Showing papers on "Network traffic simulation published in 2016"

Short-term traffic flow rate forecasting based on identifying similar traffic patterns

Abstract: The ability to timely and accurately forecast the evolution of traffic is very important in traffic management and control applications. This paper proposes a non-parametric and data-driven methodology for short-term traffic forecasting based on identifying similar traffic patterns using an enhanced K-nearest neighbor (K-NN) algorithm. Weighted Euclidean distance, which gives more weight to recent measurements, is used as a similarity measure for K-NN. Moreover, winsorization of the neighbors is implemented to dampen the effects of dominant candidates, and rank exponent is used to aggregate the candidate values. Robustness of the proposed method is demonstrated by implementing it on large datasets collected from different regions and by comparing it with advanced time series models, such as SARIMA and adaptive Kalman Filter models proposed by others. It is demonstrated that the proposed method reduces the mean absolute percent error by more than 25%. In addition, the effectiveness of the proposed enhanced K-NN algorithm is evaluated for multiple forecast steps and also its performance is tested under data with missing values. This research provides strong evidence suggesting that the proposed non-parametric and data-driven approach for short-term traffic forecasting provides promising results. Given the simplicity, accuracy, and robustness of the proposed approach, it can be easily incorporated with real-time traffic control for proactive freeway traffic management.

Traffic Engineering in Software-Defined Networking: Measurement and Management

Abstract: As the next generation network architecture, software-defined networking (SDN) has exciting application prospects. Its core idea is to separate the forwarding layer and control layer of network system, where network operators can program packet forwarding behavior to significantly improve the innovation capability of network applications. Traffic engineering (TE) is an important network application, which studies measurement and management of network traffic, and designs reasonable routing mechanisms to guide network traffic to improve utilization of network resources, and better meet requirements of the network quality of service (QoS). Compared with the traditional networks, the SDN has many advantages to support TE due to its distinguish characteristics, such as isolation of control and forwarding, global centralized control, and programmability of network behavior. This paper focuses on the traffic engineering technology based on the SDN. First, we propose a reference framework for TE in the SDN, which consists of two parts, traffic measurement and traffic management. Traffic measurement is responsible for monitoring and analyzing real-time network traffic, as a prerequisite for traffic management. In the proposed framework, technologies related to traffic measurement include network parameters measurement, a general measurement framework, and traffic analysis and prediction; technologies related to traffic management include traffic load balancing, QoS-guarantee scheduling, energy-saving scheduling, and traffic management for the hybrid IP/SDN. Current existing technologies are discussed in detail, and our insights into future development of TE in the SDN are offered.

Research challenges for traffic engineering in software defined networks

Abstract: SDN is an emerging networking paradigm that separates the network control plane from the data forwarding plane with the promise to dramatically improve network resource utilization, simplify network management, reduce operating costs, and promote innovation and evolution. While traffic engineering techniques have been widely exploited for ATM and IP/MPLS networks for performance optimization in the past, the promising SDN networks require novel traffic engineering solutions that can exploit the global network view, network status, and flow patterns/characteristics in order to achieve better traffic control and management. This article discusses the state-of-the-art in traffic engineering for SDN with attention to four cores including flow management, fault tolerance, topology update, and traffic analysis. Challenging issues for SDN traffic engineering solutions are discussed in detail.

Mining Road Network Correlation for Traffic Estimation via Compressive Sensing

Abstract: This paper presents a transport traffic estimation method which leverages road network correlation and sparse traffic sampling via the compressive sensing technique. Through the investigation on a traffic data set of more than 4400 taxis from Shanghai city, China, we observe nontrivial traffic correlations among the traffic conditions of different road segments and derive a mathematical model to capture such relations. After mathematical manipulation, the models can be used to construct representation bases to sparsely represent the traffic conditions of all road segments in a road network. With the trait of sparse representation, we propose a traffic estimation approach that applies the compressive sensing technique to achieve a city-scale traffic estimation with only a small number of probe vehicles, largely reducing the system operating cost. To validate the traffic correlation model and estimation method, we do extensive trace-driven experiments with real-world traffic data. The results show that the model effectively reveals the hidden structure of traffic correlations. The proposed estimation method derives accurate traffic conditions with the average accuracy as 0.80, calculated as the ratio between the number of correct traffic state category estimations and the number of all estimation times, based on only 50 probe vehicles' intervention, which significantly outperforms the state-of-the-art methods in both cost and traffic estimation accuracy.

