Showing papers on "Network traffic simulation published in 2015"

Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies

Abstract: This paper presents a computationally efficient and theoretically rigorous dynamic traffic assignment (DTA) model and its solution algorithm for a number of emerging emissions and fuel consumption related applications that require both effective microscopic and macroscopic traffic stream representations. The proposed model embeds a consistent cross-resolution traffic state representation based on Newell’s simplified kinematic wave and linear car following models. Tightly coupled with a computationally efficient emission estimation package MOVES Lite, a mesoscopic simulation-based dynamic network loading framework DTALite is adapted to evaluate traffic dynamics and vehicle emission/fuel consumption impact of different traffic management strategies.

A Review of Network Traffic Analysis and Prediction Techniques.

Abstract: Analysis and prediction of network traffic has applications in wide comprehensive set of areas and has newly attracted significant number of studies. Different kinds of experiments are conducted and summarized to identify various problems in existing computer network applications. Network traffic analysis and prediction is a proactive approach to ensure secure, reliable and qualitative network communication. Various techniques are proposed and experimented for analyzing network traffic including neural network based techniques to data mining techniques. Similarly, various Linear and non-linear models are proposed for network traffic prediction. Several interesting combinations of network analysis and prediction techniques are implemented to attain efficient and effective results. This paper presents a survey on various such network analysis and traffic prediction techniques. The uniqueness and rules of previous studies are investigated. Moreover, various accomplished areas of analysis and prediction of network traffic have been summed.

Research on urban public traffic network with multi-weights based on single bus transfer junction

Abstract: Regarding single bus transfer junction as a research object, this paper constructs the urban traffic network models with multi-weights taking different bus lines in bus transfer junction as the network nodes, that is, the urban traffic network with multi-weights is given different properties weights at every edge. According to the method of network split, the complex network with multi-weights is split into several different single weighted complex networks. Then, we study the global synchronization of the new network model by changing congestion degrees, transfers coefficient and passenger flow density between different bus lines. Finally, analytical and simulated results are given to show the impact of different properties weights to the public traffic network balance.

A simulation of the cascading failure of a complex network model by considering the characteristics of road traffic conditions

Abstract: Using the dual method, we start with a traditional road traffic network with a constructed logic network with small-world characteristics and construct the complex network of road traffic. After analyzing and comparing with other complex networks, the time delay, restorative, and other characteristics are presented for the complex network of road traffic, and then, the cascading failure model of the complex network is simulated. The simulation results show that using different time delays, an incident dissipation factor and load capacity can reasonably avoid a cascading failure, and they can remove its effects. In addition, our results provide value and guidance for building a road traffic network that prevents and removes the cascading failure of a road network.

Spatio-temporal tensor completion for imputing missing internet traffic data

Abstract: Network traffic data consists of Traffic Matrix (TM), which represents the volumes of traffic between Origin and Destination (OD) pairs in the network. It is a key input parameter of network engineering tasks. However, direct measurement of the OD pairs traffic is usually not feasible. Even good traffic measurement systems can suffer from errors, missing data. So obtaining the ODs traffic precisely is a challenge. Existing completion methods often perform poorly for network traffic estimation. Their recovery accuracy tends to be significantly worse when the data loss rate is high. Taking into account network traffic lower-dimensional latent structure and traffic hidden characteristic, a tensor (multi-way array) is introduced to model a time series of pure spatial traffic matrices in this paper. To recover the missing entries in tensors of traffic data, a novel spatio-temporal tensor completion method has been proposed. This approach not only takes advantage of tensor decomposition and its lower-dimensional representation, but also well takes into account traffic spatio-temporal properties. The extensive experiments with the real-world traffic trace data show that the proposed method can significantly reduce the missing traffic data recovery errors and achieve satisfactory completion accuracy comparing with the state-of-the-art completion methods.

Load balancing for multicast traffic in SDN using real-time link cost modification

Abstract: In this paper we propose an approach for applying traffic load balancing to multicast traffic through real-time link cost modification in a software defined network (SDN) controller. We present an SDN controller architecture supporting traffic monitoring, group management, and multicast traffic routing. An implemented prototype is described, and this prototype is used to implement shortest path multicast routing techniques which make use of the real-time state of traffic flows in the network. This prototype is evaluated through experimentation in Mininet emulated wide area networks. Evaluation is presented in terms of resulting network performance metrics focusing on the distribution of traffic flows. Our results demonstrate that real-time modification of links costs produces statistically significant improvements in traffic distribution metrics, with an average improvement of up to 52.8% in traffic concentration relative to shortest-path routing. This indicates that SDN enables the use of real-time modification of link cost functions as an effective technique for implementing traffic load balancing for multicast traffic.

