Showing papers on "Network traffic simulation published in 2018"

An Approach to Intelligent Traffic Management System Using a Multi-agent System

Abstract: Intelligent traffic management can be considered one of the most promising solutions to contemporary traffic problems The traffic in transportation associated with emergency conditions including psychiatric improvement in transport network, allocation of variable traffic flows, reducing the number of the crowded traffic roads and paths as well as its negative effects (such as delays, waiting time, stress of driver, noise and air pollution, and blocking the assistive devices) This research has been used by new multi-agent systems to manage traffic On the one hand, the proposed algorithm includes traffic flow improvement in emergency conditions until it is considered as real-time traffic information and in other hand, by preventing the increase the volume of a paths have effects in the reduce of crowded traffic situations at a specific time (for example, providing the proposed paths in the shortest time) In this article, the integrated environment is including JACK software for having a virtual agent behavior simulation In general, we can use the different simulation form in a distribution network to display the crowded and traffic In this article, the JACK software is used for having the explicit capabilities and supporting the common of this software in modelling the multi-agent systems, such as agents, design, event and capabilities In addition, designing and analyzing of this interaction is simplest than the existed designs in JACK software As a result of the proposed model, it seems reasonable that the proposed approach is different than previous works in each case On the one hand, there are modeling system in the different tasks as intelligent agents dependent to present the simple and effective road network In this case, it may correct and changes the actions of driver in emergency conditions by the concept of agent cooperation for achieving the common target

Prediction for network traffic of radial basis function neural network model based on improved particle swarm optimization algorithm

Abstract: The neural network data are usually characterized by abruptness, nonlinearity and time variability, and thus, it is difficult to yield accuracy results of network traffic prediction based on a traditional radial basis function neural network that has the shortcomings of slow convergence and easily falling to local optimum. To improve the accuracy of network traffic prediction and optimize the method of parameter and structure setting for a neural network, a model of radial basis function neural network with improved particle swarm optimization algorithm is proposed by referring to the related theories of network traffic and phase space reconstruction. The improved particle swarm optimization algorithm can adjust the inertia weight and the learning factors, and make $$t$$ -distribution mutation of particles’ positions via global extremum to avoid local convergence and thereby improving its global searching capacity; with such an algorithm, the parameters of radial basis function neural network are optimized; then, in order to verify the algorithm’s effectiveness, the radial basis function neural network is trained to become an optimal prediction model, which is adopted for the prediction of two typical chaotic time series and the real network traffic. It is then compared with the traditional radial basis function neural network model and the radial basis function prediction model by improved particle swarm optimization; and the simulations result shows that the application of this model improves the accuracy of network traffic prediction, and demonstrates the algorithm’s feasibility and effectiveness for network traffic prediction.

An Application of Internet Traffic Prediction with Deep Neural Network

Abstract: The advance knowledge of future traffic load is helpful for network service providers to optimize the network resource and to recover the demand criteria. This paper presents the task of internet traffic prediction with three different architectures of Deep Belief Network (DBN). The artificial neural network is created with the depth of 4 hidden layers in each model to learn the nonlinear hierarchal essence present in the time series of internet traffic data. The deep learning in the network is executed with unsupervised pretraining of the layers. The emphasis is given to the topology of DBN that achieves excellent prediction accuracy. The adopted approach provides accurate traffic predictions while simulating the traffic data patterns and stochastic elements, achieving 0.028 Root Mean Square Error (RMSE) value on the test data set. To validate our choice for hidden layer size selection, further more experiments were done for chaotic time series prediction.

Characterization of Substation Process Bus Network Delays

Abstract: This paper presents the characterization of network delays in an IEC61850 process bus substation area network, both through theoretical analysis and simulations. Several design targets were defined considering the recommendations of standards and good design practices: number of network hops, total network delay, probability of the delay being exceeded, link load, network topology and availability. An analytical delay estimation methodology is proposed, considering both the steady-state traffic and traffic resulting from a breaker failure event. A complete substation is taken as an example for characterizing the network delays, considering a star network topology. Simulations allow to obtain the cumulative distribution functions and percentile values of network delays. Results show a good agreement between the simulation and the analytical analysis. While the delay is best characterized statistically through simulation, finding the maximum network delay through simulations can be very time consuming, making the analytical analysis more suitable.

