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22 December 2021 Research on invulnerability optimization of urban agglomeration railroad coupling traffic network based on the improved PSO algorithm
Zhicheng Yang, Chengbing Li
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Proceedings Volume 12058, Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021); 120581A (2021) https://doi.org/10.1117/12.2619727
Event: 5th International Conference on Traffic Engineering and Transportation System (ICTETS 2021), 2021, Chongqing, China
Abstract
In order to improve the ability of urban agglomeration traffic network to resist sudden disasters, this paper constructs railroad coupling traffic network and its capacity optimization framework by considering the impact of cascading failure. Then network invulnerability is optimized by adjusting site resources. Firstly, rail-road coupling traffic network model and cascading failure model are constructed based on the characteristics of rail and road networks, and random attack strategy and invulnerability measurement indexes are defined. Secondly, this paper improves particle swarm optimization with adaptive mutation (MPSO) based on initial position mapping, inertia weight and mutation strategy, and further constructs node capacity optimization based on the MPSO. Finally, taking Hu-Bao-E-Yu urban agglomeration as an example, the node capacity optimization algorithm is simulated, and its invulnerability is tested. The results show that the MPSO algorithm has high convergence accuracy and speed, and can overcome premature convergence. Simultaneously, the network optimized by PSO and MPSO algorithm shows better invulnerability than unoptimized network and the optimization effect of MPSO is more prominent.
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Zhicheng Yang and Chengbing Li "Research on invulnerability optimization of urban agglomeration railroad coupling traffic network based on the improved PSO algorithm", Proc. SPIE 12058, Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021), 120581A (22 December 2021); https://doi.org/10.1117/12.2619727
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KEYWORDS
Particle swarm optimization
Optimization (mathematics)
Particles
Roads
Failure analysis
Computer simulations
Scientific research
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