Traffic Flow Theory: Characteristics, Experimental Methods, and Numerical Techniques

Abstract: Creating Traffic Models is a challenging task because some of their interactions and system components are difficult to adequately express in a mathematical form. Traffic Flow Theory: Characteristics, Experimental Methods, and Numerical Techniques provide traffic engineers with the necessary methods and techniques for mathematically representing traffic flow. The book begins with a rigorous but easy to understand exposition of traffic flow characteristics including Intelligent Transportation Systems (ITS) and traffic sensing technologies. * Includes worked out examples and cases to illustrate concepts, models, and theories* Provides modeling and analytical procedures for supporting different aspects of traffic analyses for supporting different flow models* Carefully explains the dynamics of traffic flow over time and space

Robustness analysis of the European air traffic network

Abstract: The European air traffic network (ATN), consisting of a set of airports and area control centres, is highly complex. The current indicator of its performance, air traffic flow management delays, is insufficient for planning and management purposes. Topological analysis of ATNs of this kind has highlighted betweenness centrality (BC) as an indicator of network robustness, although such an indicator assumes no knowledge of actual traffic flows and the network's operational characteristics. This paper conducts topological and operational analyses of the European ATN in order to derive a more relevant and appropriate indicator of robustness. By applying a flow maximisation model to the network influenced by a range of capacity reductions at the local level, we propose a new index called the Relative Area Index (RAI). The RAI quantifies the importance of an individual node relevant to the performance of the entire network when it suffers from capacity reduction at a local scale. Air traffic data from three typ...

Vehicle-pedestrian interaction for mixed traffic simulation

Abstract: Simulation of real-world traffic scenarios is widely needed in virtual environments. Different from many previous works on simulating vehicles or pedestrians separately, our approach aims to capture the realistic process of vehicle-pedestrian interaction for mixed traffic simulation. We model a decision-making process for their interaction based on a gap acceptance judging criterion and then design a novel environmental feedback mechanism for both vehicles' and pedestrians' behavior-control models to drive their motions. We demonstrate that our proposed method can soundly model vehicle-pedestrian interaction behaviors in a realistic and efficient manner and is convenient to be plugged into various traffic simulation systems. Copyright © 2015 John Wiley & Sons, Ltd.

A novel hybrid prediction algorithm to network traffic

Abstract: Network traffic describes the characteristics and users’ behaviors of communication networks. It is a crucial input parameter of network management and network traffic engineering. This paper proposes a new prediction algorithm to network traffic in the large-scale communication network. First, we use signal analysis theory to transform network traffic from time domain to time-frequency domain. In the time-frequency domain, the network traffic signal is decomposed into the low-frequency and high-frequency components. Second, the gray model is exploited to model the low-frequency component of network traffic. The white Gaussian noise model is utilized to describe its high-frequency component. This is reasonable because the low-frequency and high-frequency components, respectively, represent the trend and fluctuation properties of network traffic, while the gray model and white Gaussian noise model can well capture the characteristics. Third, the prediction models of low-frequency and high-frequency components are built. The hybrid prediction algorithm is proposed to overcome the problem of network traffic prediction in the communication network. Finally, network traffic data from the real network is used to validate our approach. Simulation results indicate that our algorithm holds much lower prediction error than previous methods.

Spatiotemporal Traffic Matrix Synthesis

Abstract: Traffic matrices describe the volume of traffic between a set of sources and destinations within a network. These matrices are used in a variety of tasks in network planning and traffic engineering, such as the design of network topologies. Traffic matrices naturally possess complex spatiotemporal characteristics, but their proprietary nature means that little data about them is available publicly, and this situation is unlikely to change. Our goal is to develop techniques to synthesize traffic matrices for researchers who wish to test new network applications or protocols. The paucity of available data, and the desire to build a general framework for synthesis that could work in various settings requires a new look at this problem. We show how the principle of maximum entropy can be used to generate a wide variety of traffic matrices constrained by the needs of a particular task, and the available information, but otherwise avoiding hidden assumptions about the data. We demonstrate how the framework encompasses existing models and measurements, and we apply it in a simple case study to illustrate the value.

