Showing papers on "Network traffic simulation published in 2012"

Exploiting the fundamental diagram of urban networks for feedback-based gating

Abstract: Traffic signal control for urban road networks has been an area of intensive research efforts for several decades, and various algorithms and tools have been developed and implemented to increase the network traffic flow efficiency. Despite the continuous advances in the field of traffic control under saturated conditions, novel and promising developments of simple concepts in this area remains a significant objective, because some proposed approaches that are based on various meta-heuristic optimization algorithms can hardly be used in a real-time environment. To address this problem, the recently developed notion of network fundamental diagram for urban networks is exploited to improve mobility in saturated traffic conditions via application of gating measures, based on an appropriate simple feedback control structure. As a case study, the proposed methodology is applied to the urban network of Chania, Greece, using microscopic simulation. The results show that the total delay in the network decreases significantly and the mean speed increases accordingly.

A first look at cellular machine-to-machine traffic: large scale measurement and characterization

Abstract: Cellular network based Machine-to-Machine (M2M) communication is fast becoming a market-changing force for a wide spectrum of businesses and applications such as telematics, smart metering, point-of-sale terminals, and home security and automation systems. In this paper, we aim to answer the following important question: Does traffic generated by M2M devices impose new requirements and challenges for cellular network design and management? To answer this question, we take a first look at the characteristics of M2M traffic and compare it with traditional smartphone traffic. We have conducted our measurement analysis using a week-long traffic trace collected from a tier-1 cellular network in the United States. We characterize M2M traffic from a wide range of perspectives, including temporal dynamics, device mobility, application usage, and network performance.Our experimental results show that M2M traffic exhibits significantly different patterns than smartphone traffic in multiple aspects. For instance, M2M devices have a much larger ratio of uplink to downlink traffic volume, their traffic typically exhibits different diurnal patterns, they are more likely to generate synchronized traffic resulting in bursty aggregate traffic volumes, and are less mobile compared to smartphones. On the other hand, we also find that M2M devices are generally competing with smartphones for network resources in co-located geographical regions. These and other findings suggest that better protocol design, more careful spectrum allocation, and modified pricing schemes may be needed to accommodate the rise of M2M devices.

Efficient network-wide model-based predictive control for urban traffic networks

Abstract: Traffic congestion has become a stringent issue in urban areas. Traffic control systems are designed to make a better use of the existing traffic infrastructures in order to improve traffic conditions. Along with the fast development of the transportation infrastructures, traffic networks become larger and more complex. Therefore, network-wide traffic control systems that can coordinate the whole network and improve the utilization of the entire traffic infrastructure, are highly required. In this paper, a structured network-wide traffic controller is presented based on Model Predictive Control (MPC) theory. Two macroscopic models are proposed to be the prediction model of the MPC controller. One is more accurate, but correspondingly requires more computation time; the other sacrifices a certain amount of accuracy, but is computationally much more efficient. Based on these two models, MPC controllers are developed. Simulation results show that the MPC controllers are capable of coordinating an urban traffic network, especially in the situations that the traffic flow is not spread evenly through the network. Through reducing the prediction model, the corresponding MPC controller exhibits less on-line computational burden, and thus becomes more applicable in practice. Therefore, it becomes possible for the control system to deal with complex urban road networks more efficiently.

A stochastic model of traffic flow: Theoretical foundations

Abstract: In a variety of applications of traffic flow, including traffic simulation, real-time estimation and prediction, one requires a probabilistic model of traffic flow. The usual approach to constructing such models involves the addition of random noise terms to deterministic equations, which could lead to negative traffic densities and mean dynamics that are inconsistent with the original deterministic dynamics. This paper offers a new stochastic model of traffic flow that addresses these issues. The source of randomness in the proposed model is the uncertainty inherent in driver gap choice, which is represented by random state dependent vehicle time headways. A wide range of time headway distributions is allowed. From the random time headways, counting processes are defined, which represent cumulative flows across cell boundaries in a discrete space and continuous time conservation framework. We show that our construction implicitly ensures non-negativity of traffic densities and that the fluid limit of the stochastic model is consistent with cell transmission model (CTM) based deterministic dynamics.

