Microsimulation

Last updated

Microsimulation is the use of computerized analytical tools to perform analysis of activities such as highway traffic flowing through an intersection, financial transactions, or pathogens spreading disease through a population on the granularity level of individuals. Synonyms include microanalytic simulation [1] and microscopic simulation. [2] Microsimulation, with its emphasis on stochastic or rule-based structures, should not be confused with the similar complementary technique of multi-agent simulation, which focuses more on the behaviour of individuals. [3]

Contents

For example, a traffic microsimulation model could be used to evaluate the effectiveness of lengthening a turn lane at an intersection, and thus help decide whether it is worth spending money on actually lengthening the lane.

Introduction

Microsimulation can be distinguished from other types of computer modeling in looking at the interaction of individual units such as people or vehicles. Each unit is treated as an autonomous entity and the interaction of the units is allowed vary depending on stochastic (randomized) parameters. These parameters are intended to represent individual preferences and tendencies. For example, in a traffic model some drivers are cautious and wait for a large gap before turning, while others are aggressive and accept small gaps. Similarly, in a public health model individuals could vary in their resistance to a virus, as well as in personal habits that contribute to the spread of the virus (e.g. how frequently/thoroughly they wash their hands).

The International Microsimulation Association, [4] defines microsimulation as 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 (importantly) the way this change is distributed in the population or location that is being modeled.

Econometric microsimulation

In applied econometrics research, microsimulation is used to simulate the behavior of individuals over time. The microsimulation can either be dynamic or static. If it is dynamic the behavior of people changes over time, whereas in the static case a constant behavior is assumed.

There are several microsimulation models for taxation, pensions, and other types of economic and financial activity. These models are typically implemented by government agencies or academics. One example is Pensim2 (a dynamic microsimulation pension model) which dynamically simulates pension income for the next 50 years in the United Kingdom. EUROMOD is a static microsimulation model for 27 European Union states, while SOUTHMOD adopts the same framework for several countries in the Global South. North American microsimulation models include the longitudinal, dynamic microsimulation CORSIM, and daughter models DYNACAN (Canada, terminated June 1, 2009) and POLISIM (United States). The U.S. Department of Health and Human Services uses the static microsimulation Transfer Income Model (TRIM) to understand the potential impacts of changes to tax, transfer, and health programs. [5] A related example that provides spatially-detailed microsmulation of urban development is PECAS.

Econometric microsimulation models can be classified into two types:

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.

Traffic microsimulation

view of a typical microsimulation 2D animation. Shown, a roundabout in a country where traffic drives on the left. UK Roundabout 8 Cars.gif
view of a typical microsimulation 2D animation. Shown, a roundabout in a country where traffic drives on the left.

Microsimulation is also used in traffic modelling and is typified by software packages such as TransModeler, PTV VISSIM, TSIS-CORSIM, Cube Dynasim, LISA+, Quadstone Paramics, SiAS Paramics, Simtraffic, Aimsun, and MATSim. Analytical modelling software such as LINSIG, TRANSYT, TRANSYT-7F or SIDRA INTERSECTION represent a different class of models based on mathematical algorithms representing combinations of traffic model elements.

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. [6] 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 packages 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 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/). In Canada, the Population Health Model (POHEM) is a common platform that examines multiple chronic diseases, including diabetes, cardiovascular disease and arthritis. [7]

Spatial microsimulation

Economic and health approaches to microsimulation provide insight into the impacts of changes in environmental, economic, or policy conditions on a given population of individuals. However, the impacts of many changes are context dependent, meaning that the same alteration (e.g. in income tax bands) may have desirable effects in some regions, but undesirable effects in others. This understanding lies at the root of spatial approaches to microsimulation. The term spatial microsimulation refers to a set of techniques that allow the characteristics of individuals living in a particular area to be approximated, based on a set of constraint variables that are known about the area. As with econometric microsimulation, spatial microsimulation can be either dynamic or static, and can include interacting or passive units. [8]

Guy Orcutt is widely cited as the originator of spatial microsimulation. Spatial microsimulation has high computational and data requirements and some degree of computer programming is a prerequisite to setting up models. For these reasons, the technique is not widely used. However, a number of factors have led to rapid growth in the number of publications on spatial microsimulation within academic geography and related disciplines. These include:

Programming languages and platforms

There are general purpose programming languages, in addition to topic-specific programs (see Traffic Simulation). Examples include JAS-mine, [9] LIAM2, [10] MODGEN, [11] and OpenM++. [12]

See also

Further reading

Related Research Articles

<span class="mw-page-title-main">Simulation</span> Imitation of the operation of a real-world process or system over time

A simulation is an imitative representation of a process or system that could exist in the real world. In this broad sense, simulation can often be used interchangeably with model. Sometimes a clear distinction between the two terms is made, in which 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. Another way to distinguish between the terms is to define simulation as experimentation with the help of a model. This definition includes time-independent simulations. Often, computers are used to execute the simulation.