Analysis of Large-Scale Traffic Dynamics in an Urban Transportation Network Using Non-Negative Tensor Factorization

Abstract: In this paper, we present our work on clustering and prediction of temporal evolution of global congestion configurations in a large-scale urban transportation network. Instead of looking into temporal variations of traffic flow states of individual links, we focus on temporal evolution of the complete spatial configuration of congestions over the network. In our work, we pursue to describe the typical temporal patterns of the global traffic states and achieve long-term prediction of the large-scale traffic evolution in a unified data-mining framework. To this end, we formulate this joint task using regularized Non-negative Tensor Factorization, which has been shown to be a useful analysis tool for spatio-temporal data sequences. Clustering and prediction are performed based on the compact tensor factorization results. The validity of the proposed spatio-temporal traffic data analysis method is shown on experiments using simulated realistic traffic data.

Systems and Methods for Managing Network Traffic with a Network Operator

Abstract: A method for managing network slice enabled traffic on a communications network is disclosed. The network slice is instantiated on the communications network for providing connectivity resources to a network operator using the communications network. The method comprises measuring a traffic level indicative of the traffic enabled by the network slice; and adjusting the traffic enabled by the network slice in accordance with the traffic level and a network operator enabled function associated with the network slice.

An Integrated Simulation System Considering WMN-PSO Simulation System and Network Simulator 3

Abstract: With the fast development of wireless technologies, Wireless Mesh Net-works (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented a simulation system based on Particle Swam Optimization for solving node placement problem in wireless mesh networks, called WMN-PSO. In this paper, we implement an integrated system considering WMN-PSO and net-work simulator 3 (ns-3). For simulation, we consider two WMN architectures. From simulation results, we found that the total received throughput of I/BWMNis higher than Hybrid WMN and the delay of I/B WMN is lower than Hybrid WMN.

Traffic Matrix Prediction and Estimation Based on Deep Learning for Data Center Networks

Abstract: Network traffic analysis is a crucial technique for systematically operating a data center network. Many network management functions rely on exact network traffic information. Although a great number of works to obtain network traffic have been carried out in traditional ISP networks, they cannot be employed effectively in data center networks. Motivated by that, we focus on the problem of network traffic prediction and estimation in data center networks. We involve deep learning techniques in the network traffic prediction and estimation fields, and propose two deep architectures for network traffic prediction and estimation, respectively. We first use a deep architecture to explore the time-varying property of network traffic in a data center network, and then propose a novel network traffic prediction approach based on a deep belief network and a logistic regression model. Meanwhile, to deal with the highly ill-pose property of network traffic estimation, we further propose a network traffic estimation method using the deep belief network trained by link counts. We validate the effectiveness of our methodologies by real traffic data.

Energy efficiency and blocking reduction for tidal traffic via stateful grooming in IP-over-optical networks

Abstract: The growing popularity of high-speed mobile communications, cloud computing, and the Internet of Things (IoT) has reinforced the tidal traffic phenomenon, which induces spatio-temporal disequilibrium in the network traffic load. The main reason for tidal traffic is the large-scale population migration between business areas during the day and residential areas during the night. Traffic grooming provides an effective solution to aggregate multiple fine-grained IP traffic flows into the optical transport layer by flexibly provisioning lightpaths over the physical topology. In this paper, we introduce a comprehensive study on energy efficiency and network performance enhancement in the presence of tidal traffic. We propose and leverage the concept of stateful grooming to apply differentiated provisioning policies based on the state of network nodes. We formulate and solve the node-state-decision optimization problem, which can decide the specific state of network nodes when a certain traffic profile is given, considering the trade-off between energy efficiency and blocking performance with a multi-objective integer linear program (MOILP). Then, we propose an online traffic-aware intelligent differentiated allocation of lightpaths (TIDAL) algorithm, based on stateful grooming and the MOILP, to accommodate the dynamic tidal traffic. Our illustrative numerical results show that the proposed method can achieve a significant performance improvement in an energy-efficient way.

