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.

An enroute ATC simulation experiment for sector capacity estimation

Abstract: A series of enroute ATC simulation experiments was conducted to estimate the airspace capacity of existing sectors in Japan and to evaluate a new route structure for accommodating the traffic increase in the future. A simulation method for capacity estimation and the results of the experiments focusing on the controller's workload limit are presented. From the results of regression analysis, the relationship between the airspace capacity and the controller's intervention in the traffic flow is discussed. About 40% increase of capacity is attained with an introduction of parallel routes and traffic flow management. >

Analysis of network-level traffic states using locality preservative non-negative matrix factorization

Abstract: In this paper, we propose to perform clustering and temporal prediction on network-level traffic states of large-scale traffic networks. Rather than analyzing dynamics of traffic states on individual links, we study overall spatial configurations of traffic states in the whole network and temporal dynamics of global traffic states. With our analysis, we can not only find out typical spatial patterns of global traffic states in daily traffic scenes, but also acquire long-term general predictions of the spatial patterns, which could be used as prior knowledge for modeling temporal behaviors of traffic flows. For this purpose, we use a locality preservation constraints based non-negative matrix factorization (LPNMF) to obtain a low-dimensional representation of network-level traffic states. Clustering and temporal prediction are then performed on the proposed compact representation. Experiments on realistic simulated traffic data are provided to check and illustrate the validity of our proposed approach.

Network Anomaly Traffic Detection Algorithm Based on SVM

Abstract: In order to guarantee the high level of network security and improve the user experience of the network, in this paper, we propose an effective network anomaly traffic detection algorithm. Firstly, six types of network features are used in our work, such as 1) Number of source IP address, 2) Number of source port number, 3) Number of destination IP address, 4) Number of destination port number, 5) Number of packet type, 6) Number of distinct packets with same packet size. Afterwards, we discuss how to generate normalized entropy for the features which are exploited in the network anomaly traffic detection. Secondly, we convert the network traffic anomaly detection problem to a classification problem, and proposed a hybrid PSO-SVM model to solve it. Finally, experimental results demonstrate that the proposed method can detect different network traffic anomaly behaviors with high accuracy.

A Multi-Threaded Execution Model for the Agent-based SEMSim Traffic Simulation

Abstract: An efficient simulation execution engine is crucial for agent-based traffic simulation. Depending on the size of the simulation scenario the execution engine would have to update several thousand agents during a single time step. This update may also include route calculations which are computationally expensive. The ability to dynamically re-calculate the route of agents is a feature often not required in classical microscopic traffic simulations. However, for the agent-based traffic simulation which is part of the Scalable Electro-Mobility Simulation (SEMSim) platform, the routing ability of agents is an important feature. In this paper, we describe a multi-threaded simulation engine that explicitly supports routing capabilities for every agent. In addition, we analyse the efficiency and performance of our execution model in the context of a Singapore-based simulation scenario.

A network traffic prediction approach based on multifractal modeling

Abstract: This work extends the notion of the widely mentioned and used fractional Brownian traffic model in the literature. Extensive experimental investigations indicate that the proposed traffic model, named extended fractional Brownian traffic, can capture not only the self-similar properties, but also the inherent multifractal characteristics of those traffic flows found in modern communication networks. Additionally, the structure of this traffic model is taken into account in a traffic prediction algorithm that benefits from the more accurate traffic modeling. The experimental results clearly point out the advantages of using the proposed model in traffic modeling as well as in traffic prediction.