<span class="mw-page-title-main">Computer simulation</span> Process of mathematical modelling, performed on a computer

Computer 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, astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.

<span class="mw-page-title-main">Crowd simulation</span> Model of movement

Crowd simulation is the process of simulating the movement of a large number of entities or characters. It is commonly used to create virtual scenes for visual media like films and video games, and is also used in crisis training, architecture and urban planning, and evacuation simulation.

Network traffic simulation is a process used in telecommunications engineering to measure the efficiency of a communications network.

Pensim2 is a dynamic microsimulation model to simulate the income of pensioners, owned by the British Department for Work and Pensions.

<span class="mw-page-title-main">Spatial analysis</span> Formal techniques which study entities using their topological, geometric, or geographic properties

Spatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is geospatial analysis, the technique applied to structures at the human scale, most notably in the analysis of geographic data. It may also be applied to genomics, as in transcriptomics data.

A tax-benefit model is a form of microsimulation model. It is usually based on a representative or administrative data set and certain policy rules. These models are used to cost certain policy reforms and to determine the winners and losers of reform. One example is EUROMOD, which models taxes and benefits for 27 EU states, and its post-Brexit offshoot, UKMOD.

Dynamic simulation is the use of a computer program to model the time-varying behavior of a dynamical system. The systems are typically described by ordinary differential equations or partial differential equations. A simulation run solves the state-equation system to find the behavior of the state variables over a specified period of time. The equation is solved through numerical integration methods to produce the transient behavior of the state variables. Simulation of dynamic systems predicts the values of model-system state variables, as they are determined by the past state values. This relationship is found by creating a model of the system.

TransModeler is the name of a based traffic simulation platform for doing wide-area traffic planning, traffic management, and emergency evacuation studies that is developed by Caliper Corporation. It can animate the behavior of multi-modal traffic systems to show the flow of vehicles, the operation of traffic signals, and the overall performance of the transportation network.

<span class="mw-page-title-main">PTV VISSIM</span>

PTV Vissim is a microscopic multi-modal traffic flow simulation software package developed by PTV Planung Transport Verkehr AG in Karlsruhe, Germany. The name is derived from "Verkehr In Städten - SIMulationsmodell". PTV Vissim was first developed in 1992 and is today a global market leader.

The Policy Simulation Model (PSM) is a static microsimulation model which encapsulates the tax and benefits system, and population, of Great Britain. It is based on survey data from the Family Resources Survey (FRS) which is uprated to simulate the current year, together with several years into the future through a process of static uprating. The uprating process covers a complex range of processes, ranging from simple numerical uprating of financial values, to modelling the draw-down of old benefits through to the implications of the rising state pension age.

<span class="mw-page-title-main">Reservoir modeling</span>

In the oil and gas industry, reservoir modeling involves the construction of a computer model of a petroleum reservoir, for the purposes of improving estimation of reserves and making decisions regarding the development of the field, predicting future production, placing additional wells and evaluating alternative reservoir management scenarios.

<span class="mw-page-title-main">Traffic simulation</span>

Traffic simulation or the simulation of transportation systems is the mathematical modeling of transportation systems through the application of computer software to better help plan, design, and operate transportation systems. Simulation of transportation systems started over forty years ago, and is an important area of discipline in traffic engineering and transportation planning today. Various national and local transportation agencies, academic institutions and consulting firms use simulation to aid in their management of transportation networks.

Paramics is traffic microsimulation software, originally developed by Quadstone Ltd. There is a related pedestrian microsimulation product called the Urban Analytics Framework.

