For most technical networks, the interplay of dynamics, traffic, and topology is assumed crucial to their evolution. In this Letter, we propose a traffic-driven evolution model of weighted technological networks. By introducing a general strength-coupling mechanism under which the traffic and topology mutually interact, the model gives power-law distributions of degree, weight, and strength, as confirmed in many real networks. Particularly, depending on a parameter W that controls the total weight growth of the system, the nontrivial clustering coefficient C, degree assortativity coefficient r, and degree-strength correlation are all consistent with empirical evidence.

Many weighted scale-free networks are known to have a power-law correlation between strength and degree of nodes, which, however, has not been well explained. We investigate the dynamic behavior of resource-traffic flow on scale-free networks. The dynamical system will evolve into a kinetic equilibrium state, where the strength, defined by the amount of resource or traffic load, is correlated with the degree in a power-law form with tunable exponent. The analytical results agree well with simulations.

In this paper, inspired by the idea that different nodes should play different roles in network synchronization, we bring forward a coupling method where the coupling strength of each node depends on its neighbors' degrees. Compared with the uniform coupled method and the recently proposed Motter-Zhou-Kurths method, the synchronizability of scale-free networks can be remarkably enhanced by using the present coupled method, and the highest network synchronizability is achieved at β = 1 which is similar to a method introduced in [AIP Conf. Proc. 776, 201 (2005)].

As a promising transport in the future, electric vehicles plays an important role in people's lives and energy conservation. Planning of electric vehicle charging stations has a far-reaching significance for the popularity of electric vehicles. In this paper, we discuss the siting problem of electric vehicle charging station and propose a two-step method of optimization method. Firstly, we establish a charging station location model, then use Voronoi diagram to determine the preliminary zone, finally we get this problem optimally solved by immune algorithm.The example verifies feasibility of this model.

The electric demand of EV in the public transportation sector is increasingly important to the future city’s power distribution and even infrastructure construction. According to the characteristic of public transportation, this paper analyzed the influence factors of EV power load and they were divided into three parts. Then a predicting model of EV power load in public transportation based on fuzzy clustering analysis method was put forward. We used BP (Back Propagation) neural network algorithm to solve the fuzzy clustering analysis problem. Finally the predicting model was operated in a practical example. Results showed that this predicting model of EV power load in public transportation based on fuzzy clustering analysis could be appropriately applied in reality.