International Journal of Modern Physics C

We compare the efficiencies and robustness of four transport networks that can be possibly formed as a result of deliberate city planning. The networks are constructed based on their spatial resemblance to the cities of Manhattan (lattice), Sudan (random), Beijing (single-blob) and Greater Cairo (dual-blob). For a given type, a genetic algorithm is employed to obtain an optimized set of the bus routes. We then simulate how commuter travels using Yen's algorithms for k shortest paths on an adjacency matrix. The cost of traveling such as walking between stations is captured by varying the weighted sums of matrices. We also consider the number of transfers a posteriori by looking at the computed shortest paths. With consideration to distances via radius of gyration, redundancies of travel and number of bus transfers, our simulations indicate that random and dual-blob are more efficient than single-blob and lattice networks. Moreover, dual-blob type is least robust when node removals are targeted but is most resilient when node failures are random. The work hopes to guide and provide technical perspectives on how geospatial distribution of a city limits the optimality of transport designs.

Two aspects of fractal networks are considered: the community structure and the class structure, where classes of nodes appear as a consequence of a local symmetry of nodes. The analyzed systems are the networks constructed for two selected symmetric fractals: the Sierpinski triangle and the Koch curve. Communities are searched for by means of a set of differential equations. Overlapping nodes which belong to two different communities are identified by adding some noise to the initial connectivity matrix. Then, a node can be characterized by a spectrum of probabilities of belonging to different communities. Our main goal is that the overlapping nodes with the same spectra belong to the same class.

The weaving section, as a typical bottleneck, is one source of vehicle conflicts and an accident-prone area. Traffic accident will block lanes and the road capacity will be reduced. Several models have been established to study the dynamics around traffic bottlenecks. However, little attention has been paid to study the complex traffic dynamics influenced by the combined effects of bottleneck and accident. This paper presents a cellular automaton model to characterize accident-induced traffic behavior around the weaving section. Some effective control measures are proposed and verified for traffic management under accident condition. The total flux as a function of inflow rates, the phase diagrams, the spatial-temporal diagrams, and the density and velocity profiles are presented to analyze the impact of accident. It was shown that the proposed control measures for weaving traffic can improve the capacity of weaving section under both normal and accident conditions; the accidents occurring on median lane in the weaving section are more inclined to cause traffic jam and reduce road capacity; the capacity of weaving section will be greatly reduced when the accident happens downstream the weaving section.

Unsuitable driving behaviors often lead to the occurrence of traffic accidents. To reduce accidents and to prolong human life, simulated investigations are highly desirable to evaluate the effect of traffic safety in terms of number of traffic accidents. In this paper, a three-lane traffic flow model is proposed to analyze the probability of the occurrence of traffic accidents on highway. We define appropriate driving rules for the forward moving and lane changing of the vehicles. Three types of vehicle accidents are designed to investigate the relationships between different driving behaviors and traffic accidents. We simulate four road driving strategies, and compute the traffic flow, velocity, lane-changing frequency and the probability of the occurrence of traffic accidents for different road driving strategies. According to the simulation and analysis, it is shown that the probability of the occurrence of traffic accidents can be reduced by using the specified road driving strategies. Additionally, we found that the occurrence of traffic accidents can be avoided when the slow vehicles are suitably constrained to move on a three-lane highway.

Recently, the controllability of complex networks has attracted wide attention of researchers. The main contribution comes from Liu et al. who proposed the structural controllability as an analytical framework for making predictions regarding the control of directed networks in Nature. Since then, the controllability of many model and real networks has been deeply investigated except deterministic complex networks. In this paper, we focus on studying the controllability of deterministic complex networks. We examine six typical deterministic networks, the simulation results show that the minimum number of driver nodes grows linearly with network size. When the network size is large enough, the controllability approximates to a constant not more than 0.4, indicating that the deterministic networks are relatively easy to control. Furthermore, we investigate the characteristics of driver nodes in deterministic complex networks, finding that the driver nodes tend to avoid high degree nodes but to have high clustering coefficients.

A model of opinion dynamics with two types of agents as social actors are presented, using the Ising thermodynamic model as the dynamics template. The agents are considered as opportunists which live at sites and interact with the neighbors, or fanatics/missionaries which move from site to site randomly in persuasion of converting agents of opposite opinion with the help of opportunists. Here, the moving agents act as an external influence on the opportunists to convert them to the opposite opinion. It is shown by numerical simulations that such dynamics of opinion formation may explain some details of consensus formation even when one of the opinions are held by a minority. Regardless the distribution of the opinion, different size societies exhibit different opinion formation behavior and time scales. In order to understand general behavior, the scaling relations obtained by comparing opinion formation processes observed in societies with varying population and number of randomly moving agents are studied. For the proposed model two types of scaling relations are observed. In fixed size societies, increasing the number of randomly moving agents give a scaling relation for the time scale of the opinion formation process. The second type of scaling relation is due to the size dependent information propagation in finite but large systems, namely finite-size scaling.

