Traffic dynamics on coupled spatial networks

Wei, Du; Zhou, Xing-Lian; Chen, Zhe; Cai, Kai-Yuan; Cao, Xianbin

doi:10.1016/j.chaos.2014.07.009

Cited by 46 publications

(23 citation statements)

References 39 publications

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“…The speed ratio β norm i (t) is larger in the morning and evening rush hours for all three SDNs, because the operation speeds of buses are lower in peak hours. The speed ratio β norm i (t) was considered as the key factor influencing the mode-selection behavior of travelers and, thus, the coupling strength of layered networks [21,24,29]. However, our empirical results indicate that passengers did not react to speed ratio changes (Figs.…”

Section: Preliminary Observation On Three (Case Studies) Sdnscontrasting

confidence: 48%

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Understanding coupling dynamics of public transportation networks

et al. 2018

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Subway and bus networks work as an integrated multiplex transportation system and play an indispensable role in modern big cities. Even though a variety of works have investigated the coupling dynamics of multiplex transportation networks, empirical data that validates the determinant coupling factors are still lacking. In this paper, we employ smartcard data of 2.4 million subway and bus passengers in Shenzhen, China to study the coupling dynamics of subway and bus networks. Surprisingly, the coupling of subway and bus networks is not notably influenced by the time-varying speed ratio of the two network layers but is jointly determined by the distribution of travel demands and transportation facilities. Our findings highlight the important role of real travel demand data in analyzing the coupling dynamics of multiplex transportation networks. They also suggest that the speed ratio of different network layers, which was regarded as a key factor in determining coupling strength, has a negligible effect on travelers' route selections, and thus the coupling dynamics of multiplex transportation networks.

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Section: Preliminary Observation On Three (Case Studies) Sdnscontrasting

confidence: 48%

“…The results indicated that travel demand plays crucial quantitative and qualitative roles [27]. How interconnections between network layers influence traffic congestion [28] and how speed ratio influences network capacity and travel time [29] were also investigated.…”

Section: Introductionmentioning

confidence: 99%

Understanding coupling dynamics of public transportation networks

et al. 2018

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“…Examples of such infrastructures include power grid and communication networks, where a communication network controls a power grid network and the power grid network in turn provides power to the communication network 20 . Owing to this coupling relationship they can be modeled as interdependent networks 21,22,23 . In two interdependent networks A and B, a node in network A is interdependent on its coupling node in network B when the node in network A needs the coupling node from network B to function properly, and vice versa.…”

Section: Chen Hong Et Almentioning

confidence: 99%

Cascading failures with local load redistribution in interdependent Watts–Strogatz networks

Chen

Zhang

Wei

et al. 2016

Int. J. Mod. Phys. C

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Cascading failures of loads in isolated networks have been studied extensively over the last decade. Since 2010, such research has extended to interdependent networks. In this paper, we study cascading failures with local load redistribution in interdependent WattsStrogatz networks. The effects of rewiring probability and coupling strength on the resilience of interdependent Watts-Strogatz networks have been extensively investigated. It has been found that, for small values of the tolerance parameter, interdependent networks are more vulnerable as rewiring probability increases. For larger values of the tolerance parameter, the robustness of interdependent networks firstly decreases and then increases as rewiring probability increases. Coupling strength has a different impact on robustness.For low values of coupling strength the resilience of interdependent networks decreases with the increment of the coupling strength until it reaches a certain threshold value. For values of coupling strength above this threshold, the opposite effect is observed. Our results are helpful to understand and design resilient interdependent networks.

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“…Yan et al developed a routing algorithm using degree and distance information [10], and Echenique et al propose one that uses distance and the number of packets stored at each node [11]. Du et al analyzed traffic dynamics in coupled spatial networks and proposed a dynamic allocation mechanism to deliver capacity [12,13]. Tang et al proposed two routing algorithms that employed a global and a local self-adjusting traffic awareness protocol [14].…”

Section: Introductionmentioning

confidence: 99%

“…Typical examples of static combinatorial optimization problems are the traveling salesman problem [1,2], the quadratic assignment problem [3] and the vehicle routing problem [4], whereas examples of dynamic combinatorial optimization problems are the packet routing problem [5][6][7][8][9][10][11][12][13][14][15][16][17] and the traffic flow control problem [18]. In static combinatorial optimization problems, the search space of the solution does not change.…”

Section: Introductionmentioning

confidence: 99%

An improved routing algorithm using chaotic neurodynamics for packet routing problems

Morita

Kimura

2018

NOLTA

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Abstract:Combinatorial optimization problems consist of static problems such as the traveling salesman problem and the quadratic assignment problem, and dynamic problems such as the packet routing problem and the traffic flow control problem. In static combinatorial optimization problems, the search space for the solution does not change over time and, therefore, neither does the optimal solution. On the contrary, in dynamic combinatorial optimization problems, the search space always changes. Thus, there is no guarantee that the optimal solution for one iteration also applies to the next iteration. In the context of dynamic combinatorial optimization problems, we propose in this paper a heuristic routing method that uses chaotic neurodynamics and degree information to solve the packet routing problem. Numerical experiments showed that the proposed method improves average arrival rate by approximately 130% over the conventional shortest hop method.

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Traffic dynamics on coupled spatial networks

Cited by 46 publications

References 39 publications

Understanding coupling dynamics of public transportation networks

Understanding coupling dynamics of public transportation networks

Cascading failures with local load redistribution in interdependent Watts–Strogatz networks

An improved routing algorithm using chaotic neurodynamics for packet routing problems

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