Network traffic flow management using machine learning

Abstract: A system and method for management of network traffic flow for one or more networks of interconnected computing devices, including predicting one or more types of network traffic data flows using a Machine Learning (ML) classifier, and updating the ML classifier according to identified changes in the network traffic data flows. Using software-defined-networking (SDN) tools and an updated ML classifier, training data is generated to formulate accurate new predictions of the one or more types of network traffic data flows. Network traffic flow is regulated based on the generated new predictions.

Traffic speed prediction method for urban networks — an ANN approach

Abstract: The paper proposes a traffic speed prediction algorithm for urban road traffic networks. The motivation of the prediction is to provide short time forecast in order to support ITS (Intelligent Transport System) functionalities, such as traveler information systems, route guidance (navigation) systems, as well as adaptive traffic control systems. A potential and efficient solution to this problem is the application of a soft computing method. Namely, an artificial neural network (ANN) is used for the forecast by involving the measured speed patterns. The ANN is trained by using data produced by Vissim (a microscopic road traffic simulator) simulations. The proposed algorithm is developed and analyzed on a real-word test network (part of downtown in Budapest).

Characteristics of Optimal Solutions to the Sensor Location Problem

Abstract: The sensor location problem is that of locating the minimum number of traffic sensors at intersections of a road network such that the traffic flow on the entire network can be determined. In this paper, we provide a new necessary condition on the location of these sensors to enable the traffic flow throughout the network to be computed. This condition is not sufficient in general, but we show that for a large class of problem instances, the condition is sufficient. Many typical road networks are included in this category, and we show how our condition can be used to inform traffic sensor placement.

Stochastic Geometry Models for 5G Heterogeneous Mobile Networks

Abstract: Next generation wireless networks are expected to provide thousand times higher capacity comparing to existing LTE (Long Term Evolution) networks. Increasing of network capacity can be achieved by combining both spatial and spectral network densification. Influence of spatial network densification on future tremendous capacity growth is very high due to limited spectral resources. Therefore, optimal network planning is an important challenge for future heterogeneous networks with high number of small cells. Network geometry modeling is the significant part of network design and analysis. Multi-tier heterogeneous networks are very complex in terms of topology that requires new advanced approaches to the network planning. In we study the most recent solutions on the stochastic network geometry and analyze their feasibility for different scenarios of heterogeneous network. Studied approached provides good tractability of the mobile network topology and behavior. Poisson point processes combining with Voronoi tessellation provides good approximation of network nodes deployment and coverage areas. We also study feasibility of stochastic models for different buildings environment, including hyper dense skyscrapers environment. Hybrid network model combining Poisson point process with K-means clustering method was developed for D2D (Device-to-Device) heterogeneous network. Proposed model reflects random user behavior and estimate available groups for D2D transmission. Performance simulation of single tier, multi-tier and D2D based heterogeneous network shows that heterogeneous network provides significantly higher performance in terms of throughput and signal-to-interference-plus-noise ratio. Future research directions for network geometry have been outlined in this paper including emerging hot topic of combing the stochastic and deterministic network modelling.

Traffic signal control of a road network using MILP in the MPC framework

Abstract: This paper investigates the significance of a traffic signal control scheme that simultaneously adjusts all signal parameters, i.e., cycle time, split time and offset, in a road network. A novel framework of model predictive control (MPC) is designed that overcomes the limitations of other MPC based traffic signal control strategies, which are mostly restricted to control only split or green time in a fixed cycle ignoring signal offset. A simple macroscopic model of traffic tailored to MPC is formulated that describes traffic dynamics in the network at a short sampling interval. The proposed framework is demonstrated using a small road network with dynamically changing traffic flows. The parameters of the proposed model are calibrated by using data obtained from detailed microscopic simulation that yields realistic statistics. The model is transformed into a mixed logical dynamical system that is suitable to a finite horizon, and traffic signals are optimized using mixed integer linear programming (MILP) for a given performance index. The framework makes the signals flexibly turn to red and green by adapting quickly to any changes in traffic conditions. Results are also verified by microscopic traffic simulation and compared with other signal control schemes.

Real-Time Corrected Traffic Correlation Model for Traffic Flow Forecasting

Abstract: This paper focuses on the problems of short-term traffic flow forecasting. The main goal is to put forward traffic correlation model and real-time correction algorithm for traffic flow forecasting. Traffic correlation model is established based on the temporal-spatial-historical correlation characteristic of traffic big data. In order to simplify the traffic correlation model, this paper presents correction coefficients optimization algorithm. Considering multistate characteristic of traffic big data, a dynamic part is added to traffic correlation model. Real-time correction algorithm based on Fuzzy Neural Network is presented to overcome the nonlinear mapping problems. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling methods.