Enhancing the performance of existing urban traffic light control through extremum-seeking

Abstract: Urban traffic light controllers are responsible for maintaining good performance within the transport network. Most existing and proposed controllers have design parameters that require some degree of tuning, with the sensitivity of the performance measure to the parameter often high. To date, tuning has been largely treated as a manual calibration exercise but ignores the effects of changes in traffic condition, such as demand profile evolution due to urban population growth. To address this potential shortcoming, we seek to use a newly developed extremum-seeker to calibrate the parameters of existing urban traffic light controllers in real-time such that a certain performance measure is optimised. The results are demonstrated for three categories of traffic controllers on a microscopic urban traffic simulation. It is demonstrated that the extremum-seeking scheme is able to seek the optimal parameters, with respect to a certain performance measure, for each of these traffic light controllers in an urban, uni-modal traffic environment.

Control system for reducing energy consumption in backbone computer network

Abstract: SUMMARY Network optimization concerned with operational traffic management in existing data networks is typically oriented towards either maximizing throughput in congested networks while providing for adequate transmission quality, or towards balancing the traffic so as to maintain possibly large free capacity for carrying additional (new) traffic. Nowadays, the reduction of power consumption is a new key aspect in the development of modern wired networks. Power management capabilities allow modulating the energy consumption of devices that form a network by putting them into standby state, or by decreasing their performance in case of low incoming traffic volume. This paper presents a framework for backbone network management, which leads to the minimization of the energy used by this network. The policy for dynamic power management of the whole network through energy-aware routing, traffic engineering, and network equipment activity control is introduced and discussed. The concept of the system is to achieve the desired trade-off between total power consumption and the network performance according to the current load, incoming traffic, and user requirements. The effectiveness of our framework is illustrated by means of a numerical study. Copyright © 2012 John Wiley & Sons, Ltd.

The issue of timely delivery of video traffic with controlled loss of quality

Abstract: Need of development of new methods of coding and management of the bit speed of a video traffic for coordination with the capacity of a telecommunication network justifies.

Queuing Systems for the Internet

Abstract: SUMMARY This article proposes a versatile model of a multiservice queueing system with elastic traffic. The model can provide a basis for an analysis of telecommunications and computer network systems, internet network systems in particular. The advantage of the proposed approach is a possibility of a determination of delays in network nodes for a number of

Real-time traffic network state estimation and prediction with decision support capabilities: Application to integrated corridor management

Abstract: This paper presents a real-time traffic network state estimation and prediction system with built-in decision support capabilities for traffic network management. The system provides traffic network managers with the capabilities to estimate the current network conditions, predict congestion dynamics, and generate efficient traffic management schemes for recurrent and non-recurrent congestion situations. The system adopts a closed-loop rolling horizon framework in which network state estimation and prediction modules are integrated with a traffic network manager module to generate efficient proactive traffic management schemes. The traffic network manger adopts a meta-heuristic search mechanism to construct the schemes by integrating a wide variety of control strategies. The system is applied in the context of Integrated Corridor Management (ICM), which is envisioned to provide a system approach for managing congested urban corridors. A simulation-based case study is presented for the US-75 corridor in Dallas, Texas. The results show the ability of the system to improve the overall network performance during hypothetical incident scenarios.

Toward Designing a Quantum Key Distribution Network Simulation Model

Abstract: As research in quantum key distribution network technologies grows larger and more complex, the need for highly accurate and scalable simulation technologies becomes important to assess the practical feasibility and foresee difficulties in the practical implementation of theoretical achievements. In this paper, we described the design of simplified simulation environment of the quantum key distribution network with multiple links and nodes. In such simulation environment, we analyzed several routing protocols in terms of the number of sent routing packets, goodput and Packet Delivery Ratio of data traffic flow using NS-3 simulator.

