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.

Traffic monitoring and analysis for the optimization of a 3G network

Abstract: Recent years have recorded a surge of research activities on IP traffic monitoring, enabled by the availability of monitoring hardware and large-scale storage at accessible costs. More recently, passive monitoring has been applied to operational 3G networks. The passive observation of network traffic, coupled with advanced traffic-analysis methods, can be a powerful and cost-effective means to infer the network status and localize points of performance degradation without requiring complete access to all network elements. Furthermore, the availability of high-quality traces can be exploited to predict the load of the network under hypothetical conditions, variations of the actual network configuration at the capturing time. Both approaches can be useful for some engineering and reoptimization tasks that are commonly encountered in the lifetime of an operational 3G network. In abstract terms, the availability of high-quality traces can greatly empower the measurement-based optimization cycle, with human experts in the loop, thus driving an already operational 3G network toward improved performances. In this article we discuss the contribution that traffic monitoring and analysis (TMA) can provide to the optimization of an operational 3G network

Traffic flow forecasting using a spatio-temporal Bayesian network predictor

Abstract: A novel predictor for traffic flow forecasting, namely spatiotemporal Bayesian network predictor, is proposed. Unlike existing methods, our approach incorporates all the spatial and temporal information available in a transportation network to carry our traffic flow forecasting of the current site. The Pearson correlation coefficient is adopted to rank the input variables (traffic flows) for prediction, and the best-first strategy is employed to select a subset as the cause nodes of a Bayesian network. Given the derived cause nodes and the corresponding effect node in the spatio-temporal Bayesian network, a Gaussian Mixture Model is applied to describe the statistical relationship between the input and output. Finally, traffic flow forecasting is performed under the criterion of Minimum Mean Square Error (M.M.S.E.). Experimental results with the urban vehicular flow data of Beijing demonstrate the effectiveness of our presented spatio-temporal Bayesian network predictor.

Traffic Engineering vs. Content Distribution: A Game Theoretic Perspective

Abstract: In this paper we explore the interaction between content distribution and traffic engineering. Because a traffic engineer may be unaware of the structure of content distribution systems or overlay networks, this management of the network does not fully anticipate how traffic might change as a result of his actions. Content distribution systems that assign servers at the application level can respond very rapidly to changes in the routing of the network. Consequently, the traffic engineer's decisions may almost never be applied to the intended traffic. We use a game-theoretic framework in which infinitesimal users of a network select the source of content, and the traffic engineer decides how the traffic will route through the network. We formulate a game and prove the existence of equilibria. Additionally, we present a setting in which equilibria are socially optimal, essentially unique, and stable. Conditions under which efficiency loss may be bounded are presented, and the results are extended to the cases of general overlay networks and multiple autonomous systems.

Near real-time traffic routing

Abstract: A near real-time physical transportation network routing system comprising: a traffic simulation computing grid and a dynamic traffic routing service computing grid. The traffic simulator produces traffic network travel time predictions for a physical transportation network using a traffic simulation model and common input data. The physical transportation network is divided into a multiple sections. Each section has a primary zone and a buffer zone. The traffic simulation computing grid includes multiple of traffic simulation computing nodes. The common input data includes static network characteristics, an origin-destination data table, dynamic traffic information data and historical traffic data. The dynamic traffic routing service computing grid includes multiple dynamic traffic routing computing nodes and generates traffic route(s) using the traffic network travel time predictions.

A discrete-event mesoscopic traffic simulation model for hybrid traffic simulation

Abstract: The paper presents a mesoscopic traffic simulation model, particularly suited for the development of integrated meso-micro traffic simulation models. The model combines a number of the recent advances in simulation modeling, such as discrete-event time resolution and combined queue-server and speed-density modeling, with a number of new features such as the ability to integrate with microscopic models to create hybrid traffic simulation. The ability to integrate with microscopic models extends the area of use to include evaluation of ITS systems, which often require the detailed modeling of vehicles in areas of interest, combined with a more general modeling of large surrounding areas to capture network effects of local phenomena. The paper discusses the structure of the model, presents a framework for integration with micro models, and illustrates its validity through a case study with a congested network north of Stockholm. It also compares its performance with a hybrid model applied to the same network