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

Analysis of topological characteristics in air traffic situation networks

Abstract: In the face of growing demand for air traffic, there is a clear need to measure how difficult a given air traffic situation looks. Currently, the concept of air traffic complexity is usually used t...

Wireless Sensor Network for Real Time Healthcare Monitoring: Network Design and Performance Evaluation Simulation

Abstract: Nowadays, Wireless sensor network (WSN) technologies are considered as potential solution healthcare monitoring applications. Different researches focus on network designing network for health care monitoring wireless sensor network (HCWSN), especially in the physical design of the HCWSN. However, work to evaluate the performance these network designs is largely lacking. This paper presents a HCWSN network design and simulation study to evaluate the performance in different scenarios such as network topologies, routing and media access control protocols. A practical WSN for HCWSN and a prototype SPO2 device integrated with WSN node have been designed. The testing results are also described in this paper.

Simulation-based synthesis for approximately optimal urban traffic light management

Abstract: Suitable control measures and strategies must be taken to counteract the reduced throughput and the degradation of the network infrastructure caused by traffic congestion in urban networks. This paper studies and analyzes the performance of an adaptive traffic-responsive strategy that manages the traffic light parameters (the cycle time and the split time) in an urban network to reduce traffic congestion. The proposed traffic-responsive strategy adopts a nearly-optimal control formulation: first, an (approximate) solution of the HJB is parametrized via an appropriate Lyapunov positive definite matrix; then, the solution is updated via a procedure that generates candidate control strategies and selects at each iteration the best one based on the estimation of close-to-optimality and the information coming from the simulation model of the network (simulation-based design). Simulation results obtained using an AIMSUN model of the traffic network of Chania, Greece, an urban traffic network containing many varieties of junction staging, demonstrate the efficiency of the proposed approach.

Network Traffic Prediction Based on Neural Network

Abstract: Predicting and modeling network traffic is always an important subject in network capability studying. The aim of this article is to explore a new network model in order to describe and predict the network character accurately. Firstly wavelet neural network is investigated and its disadvantages are analyzed. In order to overcome disadvantages of wavelet neural network, genetic algorithm is used to optimize weight and threshold of neural network. At last, the proposed algorithm is used in network traffic prediction and the results show the proposed scheme has good performance.

Predicting route utilization and non-redundant failures in network environments

Abstract: A network analysis module may obtain information including but not limited to network traffic and topology information for a network environment including multiple separate networks and physical connections between the networks. The module generates a network topology model including but not limited to extreme-case failure or break information according to the network topology information, and applies the historical network traffic information to the network topology model to predict future expected normal traffic load and extreme-case traffic load for each route over the physical connections between the networks. Output may include one or more reports for each route, each report indicating the historical and predicted traffic levels for both normal and extreme-case scenarios of a respective route.

VEF Traces: A Framework for Modelling MPI Traffic in Interconnection Network Simulators

Abstract: Simulation is often used to evaluate the behaviour and measure the performance of computing systems. Specifically, in high-performance interconnection networks, the simulation has been extensively considered to verify the behaviour of the network itself and to evaluate its performance. In this context, network simulation must be fed with network traffic, also referred to as network workload, whose nature has been traditionally synthetic. These workloads can be used for the purpose of driving studies on network performance, but often such workloads are not accurate enough if a realistic evaluation is pursued. For this reason, other non-synthetic workloads have gained popularity over last decades since they are best to capture the realistic behaviour of existing applications. In this paper, we present the VEF traces framework, a self-related trace model, and all their associated tools. The main novelty of this framework is that, unlike existing ones, it does not provide a network simulation framework, but only offers an MPI task simulation framework, which allows one to use the MPI-based network traffic by any third-party network simulator, since this framework does not depend on any specific simulation platform.

Network Traffic Classification with Improved Random Forest

Abstract: Accurate network traffic classification is significant to numerous network activities, such as QoS and network management etc. While port-based or payload-based classification methods are becoming more and more difficult, Machine Learning methods are promising in many aspects. In this paper, we improve the standard Random Forest by setting the variable selection probability according to the importance of the corresponding variable to classify network traffic. Our test results show that the Improved Random Forest has better classification performance. And it takes less time to build the model.