Cellular automata simulation of traffic including cars and bicycles

Abstract: As ‘greening’ of all aspects of human activity becomes mainstream, transportation science is also increasingly focused around sustainability. Modal co-existence between motorised and non-motorised traffic on urban networks is, in this context, of particular interest for traffic flow modelling. The main modelling problems here are posed by the heterogeneity of vehicles, including size and dynamics, and by the complex interactions at intersections. Herein we address these with a novel technique, based on one-dimensional cellular automata components, for modelling network infrastructure and its occupancy by vehicles. We use this modelling approach, together with a corresponding vehicle behaviour model, to simulate combined car and bicycle traffic for two elemental scenarios—examples of components that would be used in the building of an arbitrary network. Results of simulations performed on these scenarios, (i) a stretch of road and (ii) an intersection causing conflict between cars and bicycles sharing a lane, are presented and analysed.

Understanding the characteristics of cellular data traffic

Abstract: Because of rapidly growing subscriber populations, advances in cellular communication technology, increasingly capable user terminals, and the expanding range of mobile applications, cellular networks have experienced a significant increase in data traffic, the dominant part of which is carried by the http protocol. Understanding the characteristics of this traffic is important for network design, traffic modeling, resource planning and network control. In this study we present a comprehensive characterization study of mobile http-based traffic using packet level traces collected in a large cellular network. We analyze the traffic using metrics at packet level, flow level and session level. For each metric, we conduct a comparison between traffic from different applications, as well as comparison to traffic in a wired network. Finally, we discuss the implications of our findings for better resource utilization in cellular infrastructures.

Mobile network reporting and usage analytics system and method aggregated using a distributed traffic optimization system

Abstract: Mobile network reporting and usage analytics system and method are disclosed. One embodiment includes a method generating a report for a network operator, which may be implemented on a system, including tracking optimization efficiency for traffic in a wireless network, generating the report to be provided to the network operator based on the optimization efficiency and performing functions related to traffic optimization and management in the wireless network effectuating in traffic alleviation in the wireless network measured by the optimization frequency. The optimization efficiency can include efficiency information associated with different mobile applications and user-related information in a wireless network.

Cloud computing enhanced gateway for communication networks

Abstract: A network traffic managing node of a local area network, such as a router or gateway, can monitor network traffic of the local area network. A network event associated with the local area network is detected using the network traffic managing node. The network event is reported from the network traffic managing node to one or more servers of a cloud-based computing network. A network policy update for the network traffic managing node is received from the cloud-based computing network. The network policy update is based, at least in part, on a type of network event reported to the cloud-based computing network. The network policy update is implemented at the network traffic managing node to process and/or resolve the network event.

Systems and methods involving features of adaptive and/or autonomous traffic control

Abstract: Systems and method are disclosed for adaptive and/or autonomous traffic control. In one illustrative implementation, there is provided a method for processing traffic information. Moreover, the method may include receiving data regarding travel of vehicles associated with an intersection, using neural network technology to recognize types and/or states of traffic, and using the neural network technology to process/determine/memorize optimal traffic flow decisions as a function of experience information. Exemplary implementations may also include using the neural network technology to achieve efficient traffic flow via recognition of the optimal traffic flow decisions.

Energy efficiency in heterogeneous networks

Abstract: An attractive approach to meet increasing traffic demands is to densify existing cellular networks with low power nodes. This creates a heterogeneous network. In this paper we analyse the impact of such a densification on the network energy consumption and the possibilities it offers to enhance the network energy efficiency. In a heterogeneous network, the user performance can be significantly improved. This performance increase leads to a shorter transmission time of most data packets, creating longer idle time in the network nodes. In this work, we introduce two node sleep modes operating on a fast and intermediate time scale respectively, in order to exploit short and longer idle periods of the nodes. Our results point out that the total energy consumption of the heterogeneous network featuring the node sleep modes can be maintained at a similar level as the one of the reference network, while the user performance remains superior. At very high traffic load we show that the heterogeneous network can even be more energy-efficient. This means that it is actually possible to increase end-user performance and decrease energy consumption at the same time.

