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Exploring efficient control strategies for heterogeneously congested urban networks remains a big research challenge. The theory of macroscopic fundamental diagram (MFD) provides a new perspective for network-wide congestion control decisions. This paper proposes a coordinated perimeter control strategy for multiregion heterogeneous networks based on optimized transfer flows. First, a two-layer network partitioning method is presented to capture spatial heterogeneity dynamics of urban networks. For this partition, traffic flow equilibrium model based on MFD and multiagent based hierarchical traffic management scheme are built. Then, an improved multinomial logit model is developed for deriving optimized transfer flows among multiple congested regions. A coordinated perimeter control strategy using model predictive control is further proposed, which is aimed at tracking desired accumulations of each congested region. As a case study, the proposed control strategy is applied to the downtown network of Jinan, China, using simulation analysis. The results demonstrate that it can achieve balanced network flow distribution and increased mobility.

Congestion is a kind of expression of instability of traffic network. Traffic signal control keeping traffic network stable can reduce the congestion of urban traffic. In order to improve the efficiency of urban traffic network, this study proposes a decentralized traffic signal control strategy based on backpressure algorithm used in Wi-Fi mesh networks for packets routing. Backpressure based traffic signal control algorithm can stabilize urban traffic network and achieve maximum throughput. Based on original backpressure algorithm, the variant parameter and penalty function are considered to balance the queue differential and capacity of downstream links in urban traffic network. For each traffic phase of intersections, phase weight is computed using queue differential and capacity of downstream links, which fixed the deficiency of infinite queue capacity in original backpressure algorithm. It is proved that the extended backpressure traffic signal control algorithm can maintain stability of urban traffic network, and also can prevent queue spillback, so as to improve performance of whole traffic network. Simulations are carried out in Vissim using Vissim COM programming interface and Visual Studio development tools. Evaluation results illuminate that it can get better performance than the backpressure algorithm just based on queue length differential in average queue length and delay of traffic network.

Network-wide traffic control strategies (e.g. perimeter control and route guidance) in urban networks have recently been mainly studied to relieve or postpone congestion based on the theory of macroscopic fundamental diagram (MFD). Nevertheless, these studies are mostly applied to the statically partitioned networks or the dynamic networks that fail to fully consider traffic state prediction, conflicting with strongly spatiotemporal variability of traffic congestion or objective of active traffic management (ATM). This paper proposes a methodology to dynamically identify critical congestion warning areas from heterogeneous urban road networks, which aids to design efficient perimeter control approaches. In the methodology, a dynamic directed weighted network is built on the base of the link connectivity and the real-time traffic loads, and a link travel time prediction method based on Kalman filter is developed to calibrate directed weight values and undirected input values for links. With the undirected link input information, a dynamic congestion warning community detection method which consists of three consecutive steps is developed. Firstly, it could capture emergence of new congestion areas based on the definition of congestion seed intersection. Secondly, expansion and regression of each congested area could be achieved with the objectives of spatial compactness and traffic condition homogeneity. Thirdly, a two-level merging algorithm of adjacent different congestion areas is designed utilizing modularity model in community detection of complex networks. The proposed methodology is validated using ground truth data from downtown network of Jinan City in China. The results show that the proposed algorithms can efficiently track congestion evolutionary processes and effectively detect congestion warning areas from the test real network.

The development of society requires high-quality talents, which propels colleges and universities to cultivate graduates with both theoretical and practical abilities. As an applied undergraduate university, Beijing City University has always attached importance to cultivating students' practical ability based on theoretical teaching. In “Project Planning and Control”, a module for the major of project management, the task-driven method is used for practical training. The college aims to improve students' ability to apply the theory through practical training scenarios, real-life training tasks, assessments and other key steps. In order to test the effect of practical training, SPSS statistical software is used to perform paired sample T test and correlation analysis on the usual scores and final scores. The analysis results show that the design of practical training is reasonable, achieving good results.

Fifth-generation (5G) communication network puts forward a demanding requirement for spectrum resources. To improve spectral efficiency, a novel uplink cooperative nonorthogonal multiple access (NOMA) model is proposed. In particular, the proposed scheme enables the relay node to perform a cooperative transmission and uplink transmission simultaneously during the cooperative phase, at the expense of a slight decrease in the signal reception reliability. Moreover, the optimal power allocation strategy of relay node is given. Relay selection criteria is also presented. We analyze the outage probability and outage throughput in both ideal and non-ideal successive interference cancellation (SIC) conditions. Simulation results confirm that the proposed scheme can obtain higher throughput than other cooperative NOMA schemes.

In today’s Internet era, “blended learning” is a relatively advanced and effective learning method, which is of great significance for promoting curriculum teaching reforms and improving teaching quality. This paper explores the blended teaching design method of the platform course of “Management”, clarifies the teaching objectives, overall design and unit design, and implements in teaching. A comprehensive evaluation of the teaching effect was made in three areas of the usage of the platform, analysis of students’ assessment grades and feedback of students’ questionnaire, which shows that blended teaching has achieved good results. There are still some problems in the implementation of blended teaching, including the quantity and quality of teaching resources, as well as the students’ assessment results need to be improved, and need to be improved in future research.