Dynamic route choice behaviour and simulation-based dynamic traffic assignment model for mixed traffic flows

Abstract: Intelligent Transportation Systems (ITS) focus on increasing the efficiency of existing surface transportation systems through the use of advanced computers, electronics, and communication technologies In order to perform advanced traffic management and provide travel information, dynamic traffic assignment models need to be developed to provide time-dependent estimates of traffic flows on networks in order to efficiently utilize possible advanced traffic information as well as traffic control measures Traffic assignment distributes Origin-Destination (OD) trips in a network and determines the flow patterns in a traffic network This research aims at developing simulation-based algorithm for dynamic traffic assignment problems under mixed traffic flow considerations Four different physical vehicle types are explicitly considered and modeled, including car, bus, motorcycle, and truck Four different behavioral rules, pre-specified-path driver, user-equilibrium driver, system-optimization driver, and real-time information driver, are considered in the solution procedure The DTA algorithm consists of an inner loop that incorporates a direction finding mechanism for the search process for System Optimization (SO) and User Equilibrium (UE) classes based on the simulation results of the current iteration, including experienced vehicular trip times and marginal trip times In order to understand tripmaker acceptance toward route guidance, a survey is conducted to explore possible behavioral classifications and associated percentages Numerical experiments are conducted in a test network and a real city network to illustrate the capabilities of the simulation-based DTA procedures, and to observe how system performs under multiple user class’s conditions, including multiple user behavior rules and multiple physical vehicle classes

A stochastic process traffic assignment model considering stochastic traffic demand

Abstract: In real traffic network, both link capacity and traffic demand are subject to stochastic fluctuations. These random fluctuations are major sources of travel time uncertainty. All existing stochasti...

A Distributed Semiasynchronous Algorithm for Network Traffic Engineering

Abstract: In this paper, we consider the traffic engineering problem in a large-scale hierarchical network arising in the next-generation cloud-based wireless networks. We propose a distributed semiasynchronous algorithm for this problem based on the so-called block successive upper bound minimization method of multipliers (BSUM-M). Theoretically, we show that the proposed algorithm converges to the global optimal solution under some assumptions on the degree of network asynchrony. We illustrate the effectiveness and efficiency of the proposed algorithm by comparing it with the state-of-the-art commercial solvers in a networked environment.

Pattern recognition based speed forecasting methodology for urban traffic network

Abstract: A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic. First Published Online: 4 Sept 2017

Two-stage network simulation

Abstract: In an example, there is disclosed a computing apparatus, having: one or more logic elements, including at least a processor and a memory, providing a network simulation engine to: periodically perform a network traffic simulation; cache at least one network traffic simulation in a traffic state cache; receive a quest for additional network demand; and compute a network delta based at least in part on a difference between the request for additional network demand and the traffic state cache.

Traffic simulation method and apparatus

Abstract: Provided are a traffic simulation method and apparatus, relating to the technical field of communications, and being able to simplify the network traffic simulation process. The specific solution involves: a simulation apparatus acquiring a target traffic model corresponding to a first target port; calculating, according to the target traffic model and a pre-set maximum downlink traffic value of a target base station, a downlink traffic value to be superimposed of the target base station at each pre-set time point; acquiring a reference downlink traffic value of a second port at each pre-set time point and the maximum support bandwidth of the second port; superimposing, according to a correlation between pre-set time points, the downlink traffic value to be superimposed of the target base station at each pre-set time point and the reference downlink traffic value of the second port at each pre-set time point, so as to obtain a total downlink traffic value of the second port at each pre-set time point; and determining, according to the total downlink traffic value of the second port at each pre-set time point and the maximum support bandwidth of the second port, whether the second port meets a bandwidth requirement. The embodiments of the present application are used for network traffic simulation.