Traffic Flow Control using Neural Network

Abstract: With rapid increase in motorization, urbanization, population growth, and changes in population density, Traffic Congestion problems have increased worldwide. Traffic Congestion causes increase in traveling time, air pollution and increase in fuel usage is also observed. Intelligent Transportation Systems (ITS) are used to avoid these problems and improve efficiency, safety and service. Traffic Flow Forecasting is an important part of ITS [1][2]. Traffic Flow Forecasting (TFF) is for Controlling Traffic and Intelligent Traffic Guidance. TFF is the study of interactions between vehicles, drivers, and infrastructure (which includes highways and traffic control devices), with the aim of understanding and developing an optimal road network with efficient movement of traffic and minimal traffic congestion problems. General Terms Intelligent Transportation System, Traffic Flow Forecasting, Neural Network.

Network Traffic Modeling

Abstract: From the viewpoint of a service provider, demands on the network are not entirely predictable. Traffic modeling is the problem of representing our understanding of dynamic demands by stochastic processes. Accurate traffic models are necessary for service providers to properly maintain quality of service. Many traffic models have been developed based on traffic measurement data. This chapter gives an overview of a number of common continuous-time and discrete-time traffic models. Sources are sometimes policed or regulated at the network access, usually by a leaky-bucket algorithm. Access policing can change the shape of source traffic by limiting the peak rate or burstiness. Source traffic may also be regulated by protocol mechanisms such as sliding windows or congestion windows (as in TCP), leading to other traffic models. INTRODUCTION Teletraffic theory is the application of mathematics to the measurement, modeling, and control of traffic in telecommunications networks (Willinger and Paxson, 1998). The aim of traffic modeling is to find stochastic processes to represent the behavior of traffic. Working at the Copenhagen Telephone Company in the 1910s, A. K. Erlang famously characterized telephone traffic at the call level by certain probability distributions for arrivals of new calls and their holding times. Erlang applied the traffic models to estimate the telephone switch capacity needed

A dynamic network partition method for heterogenous urban traffic networks

Abstract: Recently, it has been shown that Macroscopic Fundamental Diagrams(MFDs) existing in large scale urban traffic networks paly an important role in dynamic traffic management, traffic signal control and mitigation of urban traffic congestion. A well defined MFD can be derived from a homogeneous urban traffic network with similar traffic conditions. In reality, however, most large scale traffic networks are usually heterogeneous networks with various road types and uneven distribution of congestion. In order to use the MFD concept for controlling 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 homogeneous sub-networks and fast to compute. In this paper, a new approach to calculate the correlation degree, which describes the traffic conditions between two adjacent intersections quantitatively, is first proposed. 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 homogeneous sub-networks for large scale urban traffic networks. Finally, an application to a specified urban traffic network is investigated by using the proposed algorithm. The results show that it is an effective and efficient method for partitioning heterogeneous urban traffic networks automatically.

Providing reports to mobile network operators based on optimisiciency of wireless network traffic and/or battery consumption reduction

Abstract: Mobile network reporting and usage analytics system and method includes generating a report for a mobile network operator, which may be implemented on a system, including tracking optimisation efficiency for traffic in a wireless network, generating the report to be provided to the network operator based on the optimization efficiency and performing functions related to traffic optimisation and management in the wireless network effectuating in traffic alleviation in the wireless network measured by the optimisation frequency. The optimisation efficiency can include efficiency information associated with different mobile applications and user-related information in a wireless network. A client side proxy on the mobile device tracks optimization efficiency for traffic, and also determines battery consumption data and performs functions related to battery consumption reduction. A server side proxy determines optimization efficiency for wireless network traffic and tracks user-related information, for generating reports to network operators.

