SimulationA simulation is the imitation of the operation of a real-world process or system over time. Simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time. Often, computers are used to execute the simulation. Simulation is used in many contexts, such as simulation of technology for performance tuning or optimizing, safety engineering, testing, training, education, and video games.
Computer simulationComputer simulation is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of, or the outcome of, a real-world or physical system. The reliability of some mathematical models can be determined by comparing their results to the real-world outcomes they aim to predict. Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics (computational physics), astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering.
Business simulation gameBusiness simulation games, also known as economic simulation games or tycoon games, are games that focus on the management of economic processes, usually in the form of a business. Pure business simulations have been described as construction and management simulations without a construction element, and can thus be called simulations. Indeed, micromanagement is often emphasized in these kinds of games. They are essentially numeric, but try to hold the player's attention by using creative graphics.
Vehicle simulation gameVehicle simulation games are a genre of video games which attempt to provide the player with a realistic interpretation of operating various kinds of vehicles. This includes automobiles, aircraft, watercraft, spacecraft, military vehicles, and a variety of other vehicles. The main challenge is to master driving and steering the vehicle from the perspective of the pilot or driver, with most games adding another challenge such as racing or fighting rival vehicles.
Road space rationingRoad space rationing, also known as alternate-day travel, driving restriction and no-drive days (restricción vehicular; rodízio veicular; circulation alternée), is a travel demand management strategy aimed to reduce the negative externalities generated by urban air pollution or peak urban travel demand in excess of available supply or road capacity, through artificially restricting demand (vehicle travel) by rationing the scarce common good road capacity, especially during the peak periods or during peak po
Probabilistic classificationIn machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to. Probabilistic classifiers provide classification that can be useful in its own right or when combining classifiers into ensembles. Formally, an "ordinary" classifier is some rule, or function, that assigns to a sample x a class label ŷ: The samples come from some set X (e.
Platt scalingIn machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes. The method was invented by John Platt in the context of support vector machines, replacing an earlier method by Vapnik, but can be applied to other classification models. Platt scaling works by fitting a logistic regression model to a classifier's scores. Consider the problem of binary classification: for inputs x, we want to determine whether they belong to one of two classes, arbitrarily labeled +1 and −1.
Transportation demand managementTransportation demand management, traffic demand management or travel demand management (TDM) is the application of strategies and policies to reduce travel demand, or to redistribute this demand in space or in time. In transport, as in any network, managing demand can be a cost-effective alternative to increasing capacity. A demand management approach to transport also has the potential to deliver better environmental outcomes, improved public health, stronger communities, and more prosperous cities.
Agent-based modelAn agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models.
