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A convenient urban transportation network facilitates a high-quality life and a high-growth economy. Due to cascading failures being a ticklish question triggering continuous road congestion, the maintenance plan is momentous to restore the urban transportation network. Considering fault edges are removed and cars slowly drive out of these edges to ease traffic congestion, a traffic distribution model is proposed to analyze the cascading failures process. To resume the transportation network, this paper proposes a maintenance optimization with minimizing maintenance time. It recovers the cascading failures from two perspectives: the intra-area maintenance model and the inter-area maintenance model. At last, a transportation network of a city in China is regarded as a case study to illuminate the feasibility of the proposed models. The results show that on the premise of dividing traffic areas, it is reasonable to adopt the intra-area maintenance plan for cascading failures. Compared with the previous travel data, the inter-area maintenance plan saves more time.
Czasopismo
Rocznik
Tom
Strony
art. no. 168826
Opis fizyczny
Bibliogr. 25 poz., rys., tab., wykr.
Twórcy
autor
- School of Management, Zhengzhou University, Zhengzhou 450001, China
autor
- School of Management, Zhengzhou University, Zhengzhou 450001, China
autor
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
autor
- Kent Business School, University of Kent, Canterbury, Kent CT2 7FS, UK
Bibliografia
- 1. Aksoy I C, Mutlu M M, Alver Y. Urban Road network maintenance scheduling using ant colony optimization. Gazi University Journal of Science, 2021; 34(3): 710-716. https://doi.org/10.35378/gujs.789519
- 2. Bai G, Wang H, Zheng X, Dui H, Xie M. Improved resilience measure for component recovery priority in power grids. Frontiers of Engineering Management, 2021, 8, 545–556. https://doi.org/10.1007/s42524-021-0161-5
- 3. Barahimi A H, Eydi A, Aghaie A. Multi-modal urban transit network design considering reliability: multi-objective bi-level optimization. Reliability Engineering and System Safety, 2021; 216: 107922. https://doi.org/10.1016/j.ress.2021.107922
- 4. Behiri W, Belmokhtar-Berraf S, Chu C B. A robust ant colony metaheuristic for urban freight transport scheduling using passenger rail network. Expert Systems with Applications, 2023; 213: 118906. https://doi.org/10.1016/j.eswa.2022.118906
- 5. Bai G, Xu B, Chen X, Zhang Y, Tao J. Searching for d-MPs for all level d in multistate two-terminal networks without duplicates, IEEE Transactions on Reliability, 2021; 70(1): 319-330. https://doi.org/10.1109/TR.2020.3004971
- 6. Chen Y J, Cowling P, Polack F, Remde S, Mourdjis P. Dynamic optimization of preventative and corrective maintenance schedules for a large-scale urban drainage system. European Journal of Operational Research, 2017; 257(2): 494-510. https://doi.org/10.1016/j.ejor.2016.07.027
- 7. Chen T G, Wu S W, Yang J J, Cong G D, Li G F. Modeling of emergency supply scheduling problem based on reliability and its solution algorithm under variable road network after sudden-onset disasters. Complexity, 2020; 2020: 7501891. https://doi.org/10.1155/2020/7501891
- 8. Dui H Y, Zheng X Q, Zhao Q Q, Fang Y N. Preventive maintenance of multiple components for hydraulic tension systems. Eksploatacja i Niezawodność – Maintenance and Reliability 2021; 23(3): 489–497. https://doi.org/10.17531/ein.2021.3.9
- 9. Dui H, Wei X, Xing L. A new multi-criteria importance measure and its applications to risk reduction and safety enhancement. Reliability Engineering & System Safety, 2023, 235, 109275. https://doi.org/10.1016/j.ress.2023.109275