A link queue model of network traffic flow

Abstract: Fundamental to many transportation network studies, traffic flow models can be used to describe traffic dynamics determined by drivers' car-following, lane-changing, merging, and diverging behaviors. In this study, we develop a deterministic queueing model of network traffic flow, in which traffic on each link is considered as a queue. In the link queue model, the demand and supply of a queue are defined based on the link's fundamental diagram, and its in- and out-fluxes are computed from junction flux functions corresponding to macroscopic merging and diverging rules. We demonstrate that the model is well defined and can be considered as a continuous approximation to the kinematic wave model on a road network. From careful analytical and numerical studies, we conclude that the model is physically meaningful, computationally efficient, always stable, and mathematically tractable for network traffic flow. As an addition to the multiscale modeling framework of network traffic flow, the model strikes a balance between mathematical tractability and physical realism and can be used for analyzing traffic dynamics, developing traffic operation strategies, and studying drivers' route choice and other behaviors in large-scale road networks.

Methods for Division of Road Traffic Networks Focused on Load-Balancing

Abstract: The road traffic simulat ion is an important tool for analysis and control of road traffic networks. In order to be able to simu late very large traffic networks in a reasonable time, it is possible to use a d istributed computing environ ment. There, the co mbined power of mu ltiple interconnected computers is utilized. During the adaptation of the simulat ion for the distributed environment, it is necessary to divide the traffic network into sub-networks, which are then simulated on par- ticular nodes of the distributed computer. The load-balancing of the sub-networks and the communication between them are two key issues. In this paper, we compare the methods for traffic network division, wh ich we developed. The methods are focused on load-balancing and consist of two steps - assigning of weights to the traffic lanes and the marking of traffic lanes, which shall be div ided. For the first step, traffic simulat ions of different level of detail are utilized. For the second step, a modified breadth-first search algorith m or a genetic algorithm are utilized. The methods were thoroughly tested for their performance. Their description and the results of the tests are the main contributions of this paper.

Methods and systems for network traffic forecast and analysis

Abstract: A computer-implemented method and system are provided for forecasting traffic load on a communications network driven by market factors.

Urban traffic flow prediction based on road network model

Abstract: This paper addresses an issue of short-term traffic flow prediction in urban traffic networks with traffic signals in intersections An effective spatial prediction approach is proposed based on a macroscopic urban traffic network model In contrast with other time series based or spatio-temporal correlation methods, this research focuses on the substantial mechanism of vehicles transmission on road segments and the spatial model of the entire urban network Furthermore, this approach employs a simple speed-density model based on the macroscopic fundamental diagram (MFD) to obtain a more accurate vehicle travel time on the link Finally, the microscopic traffic simulation software, CORSIM, is adopted to simulate the real urban traffic, and the proposed method is used to predict the traffic flows generated by CORSIM The simulation results illustrate that our approach performs effective prediction timely in the rush hours, as well as the suddenly changed traffic states

Method and system for selectively metering network traffic

Abstract: An approach for resizing a network tunnel based on criteria associated with incoming traffic to the network is described. A tunnel management platform determines a frequency or a relevancy of network traffic matching predetermined class of service criteria. The tunnel management platform also calculates, based on the frequency or relevancy, a minimal amount of bandwidth to reserve for tunneling subsequent network traffic associated with the predetermined class of service criteria over a network of the service provider. A resizing of the network tunnel is then initiated based on the calculation in association with subsequent network traffic.

Microscopic traffic simulation tool for Intelligent Transportation Systems

Abstract: This paper describes a microscopic traffic simulation tool for assessing the performance of ITS traffic and vehicle control systems. The scope of simulation is very broad. The traffic network is simulated in microscopic scale with nanoscopic components being available as well. The vehicles can be simulated down to basic physical properties including throttle and brake settings, fuel consumption and others. This allows for in-depth behavioural analysis of both individual vehicles and the whole traffic flow as a result of use of different traffic management systems. The simulator also allows investigating inter-vehicle interactions including platooning behaviour and its consequences such as the string stability problem. The simulator has been designed to be modular and easily extensible to allow for new functionality in future.

