Relevant bibliographies by topics / Microsimulation

Academic literature on the topic 'Microsimulation'

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Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Microsimulation"

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Lomax, Nik, and Andrew Smith. "Microsimulation for demography." Australian Population Studies 1, no. 1 (November 19, 2017): 73–85. http://dx.doi.org/10.37970/aps.v1i1.14.

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Background: Microsimulation consists of a set of techniques for estimating characteristics and modelling change in populations of individuals. Aims: To demonstrate how microsimulation can be used by demographers who want to undertake population estimates and projections. Data and methods: We use data from the 2011 United Kingdom (UK) Census of population to create a synthetic population by age, sex and ethnic group. Static and dynamic microsimulations and the visualisation of results are undertaken using the statistical package R. The code and data used in the static and dynamic microsimulation are available via a GitHub repository. Results: A synthetic population in 2011 by age, sex and ethnicity was produced for the East London Borough of Tower Hamlets, estimated from two Census tables. A population projection was produced for each of these age, sex and ethnicity groups to 2021. We used a projection of the Bangladeshi population to visualise population growth by Middle-layer Super Output Area (MSOA) and to produce a population pyramid of estimates in 2021. Conclusions: We argue that microsimulation is an adaptable technique which is well suited to demography, for both population estimation and projection. Although our example is applied to the East London Borough of Tower Hamlets, the approach could be readily applied in Australia, or any other country.
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Kelly, Terence, and Kai Nagel. "Relaxation Criteria for Iterated Traffic Simulations." International Journal of Modern Physics C 09, no. 01 (February 1998): 113–32. http://dx.doi.org/10.1142/s0129183198000108.

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Iterative transportation microsimulations adjust traveler route plans by iterating between a microsimulation and a route planner. At each iteration, the route planner adjusts individuals' route choices based on the preceding microsimulations. Empirically, this process yields good results, but it is usually unclear when to stop the iterative process when modeling real-world traffic. This paper investigates several criteria to judge relaxation of the iterative process, emphasizing criteria related to traveler decision-making.
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Walker, Joan L. "Making Household Microsimulation of Travel and Activities Accessible to Planners." Transportation Research Record: Journal of the Transportation Research Board 1931, no. 1 (January 2005): 38–48. http://dx.doi.org/10.1177/0361198105193100105.

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There is a large gap between the aggregate, trip-based models used by transportation planning agencies and the activity-based, microsimulation methods espoused by those at the forefront of research. The modeling environment presented here is intended to bridge this gap by providing a palatable way for planning agencies to move toward advanced methods. Three components to bridging the gap are emphasized: an incremental approach, a demonstration of clear gains, and a provision of an environment that eases initial implementation and allows for expansion. The modeling environment (called STEP2) is a household microsimulator, developed in TransCAD, that can be used to implement a four-step model as well as models with longer-term behavior and trip chaining. An implementation for southern Nevada is described, and comparisons are made with the region's aggregate four-step model. The models perform similarly in numerous ways. A key advantage to the microsimulator is that it provides impacts by socioeconomic group (essential for equity analysis) and individual trip movements (for use in a vehicle microsimulator). A sensitivity analysis indicates that the microsimulation model has less inelastic cross elasticity of transit demand with respect to auto travel times than the aggregate model (aggregation error). The trade-off is that microsimulators have simulation error; results are presented regarding the severity of this error. This work shows that a shift to microsimulation does not necessarily require substantial investment to achieve many of the benefits. One of the greatest advantages is a flexible environment that can expand to include additional sensitivity to demographics and transportation policy variables.
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Farooq, Bilal, Eric J. Miller, Franco Chingcuanco, and Martin Giroux-Cook. "Microsimulation framework for urban price-taker markets." Journal of Transport and Land Use 6, no. 1 (April 10, 2013): 41–51. http://dx.doi.org/10.5198/jtlu.v6i1.325.

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In the context of integrated transportation and other urban engineering infrastructure systems, there are many examples of markets, where consumers exhibit price-taking behavior. While this behavior is ubiquitous, the underlying mechanism can be captured in a single framework. Here, we present a microsimulation framework of a price-taker market that recognizes this generality and develop efficient algorithms for the associated market-clearing problem. By abstracting the problem as a specific graph theoretic problem (i.e., maximum weighted bipartite graph), we are first able to exploit algorithms that are developed in graph theory. We then explore their appropriateness in terms of large-scale integrated urban microsimulations. Based on this, we further develop a generic and efficient clearing algorithm that takes advantage of the features specific to urban price-taker markets. This clearing solution is then used to operationalize two price-taker markets, from two different contexts, within a microsimulation of urban systems. The initial validation of results against the observed data generally shows a close match.
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Schweizer, Joerg, Cristian Poliziani, Federico Rupi, Davide Morgano, and Mattia Magi. "Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data." ISPRS International Journal of Geo-Information 10, no. 3 (March 14, 2021): 165. http://dx.doi.org/10.3390/ijgi10030165.