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.

Network traffic characterisation using flow-based statistics

Abstract: Performing research on live network traffic requires the traffic to be well documented and described. The results of such research are heavily dependent on the particular network. This paper presents a study of network characteristics, which can be used to describe the behaviour of a network. We propose a number of characteristics that can be collected from the networks and evaluate them on five different networks of Masaryk University. The proposed characteristics cover IP, transport and application layers of the network traffic. Moreover, they reflect strong day-night and weekday patterns that are present in most of the networks. Variation in the characteristics between the networks indicates that they can be used for the description and differentiation of the networks. Furthermore, a weak correlation between the chosen characteristics implies their independence and contribution to network description.

Network traffic measurement and analysis

Abstract: Today one cannot think of life without the Internet. The Internet has grown at a very fast pace, which has resulted in heavy Internet traffic. Most of today's internet traffic is due to video streaming services such as YouTube and Netflix. The Average traffic load has risen, and data traffic patterns have also become unpredictable. Therefore, network traffic monitoring and analysis have become essential in order to troubleshoot and resolve problems effectively when they occur, so that network services do not stand still for long durations of time. Traffic monitoring is a technique which constantly monitors the network traffic and notifies the administrator whenever there is an outage. There are many network monitoring tools available for network administrators, which use different monitoring techniques in order to monitor and analyze network traffic. In this paper, we present different network monitoring approaches and different tools that monitor and analyze network traffic. In addition to this, we also present results by comparing different network monitoring tools.

Traffic network micro-simulation model and control algorithm based on approximate dynamic programming

Abstract: This study presents the adaptive traffic signal control algorithm in a distributed traffic network system. The proposed algorithm is based on a micro-simulation model and a reinforcement learning method, namely approximate dynamic programming (ADP). By considering traffic environment in discrete time, the microscopic traffic dynamic model is built. In particular, the authors explore a vehicle-following model using cellular automata theory. This vehicle-following model theoretically contributes to traffic network loading environment in an accessible way. To make the network coordinated, tunable state with weights of queue length and vehicles on lane is considered. The intersection can share information with each other in this state representation and make a joint action for intersection coordination. Moreover, the traffic signal control algorithm based on ADP method performs quite well in different performance measures witnessed by simulations. By comparing with other control methods, experimental results present that the proposed algorithm could be a potential candidate in an application of traffic network control system.

Measurement-based flow characterization in centrally controlled networks

Abstract: In this work we outline a framework for measurement-based performance evaluation in SDN environments. The SDN paradigm, which is based on a strict separation of the network logic from the underlying physical substrate, necessitates a comprehensive global view of the network state. To augment the network representation, we propose mechanisms for extracting traffic characteristics from network observations which are used to derive performance metrics. Such metrics can be exploited by SDN applications to optimize the performance of SDN services. Given the bursty nature of network traffic and the well known adverse impact of this property on network performance, we propose an approach for extracting flow autocorrelations from switch counters. Our main contribution is a random sampling approach that reduces the monitoring overhead while enabling a fine grained characterization of the flow autocorrelation structure. We analytically evaluate the impact of random sampling and demonstrate how services may use the estimated traffic properties to compute useful performance metrics.