Considering the weight of a node and the coupled strength of two interdependent nodes in the different networks, we propose a method to assign the initial load of a node and construct a new cascading load model in the interdependent networks. Assuming that a node in one network will fail if its degree is 0 or its dependent node in the other network is removed from the network or the load on it exceeds its capacity, we study the influences of the assortative link (AL) and the disassortative link (DL) patterns between two networks on the robustness of the interdependent networks against cascading failures. For better evaluating the network robustness, from the local perspective of a node we present a new measure to qualify the network resiliency after targeted attacks. We show that the AL patterns between two networks can improve the robust level of the entire interdependent networks. Moreover, we obtain how to efficiently allocate the initial load and select some nodes to be protected so as to maximize the network robustness against cascading failures. In addition, we find that some nodes with the lower load are more likely to trigger the cascading propagation when the distribution of the load is more even, and also give the reasonable explanation. Our findings can help to design the robust interdependent networks and give the reasonable suggestion to optimize the allocation of the protection resources.

This study describes the behavior of bubbles rising under gravity using the Shan and Chen-type multicomponent multiphase lattice Boltzmann method (LBM) [X. Shan and H. Chen, Phys. Rev. E47, 1815 (1993)]. Two-dimensional (2D) single bubble motions were simulated, considering the buoyancy effect for which the topology of the bubble was characterized by the nondimensional Eötvös (Eo), and Morton (M) numbers. In this study, a new approach based on the "effective buoyancy" was adopted and proven to be consistent with the expected bubble shape deformation. This approach expands the range of effective density differences between the bubble and the liquid that can be simulated. Based on the balance of forces acting on the bubble, it can deform from spherical to ellipsoidal shape with skirts appearing at high Eo number. A benchmark computational case for qualitative and quantitative validation was performed using COMSOL Multiphysics based on the level set method. Simulations were conducted for 1 ≤ Eo ≤ 100 and 3 × 10^-6 ≤ M ≤ 2.73 × 10^-3. Interfacial tension was checked through simulations without gravity, where Laplace's law was satisfied. Finally, quantitative analyses based on the terminal rise velocity and the degree of circularity was performed for various Eo and M values. Our results were compared with both the theoretical shape regimes given in literature and available simulation results.

This paper studies the effect of vehicles' changing lanes in the original Biham–Middleton–Levine traffic flow model. According to local vehicular information, the dynamics allows for vehicles changing their rows or columns. Simulation results show that the intermediate stable phase identified in the original model can be observed and maintained in a wide range of the vehicle density in our model. Some new kinds of space configurations have been first presented and discussed. One also notes that the geometric structure of such intermediate stable phases is highly regular for square and rectangular aspect rations.

In this paper, a new rumor spreading model which quantifies a specific rumor spreading feature is proposed. The specific feature focused on is the important role the event ambiguity plays in the rumor spreading process. To study the impact of this event ambiguity on the spread of rumors, the probability p(t) that an individual becomes a rumor spreader from an initially unaware person at time t is built. p(t) reflects the extent of event ambiguity, and a parameter c of p(t) is used to measure the speed at which the event moves from ambiguity to confirmation. At the same time, a principle is given to decide on the correct value for parameter c A rumor spreading model is then developed with this function added as a parameter to the traditional model. Then, several rumor spreading model simulations are conducted with different values for c on both regular networks and ER random networks. The simulation results indicate that a rumor spreads faster and more broadly when c is smaller. This shows that if events are ambiguous over a longer time, rumor spreading appears to be more effective, and is influenced more significantly by parameter c in a random network than in a regular network. We then determine parameters of this model through data fitting of the missing Malaysian plane, and apply this model to an analysis of the missing Malaysian plane. The simulation results demonstrate that the most critical time for authorities to control rumor spreading is in the early stages of a critical event.

The simultaneous flow of oil and water through a horizontal pipe is a common occurrence during petroleum industrial processes. Characterizing the flow behavior underlying horizontal oil–water flows is a challenging problem of significant importance. In order to solve this problem, we carry out experiment to measure multivariate signals from different flow patterns and then propose a novel modality transition-based network to analyze the multivariate signals. The results suggest that the local betweenness centrality and weighted shortest path of the constructed network can characterize the transitions of flow conditions and further allow quantitatively distinguishing and uncovering the dynamic flow behavior underlying different horizontal oil–water flow patterns.

In this paper, we use the version of the nonequilibrium Zaklan model via agent-based Monte-Carlo simulations to study the problem of the fluctuations of the tax evasion on a heterogeneous agents community of honest and tax evaders citizens. The time evolution of this system is performed by a nonequilibrium model known as majority-vote model, but with a different probability for each agent to disobey the majority vote of its neighbors.