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A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources.
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Raju, Narayana, and Haneen Farah. "Evolution of Traffic Microsimulation and Its Use for Modeling Connected and Automated Vehicles." Journal of Advanced Transportation 2021 (September 24, 2021): 1–29. http://dx.doi.org/10.1155/2021/2444363.

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Traffic microsimulation has a functional role in understanding the traffic performance on the road network. This study originated with intent to understand traffic microsimulation and its use in modeling connected and automated vehicles (CAVs). Initially, the paper focuses on understanding the evolution of traffic microsimulation and on examining the various commercial and open-source simulation platforms available and their importance in traffic microsimulation studies. Following this, current autonomous vehicle (AV) microsimulation strategies are reviewed. From the review analysis, it is observed that AVs are modeled in traffic microsimulation with two sets of strategies. In the first set, the inbuilt models are used to replicate the driving behavior of AVs by adapting the models’ parameters. In the second strategy, AV behavior is programmed with the help of externalities (e.g., Application Programming Interface (API)). Studies simulating AVs with inbuilt models used mostly VISSIM compared to other microsimulation platforms. In addition, the studies are heavily focused on AVs’ penetration rate impact on traffic flow characteristics and traffic safety. On the other hand, studies which simulated AVs with externalities focused on the communication aspects for traffic management. Finally, the cosimulation strategies for simulating the CAVs are explored, and the ongoing research attempts are discussed. The present study identifies the limitations of present CAV microsimulation studies and proposes prospects and improvements in modeling AVs in traffic microsimulation.
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Martel, Laurent, Éric Caron Malenfant, and Alain Bélanger. "Microsimulation en démographie." Cahiers québécois de démographie 40, no. 2 (2011): 171. http://dx.doi.org/10.7202/1011538ar.

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Nigel Gilbert, G., and Klaus G. Troitzsch. "Social Science Microsimulation." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 56, no. 1 (September 1997): 71–78. http://dx.doi.org/10.1177/075910639705600107.

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Wolf, Douglas A. "The Role of Microsimulation in Longitudinal Data Analysis." Canadian Studies in Population 28, no. 2 (December 31, 2001): 313. http://dx.doi.org/10.25336/p67k5x.

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Microsimulation is well known as a tool for static analysis of tax and transfer policies, for the generation of programmatic cost estimates, and dynamic analyses of socio-economic and demographic systems. However, microsimulation also has the potential to contribute to longitudinal data analysis in several ways, including extending the range of outputs generated by a model, addressing several defective-data problems, and serving as a vehicle for missing-data imputation. This paper discusses microsimulation procedures suitable for several commonly-used statistical models applied to longitudinal data. It also addresses the unique role that can be played by microsimulation in longitudinal data analysis, and the problem of accounting for the several sources of variability associated with microsimulation procedures.
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Çağlayan, Çağlar, Hiromi Terawaki, Qiushi Chen, Ashish Rai, Turgay Ayer, and Christopher R. Flowers. "Microsimulation Modeling in Oncology." JCO Clinical Cancer Informatics, no. 2 (December 2018): 1–11. http://dx.doi.org/10.1200/cci.17.00029.

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Purpose Microsimulation is a modeling technique that uses a sample size of individual units (microunits), each with a unique set of attributes, and allows for the simulation of downstream events on the basis of predefined states and transition probabilities between those states over time. In this article, we describe the history of the role of microsimulation in medicine and its potential applications in oncology as useful tools for population risk stratification and treatment strategy design for precision medicine. Methods We conducted a comprehensive and methodical search of the literature using electronic databases—Medline, Embase, and Cochrane—for works published between 1985 and 2016. A medical subject heading search strategy was constructed for Medline searches by using a combination of relevant search terms, such as “microsimulation model medicine,” “multistate modeling cancer,” and “oncology.” Results Microsimulation modeling is particularly useful for the study of optimal intervention strategies when randomized control trials may not be feasible, ethical, or practical. Microsimulation models can retain memory of prior behaviors and states. As such, it allows an explicit representation and understanding of how various processes propagate over time and affect the final outcomes for an individual or in a population. Conclusion A well-calibrated microsimulation model can be used to predict the outcome of the event of interest for a new individual or subpopulations, assess the effectiveness and cost effectiveness of alternative interventions, and project the future disease burden of oncologic diseases. In the growing field of oncology research, a microsimulation model can serve as a valuable tool among the various facets of methodology available.
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Dissertations / Theses on the topic "Microsimulation"

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Szillat, Markus Theodor. "A low-level PRT microsimulation." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340268.

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Brouwers, Lisa. "Microsimulation Models for Disaster Policy Making." Doctoral thesis, Kista : Department of Computer and Systems Sciences [Institutionen för data- och systemvetenskap], Stockholms University, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-525.

