Complex-network-based traffic network analysis and dynamics: A comprehensive review

Zhang, Mengyao; Huang, Tao; Guo, Zhaoxia; He, Zhenggang

doi:10.1016/j.physa.2022.128063

Cited by 25 publications

(10 citation statements)

References 143 publications

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“…Additionally, power transmission networks, as one of the most widely used network types, have been the subject of extensive research. These studies focus on maintaining these networks, identifying critical points, and establishing alternative paths to support crisis management [9,10,11]. Similar investigations have been carried out in the domain of transportation network efficiency, proposing solutions for managing road blockages under various conditions [9,10,11].…”

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confidence: 94%

“…These studies focus on maintaining these networks, identifying critical points, and establishing alternative paths to support crisis management [9,10,11]. Similar investigations have been carried out in the domain of transportation network efficiency, proposing solutions for managing road blockages under various conditions [9,10,11]. Another topic under analysis in the realm of complex networks is user behavior in social networks.…”

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confidence: 97%

“…These networks are intertwined in different forms or interconnected as multilayer systems. Consequently, modelling networks with simple graphs cannot adequately address a significant portion of the complexities in this domain [4,10]. In complex interconnected and heterogeneous networks, the relationships between nodes are not uniform throughout the network.…”

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confidence: 99%

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Complex Networks Disintegration Based on Learning Automata

Eslahi,

Masoumi

2023

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Complex network disintegration stands as a paramount challenge within network science, playing a pivotal role in the mitigation of malicious network behaviour. Beyond its defensive role, it offers a strategy with broader applicability, encompassing risk prediction for networks with positive attributes. Complex networks, deeply rooted in graph theory, serve as a fundamental modelling framework across diverse problem domains, ranging from social networks, communications, and telecommunications to security, power distribution, information transmission, and even weather analysis with geographical implications. Yet, the disintegration of real-world networks carries tangible costs, necessitating the development of cost-effective methods a pressing concern when confronting such networks. Additionally, real-world networks often exhibit heterogeneity, mandating practical considerations in proposed solutions. Traditionally, complex network disintegration has relied on graph theory-based algorithms and heuristic methods. Recent years, however, have witnessed the incorporation of learning algorithms that engage dynamically with complex environments. Reinforcement learning, owing to its interactive nature with the environment, emerges as a well-suited methodology. Moreover, this paper introduces an innovative approach leveraging the Learning Automata algorithm to enhance existing disintegration strategies. This research explores the central role of complex network disintegration, bridging conventional graph theory techniques with cutting-edge reinforcement learning methods. The outcome is a more comprehensive and adaptable framework for addressing real-world network challenges, spanning defence against malicious networks with the optimized cost in unknown networks.

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Complex Networks Disintegration Based on Learning Automata

Eslahi,

Masoumi

2023

Preprint

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“…Complex network theory (CNT) has been used for the analysis of traveler ows, including passenger ows in public transportation systems (Zhang et al, 2022). Since there exist correlations between topological properties and human activities, CNT can effectively be used for that purpose (Leung et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Although CNT-based models can incorporate nontrivial and dynamic properties of transportation networks, and thus bring crucial information to light (Zhang et al, 2022), their usage for predictions is still limited. Their accuracy, albeit increasing, is still questionable and the most e cient predictors are yet to be found.…”

Section: Introductionmentioning

confidence: 99%

Exploring the association between network centralities and passenger flows in metro systems

Kopsidas

Douvaras

Κεπαπτσόγλου

2023

Preprint

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Network science offers valuable tools for planning and managing public transportation systems, with measures such as network centralities proposed as complementary predictors of ridership. This paper explores the relationship between different cases of passenger flows at metro stations and network centralities of both metro and alternative public transport (substitute) networks; such an association can be useful for managing metro system operations when disruptions occur. For that purpose, linear regression and non-parametric machine learning models are developed and compared. The Athens metro system is used as testbed for developing the proposed methodology. The findings of this study can be used for deriving medium-term ridership estimates in cases of metro disruptions, as the proposed methodology can support contingency plans for both platform and rail track disruptions.

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