Being attracted by offloading local services to adjacent users, the operator allows two proximity users to build up a direct communication link, which is a new supplementary to current cellular network and called device-to-device (D2D) communication. Due to the low power radiation and occupation of the licensed band, D2D users can share the resource with cellular users fully controlled by cellular network to improve the local frequency efficiency. Due to the combinatorial and nonconvex nature of formulated problem, solving such problem optimally is a nearly impossible task. However, we propose an upper bound computation method, which can be seen as a benchmark for resource sharing scheme design. We also propose another two suboptimal schemes, i.e., suboptimal resource sharing scheme I with parallel computation structure which can achieve optimality in the most cases, and suboptimal resource sharing scheme II with relatively low complexity at the cost of acceptable performance loss. Moreover, through extensive numerical results, we find that both suboptimal resource sharing schemes can outperform the existed works. To conclude, our work actually provide a tradeoff between complexity and performance.

This paper presents a suitability evaluation approach to assess whether the LTE-based wireless private network satisfies the communication needs of smart grid applications. This approach can obtain different evaluation indexes including reliability, delay and throughput when the business attributes are used as the initial inputs by analyzing the MAC and PHY layer models and coverage characteristics. Then combined with the business analysis model, the applicability of wireless private network can be evaluated according to different power business needs. In order to verify the use of the proposed evaluation method, a simulation analysis of the suitability of the LTE 1.8GHz wireless private network is conducted for the businesses of power distribution and utilization.

The accurate topology of distribution network has a significant impact on power flow calculation, reliability evaluation and system operation. However, due to frequently reconfiguration and modification of switches, the topology is hard to maintain. In this paper, a novel two-stage method is proposed to identify the connection relationships between distribution transformers and feeders. In stage 1, a new correlation indicator is adopted to screen out the unsure distribution transformers which are doubted of being incorrectly connected. In stage 2, unsure distribution transformers are assigned to the most likely feeders, and then incorrectly connected distribution transformers are found out by clustering correction. The proposed two-stage method can help operators to correct the topology errors. Cases studies in actual distribution network verified the effectiveness of the proposed method and correlation indicator.

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 backpressure-based traffic light control method obtained better performance than the original backpressure-based and fixed-time traffic control methods.

Macroscopic fundamental diagram (MFD) characterizes a unimodal and low-scatter relationship between traffic accumulation and trip completion rate for homogenous urban network, providing a new perspective for the development of regional congestion control strategies. This paper contributes to the state-of-the-art by proposing a perimeter traffic control strategy for single urban congested region with MFD model and boundary conditions. For an urban protected region, the MFD is introduced to capture network traffic dynamics and optimal capacity. When regional accumulation exceeds the optimal capacity, the restricted inflow distribution considering boundary condition constraints of real-time traffic flows and queue dynamics is used to formulate a perimeter traffic flow control strategy for adjusting traffic signal timing on the periphery of region. To proactively reduce the possibility of queue spillbacks at border links, the dynamic queue-dependent border gated points are also integrated into the proposed control method. The simulation experiment was performed using part of urban network in Jinan, China. The results demonstrate the proposed perimeter control method alleviates the issue of congestion and improves traffic performance in the protected region and at the boundaries.

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.

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.

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.

The customized wireless devices play a key role in information exchanging for various electrical facilities. However, the development of the devices is hindered by diversity of interfaces (RS-232/485, Ethernet, USB, etc.), high cost, outdoor environment, etc. In this work, we proposed a ‘system on module’ solution to address above issues. We optimize and spare the storage space of baseband chip for embedded control system to replace the independent chips, which dramatically reduces the size, cost and power consumption of devices. Meanwhile, our SOM wireless devices can support multi-interfaces and thus realize ‘plug and play’. Moreover, our developed wireless devices allow multimode wireless communications including operator's wireless networks LTE FDD/TDD, UMTS, GSM and self-built LTE-G 1800MHz network. Last but not least, we can embedded positioning chip and the client of management system in the wireless devices for the sake of operation and maintenance.

In view of all kinds of network attacks which may affect the network security of the smart grid system, an attack detection algorithm is proposed, feature extraction and training to the data such as server logs is carried out based on Hidden Markov model (HMM) to achieve the network attack detection in smart grid system. By the proposed algorithm, analysis on real data of the smart grid system in one power company is carried out, which can effectively identify common types and numbers of network attack types. Through the proposed smart grid network attack detection algorithm, network security risks in the smart grid system can be found, and the level of network security is improved.

Development of precise active traffic control strategies urgently requires real-time estimation for operational metrics in transportation systems satisfying the level of smaller spatial granularity simultaneously. This paper proposed a probability approach to estimate real-time lane-based queue length using license plate recognition (LPR) data. The method first developed a nested logit model to depict initial lane selection and then improved a lane changing model, which can obtain lane-based equivalent cumulative arrival-departure curve. Thus queue length is obtained combined with input-output model. Finally, validation results show that the proposed approach can capture the real-time variations of queue length and achieve reasonable estimation accuracy for maximum cycle queue length.

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.