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Kongmuang, Charatdao. "Modelling crime : a spatial microsimulation approach." Thesis, University of Leeds, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434205.

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Borsari, Claudio. "Microsimulation of transportation systems - theory and applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3711/.

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Jin, Jianhui. "A small area microsimulation model for water demand." Thesis, University of Leeds, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507779.

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Odden, Colin. "Sibship in Low Fertility Settings: A Microsimulation Approach." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1357237238.

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Look, Horace Wai Fung. "Accident risk assessment using microsimulation for dynamic route guidance." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ58669.pdf.

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Mohammed, Basheer. "Operational analysis of high-occupancy vehicle lanes using microsimulation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ62955.pdf.

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McCormick, Ashley Warren. "A dynamic microsimulation of Scotland's household composition, 2001-2035." Thesis, University of Liverpool, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.569261.

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A full, dynamic microsimulation of Scotland's household composition from 2001 to 2035 is given in this thesis. When projecting populations forward through time this particular method can be a powerful tool in explaining such change(s). Within this project the most systematic dynamic microsimulation of Scotland's household composition is given to date. The simulation process has been completed in conjunction with the General Register Office for Scotland and complements their household projection .methodology. By offering a current and systematic analysis of Scotland's demography, the project gives a platform for further understanding to the key influences and components of change to Scotland's past, present and future population and household composition. Amongst these key influences especial empirical analytic focus is given to relationship formation and dissolution and migration within Scotland. Results from the dynamic microsimulation provide a set of strong indicators to the distribution of demographic change in Scotland until 2035. It is important to note that this project has built a platform for further research into Scotland's population and household composition, which is also elaborated at the closing point of this thesis.
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Lovelace, Robin. "The energy costs of commuting : a spatial microsimulation approach." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/5027/.

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Commuting is a daily ritual for a large proportion of the world's population. It is important materially, consuming large amounts of time, money and natural resources. As with many routine activities travel to work is often taken for granted but its energy consumption is of particular interest due to its heavy reliance on fossil fuels and the inflexibility of the demand for commuting. This understudied area of knowledge, the energy costs of travel to work, forms the basis of the thesis. There is much research into commuting and transport energy use as separate fields, but they have rarely been combined in the same analysis, let alone at high levels of geographical resolution. The well-established field of spatial microsimulation offers tools for investigating commuting patterns in detail at local and individual levels, with major potential benefits for transport planning. For the first time this method is deployed to investigate variability in commuter energy use both between and within small administrative zones. The maps of commuter energy use presented in this thesis illustrate this variability at national, regional and local levels. Supporting previous research, the results suggest that a range of geographical factors influence energy use for travel. This has important policy implications: when high transport energy use in commuting is due to lack of jobs in the vicinity, for example, modal shift (e.g.~from cars to bicycles) on its own has a limited potential to reduce energy costs. Such insights are quantified using existing aggregate data. The main methodological contribution of this work, however, is to add individual-level factors to the analysis - creating the potential for policy makers to also assess the distributional impacts of their interventions and target specific types of commuters having high transport energy costs, rather than treat areas as homogeneous blocks. This potential is demonstrated with a case study of South Yorkshire, where commuting energy use is cross-tabulated by socio-economic variables and disaggregated over geographical space. The areas where commuting energy use is less evenly distributed across the population, for example in urban centres, are likely to benefit most from policies that target the specific groups. Areas where commuter energy use is more even, such as Stocksbridge (in Northwest Sheffield), will benefit from more universal policies. The thesis contributes to human knowledge new information about the energy costs of commuting, its variability at various levels and insight into the implications. New methods of generating and analysing individual-level data for the analysis of commuter energy use have also been developed. These are reproducible (see the GitHub repository https://github.com/Robinlovelace/thesis-reproducible for example code and data) and will be of interest to researchers and policy makers investigating the energy security, resource efficiency and potential welfare impacts of interventions in personal travel systems.
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Books on the topic "Microsimulation"

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Troitzsch, Klaus G., Ulrich Mueller, G. Nigel Gilbert, and Jim E. Doran, eds. Social Science Microsimulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03261-9.

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G, Troitzsch Klaus, ed. Social science microsimulation. Berlin: Springer, 1996.

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O'Donoghue, Cathal. Farm-Level Microsimulation Modelling. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63979-6.

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Bélanger, Alain, and Patrick Sabourin. Microsimulation and Population Dynamics. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-44663-9.

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Asghar, Zaidi M., Harding Ann 1958-, and Williamson Paul, eds. New frontiers in microsimulation modelling. Farnham, England: Ashgate, 2009.

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Marois, Guillaume, and Samir KC. Microsimulation Population Projections with SAS. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79111-7.

