Identification and optimization of traffic bottleneck with signal timing

In urban transportation network, traffic congestion is likely to occur at traffic bottlenecks. The signal timing at intersections together with static properties of left-turn and straight-through lanes of roads are two significant factors causing traffic bottlenecks. A discrete-time model of traffic bottleneck is hence developed to analyze these two factors, and a bottleneck indicator is introduced to estimate the comprehensive bottleneck degree of individual road in regional transportation networks universally, the identification approaches are presented to identify traffic bottlenecks, bottleneck-free roads, and bottle- neck-prone roads. Based on above work, the optimization method applies ant colony algorithm with ef- fective green time as decision variables to find out an optimal coordinated signal timing plan for a re- gional network. In addition, a real experimental transportation network is chosen to verify the valida- tion of bottleneck identification. The bottleneck identification approaches can explain the features of oc- currence and dissipation of traffic congestion in a certain extent, and the bottleneck optimization meth- od provides a new way to coordinate signal timing at intersections to mitigate traffic congestion.

Underdetermined Blind Source Separation of Adjacent Satellite Interference Based on Sparseness

The problem of underdetermined blind source separation of adjacent satellite interference is proposed in this paper. Density Clustering algorithm(DC-algorithm) presented in this article is different from traditional methods. Sparseness representation has been applied in underdetermined blind signal source separation. However, some difficulties have not been considered, such as the number of sources is unknown or the mixed matrix is ill-conditioned. In order to find out the number of the mixed signals, Short Time Fourier Transform(STFT) is employed to segment received mixtures. Then, we formulate the blind source signal as cluster problem. Furthermore, we construct Cost Function Pair and Decision Coordinate System by using density clustering. At the end of this paper, we discuss the performance of the proposed method and verify the novel method based on several simulations. We verify the proposed method on numerical experiments with real signal transmission, which demonstrates the validity of the proposed method.