Stochastic macroscopic analysis and modelling for traffic management

Abstract: When congestion becomes a problem on a road or road network, there are generally three main solution areas available to tackle it: construction, pricing or traffic management. Traffic management became an increasingly preferred option towards the end of the twentieth century as an alternative to construction in many cases. Traffic management proves a more efficient alternative and focusses on influencing traffic flows such that the existing road and network capacity is more effectively utilised resulting in a reduction in congestion. The effectiveness of traffic management is dependent on the ability to influence traffic flow. However, traffic contains a relatively large amount of stochastic behaviour, which is connected to human driving behaviour. The fluctuations that occur in traffic flow due to this stochastic behaviour have a large effect on the effectiveness of traffic management. Furthermore, uncertainty between time dependant scenarios has also shown to have a large influence on the outcome of the analysis of traffic management measures. In the past, little attention has been paid to these effects. Therefore, the main objective of this thesis is to give insight into the stochastic fluctuations and uncertainty in traffic flow for the application of traffic management measures and to propose tools that allow these effects to be analysed and subsequently modelled in aggregated macroscopic flows. In doing this, the necessity to consider uncertainty and fluctuations for traffic management is also demonstrated. Stochastic processes are considered as uncertainty, which describes day-to-day uncertainties between traffic flows, and fluctuations, which describes microscopic variability in the traffic flow. Three main areas are focussed on: the analysis of variations in traffic, modelling fluctuations and uncertainty in traffic, and the visual communication of uncertainty from traffic models.

Prediction of traveller information and route choice based on real-time estimated traffic state

Abstract: Accurate depiction of existing traffic states is essential to devise effective real-time traffic management strategies using intelligent transportation systems. Existing applications of dynamic traffic assignment (DTA) methods are mainly based on either the prediction from macroscopic traffic flow models or measurements from the sensors and do not take advantage of the traffic state estimation techniques, which produce an estimate of the traffic states which has less uncertainty than the prediction or measurement alone. On the other hand, research studies which highlight the estimation of real-time traffic state are focused only on traffic state estimation and have not utilised the estimated traffic state for DTA applications. In this paper we propose a framework which utilises real-time traffic state estimate to optimise network performance during an incident through the traveller information system. The estimate of real-time traffic states is obtained by combining the prediction of traffic density using...

Insights into the effect of traffic congestion on distribution network characteristics – a numerical analysis based on navigation service data

Abstract: This research studies effects that the average increase in travel times due to road traffic congestion has on characteristics of an existing distribution network. It presents the most detailed estimate of ‘on-the-road’ effects on distribution network characteristics up to now, from the network modelling perspective and from the processed data point of view. A concrete network model allowing for the representation of all relevant transportation flows is presented. The processed traffic information relies on navigation service data. The use of such data allows the requirements that arise for the traffic analysis of a whole distribution network to be met. It is shown that this data source may considerably contribute in forthcoming research. The effects of traffic congestion are quantified to get insights into the extent to which regular traffic congestion affects distribution network characteristics and to understand the mitigating effect when the number of distribution centres is increased.

Applying back-propagation neural network to predict bus traffic

Abstract: In order to devise bus lines and make daily scheduling more precisely, this paper uses Back-Propagation(BP) neural network to predict bus traffic. As the time factor and meteorological factor are the two important factors which affect traffic, this paper does the data preprocessing for bus data in Guangzhou from August to December in 2014. BP neural network's information processing capacity is determined by the input and output neurons' characteristics, the network structure, the connection weights. The complexity of the neural network structure affects the performance of the neural network. The more complex the network structure is, the better fitted the model will be, meanwhile, it is easy to be over-fitting to the training data and its generalization performs inefficiently. If the network structure is too simple, it can't learn training data very well, and the network can't be converged well and the accuracy of data fitting can't be guaranteed. This paper conducts several experiments to determine the network structure and parameters which are suitable for prediction of bus traffic. Trough Ten-fold cross-validation experiments, the model is demonstrated being able to apply to bus traffic forecast.

Capacity Drop in First-Order Traffic Flow Models: Overview and Real-Data Validation

Abstract: First-order traffic flow models are known for their simplicity and computational efficiency and have, for this reason, been widely used for various traffic engineering tasks. However, first-order models are not able to reproduce significant traffic phenomena of great interest such as the capacity-drop and stop-and-go waves. This paper presents an overview of the, so far, proposed modelling approaches which aim to introduce the capacity-drop phenomenon into first-order traffic flow models. The background and main characteristics of each approach are analyzed with a particular emphasis on the practical applicability of such models for traffic management and control. The presented modelling approaches are calibrated and tested using real data from a motorway network in U.K.