A Class of Non-statistical Traffic Anomaly Detection in Complex Network Systems

Abstract: Recently Network traffic anomaly detection has become a popular research tendency, as it can detect new attack types in real time. The real-time network traffic anomaly detection is still an unsolved problem of network security. The network traffic appears as a complex dynamic system, precipitated by many network factors. Although various schemes have been proposed to detect anomalies, they are mostly based on traditional statistical physics. In these methods, all factors are integrated to analyze the variation of the network traffic. But in fact, the changing trend of network traffic at some moment is only determined by a few primary factors. In this paper, we present a non-statistical network traffic anomaly detection method based on the synergetic neural networks. For our method, a synergetic dynamic equation based on the order parameters is used to describe the complex behavior of the network traffic system. When the synergetic dynamic equation is evolved, only the order parameter determined by the primary factors can converge to 1. Therefore, the network traffic anomaly can be detected by referring to the primary factors. We evaluate our approach using the intrusion evaluation data set of the network traffic provided by the defense advanced research projects agency (DARPA). Experiment results show that our approach can effectively detect the network anomaly and achieve high detection probability and low false alarms rate.

Modeling telecommunications traffic using stochastic multifractal cascade process

Abstract: In the work the simulation of telecommunications traffic has been examined, which has multifractal properties, based on a mathematical model of the stochastic multiplicative cascade, the weights of which are beta probability distribution.

Multilevel Monitoring System Architecture

Abstract: Network traffic information from multiple sources, at multiple time scales, and at multiple levels of detail are integrated so that users may more easily identify relevant network information. The network monitoring system stores and manipulates low-level and higher-level network traffic data separately to enable efficient data collection and storage. Packet traffic data is collected, stored, and analyzed at multiple locations. The network monitoring locations communicate summary and aggregate data to central modules, which combine this data to provide an end-to-end description of network traffic at coarser time scales. The network monitoring system enables users to zoom in on high-level, coarse time scale network performance data to one or more lower levels of network performance data at finer time scales. When high-level network performance data of interest is selected, corresponding low-level network performance data is retrieved from the appropriate distributed network monitoring locations to provide additional detailed information.

Packet traffic features of IPv6 and IPv4 protocol traffic

Abstract: Nowadays, the IPv6 protocol is in a transition phase in operational networks. The ratio of its traffic volume is increasing day by day. The many provided facilities for IPv6 connection increasethe total IPv6 traffic load. IPv6-over-IPv4 tunnels, pilot programsto provide IPv6 connections, IPv6/IPv4 dual stack operating systems,and free IPv6 tunnel brokers cause the IPv6 protocol to expand quickly. For efficient resource utilization, the characteristics of network traffic should be determined accurately. Many traffic characterization studies regarding IPv4 have demonstrated that most of the network traffic is self-similar. Self-similarity causes significant impacts on network performance. With the increasing volume of IPv6 traffic, the characteristics of IPv6 traffic and differences between IPv4 traffic in terms of characterization should be explicitly revealed. In this study, we investigate the characteristics of IPv6 packet traffic and the differences between IPv6 and IPv4 packet traffic in terms of spectral density, autocorrelation, distribution, and self-similarity of packet interarrival time and packet size. The results obviously show that IPv6 traffic exhibits totally different properties in comparison to that of IPv4. Distribution fittings prove that packet interarrival time and packet size have different distributions in the 2 traffic types. While beta distribution models the empirical cumulative distribution of IPv4 packet size, log-logistic distribution gives more efficient results for IPv6 packet size. Furthermore, a significant difference is observed in self-similarity degrees. IPv6 protocol traffic gives greater self-similarity than that of IPv4. Results show that IPv6 traffic would cause greater performance degradations in computer networks in comparison to IPv4 due to high self-similarity.

Systems and Methods of Network Analysis and Characterization

Abstract: Systems and methods for characterizing networks are disclosed. In several embodiments, a network analyzer applies a network analysis to a network that replaces components of the network in a model of the network with equivalent or bounding models. The network analyzer can then characterize the simplified model of the network and an assessment can be made concerning the accuracy of the characterization of the network.