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Microsimulation

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Econometric Microsimulation

Microsimulation (from microanalytic simulation) is a research area in applied econometrics. It tries to simulate the behaviour of individuals over time. Microsimulation can either be dynamic or static. If it is dynamic the behaviour of people changes over time, whereas in the static case a constant behaviour is assumed. There are several microsimulation models for taxation, pension etc. run by governmental bodies or academics. One example is Pensim2 which dynamically simulates pension income for the next 50 years in the UK. Euromod is a static microsimulation model for 15 EU states. North American microsimulation models include the longitudinal, dynamic microsimulation CORSIM, and daughter models DYNACAN (Canada, terminated June 1, 2009) and POLISIM (United States). A related example that provides spatially-detailed microsmulation of urban development is UrbanSim.

According to the International Microsimulation Association[1], the microsimulation is a modelling technique that operates at the level of individual units such as persons, households, vehicles or firms. Within the model each unit is represented by a record containing a unique identifier and a set of associated attributes – e.g. a list of persons with known age, sex, marital and employment status; or a list of vehicles with known origins, destinations and operational characteristics. A set of rules (transition probabilities) are then applied to these units leading to simulated changes in state and behaviour. These rules may be deterministic (probability = 1), such as changes in tax liability resulting from changes in tax regulations, or stochastic (probability <=1), such as chance of dying, marrying, giving birth or moving within a given time period. In either case the result is an estimate of the outcomes of applying these rules, possibly over many time steps, including both total overall aggregate change and, crucially, the distributional nature of any change.

Traffic Microsimulation

view of a typical microsimulation 2D animation. Shown, a roundabout in a country where traffic drives on the left.

Microsimulation is also a term used in traffic modelling and is typified by software packages such as VISSIM, TSIS-CORSIM, Cube Dynasim, LISA+, Quadstone Paramics, SiAS Paramics, Simtraffic and Aimsun. Empirical modelling software such as LINSIG, TRANSYT, TRANSYT-7F or aaSIDRA represent a different class of models based on deterministic methods.

Traffic microsimulation models simulate the behaviour of individual vehicles within a predefined road network and are used to predict the likely impact of changes in traffic patterns resulting from changes to traffic flow or from changes to the physical environment.

Microsimulation has its greatest strength in modelling congested road networks due to its ability to simulate queueing conditions. Microsimulation models will continue to provide results at high degrees of saturation, up to the point of absolute gridlock. This capability makes these type of models very useful to analyse traffic operations in urban areas and city centers, including interchanges, roundabouts, unsignalized and signalized intersections, signal coordinated corridors, and area networks. Microsimulation also reflects even relatively small changes in the physical environment such as the narrowing of lanes or the relocation of junction stop lines.

In recent years, microsimulation modelling has gained attention in its ability to visually represent predicted traffic behaviour through 3D animation, enabling laypeople such as politicians and the general public to fully appreciate the impacts of a proposed scheme. Further advances are being made in this area with the merging of microsimulation model data with cinematic quality 3D animation and with virtual reality by such companies as FORUM8 in Japan.

Pedestrian or Crowd Microsimulation

Pedestrian or Agent based microsimulation has grown in use and acceptance within industry in recent years; these systems focus on the simulation of individual people moving through an area of space with respect to analytics measures such as Space Utilisation, Level of Service, Density, Packing and Frustration.

Many current Traffic microsimulation software’s are combining traffic components and pedestrians to create a more complete systems while many transitional Crowd Simulation tools continue to be refined for use in large scale urban space design.

Microsimulation in Health Sciences

In health sciences Microsimulation refers to a type of simulation modeling that generates individual life histories. The technique is used when 'stock-and-flow' type modeling of proportions (macrosimulation) of the population cannot sufficiently describe the system of interest. This type of modeling does not necessarily involve interaction between individuals (as described above) and in that case can generate individuals independently of each other, and can easily work with continuous time instead of discrete time steps. Several examples of microsimulation models in health sciences have been brought together in the U.S. National Cancer Institute's CISNET program (http://cisnet.cancer.gov/).

Types of Microsimulation

Closed, longitudinal, dynamic microsimulation models (such as DYNACAN and Pensim2) begin with an initial population that is only modified by the simulated life events of the demographics modules, such as fertility, mortality and migration. Thus, at any time during the model run, the simulated population can be expected to remain a fully representative (synthetic) sample of the population that it is modelling.

Open models, on the other hand, tend to focus on specific "key" individuals and generate their representativeness based on the population of said individuals. In such an environment, new individuals are added or removed from the population as needed in order to ensure an "appropriate" set of life events for the key individuals.

One of the clearest examples of this distinction is the treatment of marriage within the two types of models. While open models can simply generate an appropriate spouse for the "key" individual, closed models must, instead, determine which people within its population are likely to marry, and then to match them.

References

See also