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On the foundation of the original backpressure-based traffic light control algorithm, a distributed cooperative backpressure-based traffic light control method is proposed in this paper. The urban traffic network is modeled as a smart agent-controlled queuing network, in which the intersection agents exchange the queue length information and the selected activating light phase information of neighboring intersections through communications and determine the activating light phase at each time slot according to local traffic information. The improved phase pressure computation method considers the phase state of downstream intersections instead of only the queue length of the local intersections. Light phase switching coordination among adjacent intersections is achieved using the consensus-based bundle algorithm, in which the cooperative light phase switching problem is viewed as a task assignment issue among adjacent intersections. Simulation results illustrated that the proposed cooperative backpressurebased traffic light control method obtained better performance than the original backpressure-based and fixed-time traffic control methods.

Travel time is one of the most critical indexes to describe urban traffic operating states. How to obtain accurate and robust travel time estimates, so as to facilitate to make traffic control decision-making for administrators and trip-planning for travelers, is an urgent issue of wide concern. This paper proposes a reliable estimation method of urban link travel time using multi-sensor data fusion. Utilizing the characteristic analysis of each individual traffic sensor data, we first extract link travel time from license plate recognition data, geomagnetic detector data and floating car data, respectively, and find that their distribution patterns are similar and follow logarithmic normal distribution. Then, a support degree algorithm based on similarity function and a credibility algorithm based on membership function are developed, aiming to overcome the conflicts among multi-sensor traffic data and the uncertainties of single-sensor traffic data. The reliable fusion weights for each type of traffic sensor data are further determined by integrating the corresponding support degree with credibility. A case study was conducted using real-world data from a link of Jingshi Road in Jinan, China and demonstrated that the proposed method can effectively improve the accuracy and reliability of link travel time estimations in urban road systems.

In VANET (vehicular ad hoc network) environment, the successive vehicle position data actually are discrete, so the key to the moving vehicle modeling is to effectively reduce the updating frequency of the position data so as to alleviate the communication and database management load. This paper proposes vehicle position data updating strategy with packet repetition based on Kalman filter predicting. Firstly, we design a position data updating model based on Kalman filter difference predicting equations. Then, we design a packet repetition mode decision algorithm, which is applied to deliver vehicle position updating data. The model with packet repetition can not only generate position updating data according to preset threshold, but also decide packet repetition mode related to the distance of two adjacent vehicles in order to reduce data loss. Both simulated highway and realistic urban road experimental results show that vehicle position data updating frequency could be obviously reduced and the reliability of the communication is greatly improved through packet repetition mechanism by using this position updating strategy.

110supplementaries to address spectrum scarcity in a cellular network. These schemes are popular such as heterogeneous network (HetNet) [1,2], device-to-device (D2D) communications [3], etc. Those technologies take advantage of low power transmission and spacial diversity so as to offload partial service burden from the central network. Specifically, D2D communication allows two close terminals to build up a direct communication controlled by a third party (such as BS) [4]. Moreover, D2D communication also will play an important role in communication networks of industrial area, e.g., smart substations, power plant for Energy-internet [5][6][7][8][9].Thanks to the proximity of two D2D users, low transmit power is competent for highspeed transmission, which enable D2D communication to reuse the same resource with cellular users (CU) to further improve the spectrum efficiency. However, such frequency overlapping may cause severe co-channel interference to the prioritized cellular network. Thus, most existing literatures [10-13] mainly focus on proposing appropriate resource allocation and interference coordination strategies in order to exploit the advantages of D2D communication without causing too much interference, especially to the cellular users. The optimal power control for underlaying Abstract: It has been shown that the deployment of device-to-device (D2D) communication in cellular systems can provide better support for local services. However, improper design of the hybrid system may cause severe interference between cellular and D2D links. In this paper, we consider transceiver design for the system employing multiple antennas to mitigate the interference. The precoder and decoder matrices are optimized in terms of sum mean squared error (MSE) and capacity, respectively. For the MSE minimization problem, we present an alternative transceiver optimization algorithm. While for the non-convex capacity maximization problem, we decompose the primal problem into a sequence of standard convex quadratic programs for efficient optimization. The evaluation of our proposed algorithms for performance enhancement of the entire D2D integrated cellular system is carried out through simulations.

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The parameter determination of viscoelastic material is a multi-variable, multi-aim nonlinear optimization problem, which made the optimization process very complicated. In this paper a hybrid optimal algorithm was proposed to determine the viscoelastic parameters in the constitutive relation according to the experimentally obtained mechanical properties. This algorithm merges the Broydon-Fletcher-GoldfarbShanno search into a genetic algorithm framework as a basic operator in order to enhance the local search capability. The proposed hybrid algorithm not only can reduce the iterative times greatly but can abolish the limitation of initial parameter values. Nonlinear material characteristic curve-fitting was carried out using the proposed algorithm and other existing approaches. And the comparison results show this algorithm is accurate and effective. The numerical simulation and experimental study of viscoelastic cantilever beam also indicates that the finite element formulation and the calculative viscoelastic model parameters are reliable. The proposed optimization method can be extended to further complex parameter estimation researches.