Network traffic simulation

About: Network traffic simulation is a research topic. Over the lifetime, 4535 publications have been published within this topic receiving 74606 citations.

NESIS: a neutral schema for a web-based simulation model exchange service across heterogeneous simulation software

Abstract: Generally, the generation of a simulation model is both a time-and cost-consuming task within processes for performing simulation. Therefore, reuse of the simulation model that is once generated based on a huge amount of information/data is an essential and important issue for simulation engineers. It is more difficult to exchange a reusable simulation model across heterogeneous simulation systems because these systems have different structures and modes of expressing information even if the contents are the same. A neutral file format has been proposed as a methodology to solve this kind of problem in the exchange of simulation models between simulation and other manufacturing applications. Towards this end, in this article, NEutral SImulation Schema (NESIS) is defined based on several neutral file formats such as NIST SDM, SDX and PLM Services; then, a system architecture is designed and implemented based on the proposed neutral simulation model for an interoperable environment. The target commercial simulation applications in this article are QUEST and Plant Simulation. Therefore, interfaces for each of these commercial systems are developed. As a case study, NESIS and the developed simulation model exchange system are practically applied to a Korean automotive parts assembly line. So the result of application shows that it is possible to effectively work collaboratively in a distributed environment. To elaborate, if simulation modelling is once completed in a commercial simulation application, there is no more need to model for different kinds of applications. Therefore, the time and effort for modelling the same target is greatly reduced for different kinds of simulation applications. The reduction in the time and effort for simulation will ensure effective concurrent engineering.

Traffic signal optimization using cyclically expanded networks

Abstract: Traditionally, the coordination of multiple traffic signals and the traffic assignment problem in an urban street network are considered as two separate optimization problems. However, it is easy to see that the traffic assignment has an influence on the optimal signal coordination and, vice versa, a change in the signal coordination changes the optimal traffic assignment. In this article, we present a cyclically time-expanded network and a corresponding mixed integer linear programming formulation for simultaneously optimizing both the coordination of traffic signals and the traffic assignment in an urban street network. Although the new cyclically time-expanded network provides a model of both traffic and signals close to reality, it still has the advantage of a linear objective function. Using this model, we compute optimized signal coordinations and traffic assignment on real-world street networks. To evaluate the practical relevance of the computed solutions, we conduct extensive simulation experiments using two established traffic simulation tools that reveal the advantages of our model. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 653, 244-261 2015

The Learning and Prediction of Application-level Traffic Data in Cellular Networks

Abstract: Traffic learning and prediction is at the heart of the evaluation of the performance of telecommunications networks and attracts a lot of attention in wired broadband networks. Now, benefiting from the big data in cellular networks, it becomes possible to make the analyses one step further into the application level. In this paper, we firstly collect a significant amount of application-level traffic data from cellular network operators. Afterwards, with the aid of the traffic "big data", we make a comprehensive study over the modeling and prediction framework of cellular network traffic. Our results solidly demonstrate that there universally exist some traffic statistical modeling characteristics, including ALPHA-stable modeled property in the temporal domain and the sparsity in the spatial domain. Meanwhile, the results also demonstrate the distinctions originated from the uniqueness of different service types of applications. Furthermore, we propose a new traffic prediction framework to encompass and explore these aforementioned characteristics and then develop a dictionary learning-based alternating direction method to solve it. Besides, we validate the prediction accuracy improvement and the robustness of the proposed framework through extensive simulation results.

A fast network partition method for large-scale urban traffic networks

Abstract: In order to control the large-scale urban traffic network through hierarchical or decentralized methods, it is necessary to exploit a network partition method, which should be both effective in extracting subnetworks and fast to compute. In this paper, a new approach to calculate the correlation degree, which determines the desire for interconnection between two adjacent intersections, is first proposed. It is used as a weight of a link in an urban traffic network, which considers both the physical characteristics and the dynamic traffic information of the link. Then, a fast network division approach by optimizing the modularity, which is a criterion to distinguish the quality of the partition results, is applied to identify the subnetworks for large-scale urban traffic networks. Finally, an application to a specified urban traffic network is investigated using the proposed algorithm. The results show that it is an effective and efficient method for partitioning urban traffic networks automatically in real world.