A review of traffic simulation software

Computer simulation of tra c is a widely used method in research of tra c modelling, planning and development of tra c networks and systems. Vehicular tra c systems are of growing concern and interest globally and modelling arbitrarily complex tra c systems is a hard problem. In this article we review some of the tra c simulation software applications, their features and characteristics as well as the issues these applications face. Additionally, we introduce some algorithmic ideas, underpinning data structural approaches and quanti able metrics that can be applied to simulated model systems

Clarifying interoperability: The SISO CSPI PDG standard for commercial off-the-shelf simulation package interoperability reference models

Commercial-off-the-shelf simulation packages (CSPs), visual interactive modelling environments such as Arena, Anylogic, Flexsim, Simul8, Witness, etc., are important "black box" software tools that support the development, experimentation and visualization of simulation models. They are widely used in commerce, defence, health, manufacturing and logistics. There is a growing need to link together, or to interoperate, models developed in these CSPs across computer networks. The motivation for this includes data sensitivity, difficult to move resources and speed up

Modelling Self-similar Traffic Of Multiservice Networks

Simulation modelling is carried out, which allows adequate describing the traffic of multiservice networks with the commutation of packets with the characteristic of burstiness. One of the most effective methods for studying the traffic of telecommunications systems is computer simulation modelling. By using the theory of queuing systems (QS), computer simulation modelling of packet flows (traffic) in modern multi-service networks is performed as a random self-similar process. Distribution laws such as exponential, Poisson and normal-logarithmic distributions, Pareto and Weibull distributions have been considered.The distribution of time intervals between arrivals of packages and the service duration of service of packages at different system loads has been studied. The research results show that the distribution function of time intervals between packet arrivals and the service duration of packages is in good agreement with the Pareto and Weibull distributions, but in most cases the Pareto distribution prevails.The queuing systems with the queues M/Pa/1 and Pa/M/1 has been studied, and the fractality of the intervals of requests arriving have been compared by the properties of the estimates of the system load and the service duration. It has been found out that in the system Pa/M/1, with the parameter of the form a> 2, the fractality of the intervals of requests arriving does not affect the average waiting time and load factor. However, when ≤2, as in the M/Pa/1 system, both considered statistical estimates differ.The application of adequate mathematical models of traffic allows to correctly assess the characteristics of the quality of service (QoS) of the network

Applying Mean-field Approximation to Continuous Time Markov Chains

The mean-field analysis technique is used to perform analysis of a systems with a large number of components to determine the emergent deterministic behaviour and how this behaviour modifies when its parameters are perturbed. The computer science performance modelling and analysis community has found the mean-field method useful for modelling large-scale computer and communication networks. Applying mean-field analysis from the computer science perspective requires the following major steps: (1) describing how the agents populations evolve by means of a system of differential equations, (2) finding the emergent deterministic behaviour of the system by solving such differential equations, and (3) analysing properties of this behaviour either by relying on simulation or by using logics. Depending on the system under analysis, performing these steps may become challenging. Often, modifications of the general idea are needed. In this tutorial we consider illustrating examples to discuss how the mean-field method is used in different application areas. Starting from the application of the classical technique, moving to cases where additional steps have to be used, such as systems with local communication. Finally we illustrate the application of the simulation and uid model checking analysis techniques

neuronal simulation system of biological neural networks

Neuroscientists use computer simulations of neural systems in their efforts to understand processes that underlie neural function. As experimental data in- crease, it becomes clear that detailed physiological data alone are not enough to infer how neural circuits work. Experimentalists appear to be recogniz- ing the need for a quantitative approach to the exploration of the functional consequences of particular neural features, which is provided by modelling. The number of computer simulation programs is designed as a tool for de- velopment and simulation of realistic models of single neurons and neural networks. The present available packages for modelling of biological neural networks are often dedicated Unix-based simulation packages, which require rather large computational power from workstations, typically Unix systems. The widely distributed packages, as Genesis [8] and Neuron [4], have their own interpreted scripting language, in which users define components and run- ning parameters for their simulations. In the hands of experienced users with access to a compatible computer system, these modelling packages are powerful research tools. However, they do suffer several drawbacks for non- expert users: they don't provide a Graphical User Interface (GUI) or have a very simple one, and as a result of it they can't visually represent the simula- tion process. Also, the formal structure of the language is difficult and time consuming to learn; at least initial knowledge and skills about Unix system are necessary for users

How TRAF-NETSIM Works.

This paper describes how TRAF-NETSIM works in detail. It is a review of the TRAF-NETSIM micro-simulation model, for use in the research topic "The Development of Queueing Simulation Procedures for Traffic in Bangkok". TRAF-NETSIM is a computer program for modelling of traffic in urban networks. It is written in the FORTRAN 77 computer language. It uses bit-manipulation mechanisms for "packing" and "unpacking" data and a program overlay structure to reduce the computer memory requirements of the program. The model is based on a fixed time, and discrete event simulation approach. The periodic scan method is used in the model with a time interval of one second. In the model, up to 16 different vehicle types with 4 different vehicle categories (car, carpool, bus and truck) can be identified. Also, the driver's behaviour (passive, normal, aggressive), pedestrians' movement, parking and blocking (eg a broken-down car) can be simulated. Moreover, it has the capability to simulate the effects of traffic control ranging from a simple stop sign controlled junction to a dynamic/real time control system. The effects of spillbacks can be simulated in detail. The estimation of fuel consumption and vehicle emissions are optional simulations. Car following and lane changing models are incorporated into TRAF-NETSIM. The outputs can be shown in US standard units, Metric units, or both

Stabilization of grid frequency through dynamic demand control

Frequency stability in electricity networks is essential to the maintenance of supply quality and security. This paper investigates whether a degree of built-in frequency stability could be provided by incorporating dynamic demand control into certain consumer appliances. Such devices would monitor system frequency (a universally available indicator of supply-demand imbalance) and switch the appliance on or off accordingly, striking a compromise between the needs of the appliance and the grid. A simplified computer model of a power grid was created incorporating aggregate generator inertia, governor action and load-frequency dependence plus refrigerators with dynamic demand controllers. Simulation modelling studies were carried out to investigate the system's response to a sudden loss of generation, and to fluctuating wind power. The studies indicated a significant delay in frequency-fall and a reduced dependence on rapidly deployable backup generation

MODELLING SELF-SIMILAR TRAFFIC OF MULTISERVICE NETWORKS

Simulation modelling is carried out, which allows adequate describing the traffic of multiservice networks with the commutation of packets with the characteristic of burstiness. One of the most effective methods for studying the traffic of telecommunications systems is computer simulation modelling. By using the theory of queuing systems (QS), computer simulation modelling of packet flows (traffic) in modern multi-service networks is performed as a random self-similar process. Distribution laws such as exponential, Poisson and normal-logarithmic distributions, Pareto and Weibull distributions have been considered. The distribution of time intervals between arrivals of packages and the service duration of service of packages at different system loads has been studied. The research results show that the distribution function of time intervals between packet arrivals and the service duration of packages is in good agreement with the Pareto and Weibull distributions, but in most cases the Pareto distribution prevails. The queuing systems with the queues M/Pa/1 and Pa/M/1 has been studied, and the fractality of the intervals of requests arriving have been compared by the properties of the estimates of the system load and the service duration. It has been found out that in the system Pa/M/1, with the parameter of the form a> 2, the fractality of the intervals of requests arriving does not affect the average waiting time and load factor. However, when ????≤2, as in the M/Pa/1 system, both considered statistical estimates differ. The application of adequate mathematical models of traffic allows to correctly assess the characteristics of the quality of service (QoS) of the network