovery scheduling of regional road networks. Transportation, 2020, 47(1): 109–143.
[12] Omer M, Mostashari A, Nilchiani R. Measuring the resiliency of the Manhattan points of entry in the face of severe disruption. American journal of Engineering and Applied Sciences, 2011, 4(1): 153–161.
[13] Wu Y Y, Hou G Y, Chen S R. Post-earthquake resilience assessment and long-term restoration prioritization of transportation network. Reliability Engineering & System Safety, 2021, 211: 107612.
[14] Merschman E, Doustmohammadi M, Salman A M, et al. Postdisaster decision framework for bridge repair prioritization to improve road network resilience. Transportation Research Record: Journal of the Transportation Research Board, 2020, 2674(3): 81–92.
[15] Ye Q, Ukkusuri S V. Resilience as an objective in the optimal reconstruction sequence for transportation networks. Journal of Transportation Safety & Security, 2015, 7(1): 91–105.
[16] 程杰, 刘杰, 唐智慧. 城市道路网络修复策略研究. 中国安全科学学报, 2012, 22(9): 114–120. (Cheng J, Liu J, Tang Z H. Research on repair strategies of urban road network. China Safety Science Journal, 2012, 22(9): 114–120.)
[17] Zhang W L, Wang N Y. Resilience-based risk mitigation for road networks. Structural Safety, 2016, 62: 57–67.
[18] Luo Z W, Yang B. Towards resilient and smart urban road networks: Connectivity restoration via community structure. Sustainable Cities and Society, 2021, 75: 103344.
[19] Wang X F. Complex networks: Topology, dynamics and synchronization. International Journal of Bifurcation and Chaos, 2002, 12(5): 885–916.
[20] Newman M E J. A measure of betweenness centrality based on random walks. Social Networks, 2005, 27(1): 39–54.
[21] Bonacich P. Power and centrality: A family of measures. American Journal of Sociology, 1987, 92(5): 1170–1182.
[22] Kitsak M, Gallos L K, Havlin S, et al. Identification of influential spreaders in complex networks. Nature Physics, 2010, 6(11): 888–893.
[23] Freeman L C. Centrality in social networks: Conceptual clarification. Social Networks, 2002, 1: 215–239.
[24] Li Q, Zhou T, Lü L Y, et al. Identifying influential spreaders by weighted leaderrank. Physica A: Statistical Mechanics and Its Applications, 2014, 404: 47–55.
[25] Gleich D F. PageRank beyond the web. SIAM Review, 2015, 57(3): 321–363.
[26] Lin J Y, Ban Y F. Complex network topology of transportation systems. Transport Reviews, 2013, 33(6): 658–685.
[27] Sarlas G, Páez A, Axhausen K W. Betweenness-accessibility: Estimating impacts of accessibility on networks. Journal of Transport Geography, 2020, 84: 102680.
[28] Chen A, Yang H, Lo H K, et al. Capacity reliability of a road network: An assessment methodology and numerical results. Transportation Research Part B: Methodological, 2002, 36(3): 225–252.
[29] Scott D M, Novak D C, Aultman-Hall L, et al. Network robustness index: A new method for identifying critical links and evaluating the performance of transportation networks. Journal of Transport Geography, 2006, 14(3): 215–227.
[30] Taylor M A P, Sekhar S V C, D’Este G M. Application of accessibility based methods for vulnerability analysis of strategic road networks. Networks and Spatial Economics, 2006, 6: 267–291.
[31] 杨康, 张仲义. 基于节点重要性的供应链网络风险跨层次评估研究. 系统科学与数学, 2015, 35(1): 110– 120. (Yang K, Zhang Z Y. Researches on risk inter-hierarchy assessment in supply chain network based on node importance. Journal of Systems Science and Mathematical Sciences, 2015, 35(1): 110–120.)
[32] Du Z Y, Tang J J, Qi Y, et al. Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city — China. Physica A: Statistical Mechanics and Its Applications, 2020, 539: 122926.
PDF(5527 KB)

233

Accesses

0

Citation

Detail
Sections
Recommended

/

〈 〉