- 10. Dui H, Dong X, Chen L, Wang Y. IoT-Enabled Fault Prediction and Maintenance for Smart Charging Piles. IEEE Internet of Things Journal, 2023, online. https://doi.org/10.1109/JIOT.2023.3285206
- 11. Dui H Y, Chen S, Zhou Y J, Wu S M. Maintenance analysis of transportation networks by the traffic transfer principle considering node idle capacity. Reliability Engineering and System Safety, 2022; 221: 108386. http://doi.org/10.1016/j.ress.2022.108386
- 12. Dui H, Liu M, Song J, Wu S. Importance measure-based resilience management: Review, methodology and perspectives on maintenance. Reliability Engineering and System Safety, 2023, 237, 109383. http://doi.org/10.1016/j.ress.2023.109383
- 13. Jia H F, Li F Y, Yang L, Luo Q Y, Li Y X. Dynamic cascading failure analysis in congested urban road networks with self-organization, IEEE Access, 2022; 8: 17916-17925. https://doi.org/10.1109/ACCESS.2020.2968048
- 14. Kowalski M, Izdebski M, Żak J, Gołda P, Manerowski J. Planning and management of aircraft maintenance using a genetic algorithm. Eksploatacja i Niezawodność – Maintenance and Reliability, 2021; 23(1): 143-153. https://doi.org/10.17531/ein.2021.1.15
- 15. Li Y, Liang X, Dong S. Reliability optimization design method based on multi-level surrogate model. Eksploatacja i Niezawodność – Maintenance and Reliability, 2020; 22(4): 638-650. https://doi.org/10.17531/ein.2020.4.7
- 16. Liu T, Bai G, Tao J, Zhang Y, Fang Y, Xu B. Modeling and evaluation method for resilience analysis of multi-state networks. Reliability Engineering & System Safety, 2022; 226: 108663. http://doi.org/10.1016/j.ress.2022.108663
- 17. Motter A E, Lai Y C. Cascade-based attacks on complex networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 2002; 66(6): 065102. http://doi.org/10.1103/PhysRevE.66.065102
- 18. Nguyen T, Lin Y. Investigation of the influence of transit time on a multistate transportation network in tourism. Eksploatacja i Niezawodność – Maintenance and Reliability, 2021; 23(4): 670-677. https://doi.org/10.17531/ein.2021.4.9
- 19. Peng Y, Mo Z Y, Liu S. Passenger’s routes planning in stochastic common-lines’ multi-modal transportation network through integrating genetic algorithm and monte carlo simulation. Archives of Transport, 2021; 59(3): 73-92. https://doi.org/10.5604/01.3001.0015.0123
- 20. Shi Y, Xiang Y S, Xiao H, Xing L D. Joint optimization of budget allocation and maintenance planning of multi-facility transportation infrastructure systems. European Journal of Operational Research, 2021; 288(2): 382-393. https://doi.org/10.1016/j.ejor.2020.05.050
- 21. Shen Y, Song G, Xu H, Xie Y. Model of node traffic recovery behavior and cascading congestion analysis in networks. Physica A: Statistical Mechanics and its Applications 2020; 545: 123422. http://doi.org/10.1016/j.physa.2019.12342
- 22. Saeedmanesh M, Geroliminisa N. Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks. Transportation Research Procedia 2017; 23: 962-979. https://doi.org/10.1016/j.trpro.2017.05.053
- 23. Yin R, Yuan H L, Wang J, Zhao N, Liu L. Modeling and analyzing cascading dynamics of the urban road traffic network. Physica A: Statistical Mechanics and its Applications, 2021; 566: 125600. http://doi.org/10.1016/j.physa.2020.12560
- 24. Zhang C, Zhang Y D, Dui H Y, Wang S P, Tomovic M M. Importance measure-based maintenance strategy considering maintenance costs. Eksploatacja i Niezawodność – Maintenance and Reliability 2022; 24(1): 15–24. https://doi.org/10.17531/ein.2022.1.3
- 25. Zheng Z J, Wang Z L, Zhu L Y, Jiang H. Determinants of the congestion caused by a traffic accident in urban road networks. Accident Analysis and Prevention, 2020; 136: 105327. http://doi.org/10.1016/j.aap.2019.105327
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-92a52a46-ae5b-4ed0-954e-ee647f06164e