TSIS-CORSIM is a microscopic traffic simulation software package for signal systems, highway systems, freeway systems, or combined signal, highway and freeway systems. CORSIM consists of an integrated set of two microscopic simulation models that represent the entire traffic environment. NETSIM represents traffic on urban streets. FRESIM represents traffic on highways and freeways. Microscopic simulation models the movements of individual vehicles, which include the influences of geometric conditions, control conditions, and driver behavior. TSIS is an integrated development environment that enables users to conduct traffic operations analysis. Built using a component architecture, TSIS allows the user to customize the set of included tools, define and manage traffic analysis projects, define traffic networks and create inputs for traffic simulation analysis, execute traffic simulation models, and interpret the results of those models.

In mathematical modeling, deterministic simulations contain no random variables and no degree of randomness, and consist mostly of equations, for example difference equations. These simulations have known inputs and they result in a unique set of outputs. Contrast stochastic (probability) simulation, which includes random variables.

For pensions, a reliable Pension model is necessary for system simulations and projections, so it is important to have a sound database for pension system analyses. For an example of a complex pension model see e.g..

A dynamic microsimulation pension model is a type of a pension model projecting a pension system by means of a microsimulation and generating the complete history of each individual in a data set. The results of such model offer both the aggregate and individual indicators of the pension system. Thanks to complexity of results, there is a possibility to investigate the distribution of pensions, poverty of pensioners, impact of the changes of the pension formula, for more examples see e.g.. Detailed individual set of (administrative) data should serve as a model input.

The Dynamic Microsimulation Model of the Czech Republic is a dynamic microsimulation pension model simulating the pension system of the Czech Republic, owned by the Ministry of Labour and Social Affairs.

Historical dynamics broadly includes the scientific modeling of history. This might also be termed computer modeling of history, historical simulation, or simulation of history - allowing for an extensive range of techniques in simulation and estimation. Historical dynamics does not exist as a separate science, but there are individual efforts such as long range planning, population modeling, economic forecasting, demographics, global modeling, country modeling, regional planning, urban planning and many others in the general categories of computer modeling, planning, forecasting, and simulations.

References

  1. ↑ Orcutt, Guy H.; Caldwell, Steven; Wertheimer, Richard F. (1976). Policy Exploration Through Microanalytic Simulation. The Urban Institute. ISBN   978-0-87766-169-6.
  2. ↑ Rakha, H.; Van Aerde, M.; Bloomberg, L.; Huang, X. (January 1998). "Construction and Calibration of a Large-Scale Microsimulation Model of the Salt Lake Area". Transportation Research Record: Journal of the Transportation Research Board. 1644 (1): 93–102. doi:10.3141/1644-10. ISSN   0361-1981.
  3. ↑ Birkin, Mark; Wu, Belinda (2012). "A Review of Microsimulation and Hybrid Agent-Based Approaches". Agent-Based Models of Geographical Systems. Springer Netherlands: 51–68. doi:10.1007/978-90-481-8927-4_3. ISBN   978-90-481-8926-7.
  4. ↑ The International Microsimulation Association – Aims
  5. ↑ "TRIM3".
  6. ↑ Daguano, R. F.; Yoshioka, L. R.; Netto, M. L.; Marte, C. L.; Isler, C. A.; Santos, M. M. D.; Justo, J. F. (2023). "Automatic Calibration of Microscopic Traffic Simulation Models Using Artificial Neural Networks". Sensors. 23 (21): 8798. doi: 10.3390/s23218798 . PMC   10648796 .
  7. ↑ Hennessy, Deirdre A.; Flanagan, William M.; Tanuseputro, Peter; Bennett, Carol; Tuna, Meltem; Kopec, Jacek; Wolfson, Michael C.; Manuel, Douglas G. (2015). "The Population Health Model (POHEM): An overview of rationale, methods and applications". Population Health Metrics. 13: 24. doi: 10.1186/s12963-015-0057-x . PMC   4559325 . PMID   26339201.
  8. ↑ Ballas, D., Dorling, D., Thomas, B., & Rossiter, D. (2005). Geography matters: simulating the local impacts of national social policies (p. 491). Joseph Rowntree Foundation. doi : 10.2307/3650139, made freely available here: http://www.jrf.org.uk/publications/geography-matters-simulating-local-impacts-national-social-policies
  9. ↑ "JAS-mine".
  10. ↑ "About — LIAM2".
  11. ↑ "Modgen (Model generator)". 2009-09-30.
  12. ↑ "OpenM++".
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.