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International Microsimulation Association. Inaugural meeting. New frontiers in microsimulation modelling. Farnham, England: Ashgate, 2009.

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Perese, Kevin. Mate matching for microsimulation models. Washington, DC: Congressional Budget Office, 2002.

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van Tongeren, Frank W. Microsimulation Modelling of the Corporate Firm. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-52068-6.

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O'Donoghue, Cathal, Dimitris Ballas, Graham Clarke, Stephen Hynes, and Karyn Morrissey, eds. Spatial Microsimulation for Rural Policy Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30026-4.

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Book chapters on the topic "Microsimulation"

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Birkin, Mark. "Microsimulation." In Urban Informatics, 845–64. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_44.

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AbstractFrom origins in economics and financial analysis, microsimulation has become an important technique for spatial analysis. The method relies on conversion of aggregate census tables, sometimes complemented by sample data at the individual level, to synthetic lists of people and households. The individual records generated by the microsimulation can be aggregated flexibly to small areas, linked to create new attributes, and projected forward in time under stable conditions, or in the context of ‘what-if’ policy scenarios. The chapter outlines the basic building blocks of microsimulation and shows how these are combined within a representative practical application. It is argued that further progress can be expected through advances in computation, assimilation of data into models, and greater capacity to handle uncertainty and dynamics. We also expect the creation of more sophisticated architectures to reflect the interdependence between population structures at the micro-scale, and the supply-side infrastructures and urban environments in which they evolve.
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Heppenstall, Alison J., and Dianna M. Smith. "Spatial Microsimulation." In Handbook of Regional Science, 1–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-36203-3_65-1.

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Heppenstall, Alison J., and Dianna M. Smith. "Spatial Microsimulation." In Handbook of Regional Science, 1235–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-23430-9_65.

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Heppenstall, Alison J., and Dianna M. Smith. "Spatial Microsimulation." In Handbook of Regional Science, 1767–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-60723-7_65.

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Heike, Hans-Dieter, Kai Beckmann, Achim Kaufmann, Harald Ritz, and Thomas Sauerbier. "A Comparison of a 4GL and an Object-oriented Approach in Micro Macro Simulation." In Social Science Microsimulation, 3–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03261-9_1.

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Helbing, Dirk. "Survival Analysis, Master Equation, Efficient Simulation of Path-Related Quantities, and Hidden State Concept of Transitions." In Social Science Microsimulation, 179–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03261-9_10.

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Liebrand, Wim B. G., and David M. Messick. "Game Theory, Decision Making in Conflicts and Computer Simulations: a Good-Looking Triad." In Social Science Microsimulation, 211–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03261-9_11.

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Mueller, Ulrich. "From Life Event Analysis to Life Course Analysis." In Social Science Microsimulation, 237–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03261-9_12.

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Suleiman, Ramzi. "Simulating Cooperation and Competition: Present State and Future Objectives." In Social Science Microsimulation, 264–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03261-9_13.

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Hegselmann, Rainer. "Understanding Social Dynamics: The Cellular Automata Approach." In Social Science Microsimulation, 282–306. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03261-9_14.

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Conference papers on the topic "Microsimulation"

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Hannappel, Marc. "A Plea For Microsimulation." In 30th Conference on Modelling and Simulation. ECMS, 2016. http://dx.doi.org/10.7148/2016-0204.

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Wang, Yifei, and Lun Wu. "Residential mobility microsimulation models." In Seventeenth China Symposium on Remote Sensing. SPIE, 2010. http://dx.doi.org/10.1117/12.910436.

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Miller, Eric J., John Douglas Hunt, John E. Abraham, and Paul A. Salvini. "Microsimulation Modeling Research in Canada." In Seventh International Conference on Applications of Advanced Technologies in Transportation (AATT). Reston, VA: American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40632(245)104.

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Deluka-Tibljaš, Aleksandra, Ivan Klasić, Sanja Šurdonja, and Irena Ištoka Otković. "Application of traffic simulation models for urban road network analyses – case Studies from Rijeka city." In 6th International Conference on Road and Rail Infrastructure. University of Zagreb Faculty of Civil Engineering, 2021. http://dx.doi.org/10.5592/co/cetra.2020.1052.

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The process of road network planning and designing in urban areas can be significantly improved by using microsimulation of traffic models. Traffic microsimulations are used for analyses and estimation of new proposals as well as for the reconstruction of existing infrastructure in order to reach optimum solution for defined problem. In this paper, applications of different analyses approaches are analyzed in two case studies. Both case studies are located in the city of Rijeka but in different parts of the city, in different traffic conditions and in circumstances where different changes in traffic network are planned. In both cases new solutions were tested through VISSIM traffic model and by application of SIDRA Intersection methodology. VISSIM is a stochastic, discrete, micro-simulation model designed for traffic analyses while SIDRA Intersection is a lane-based micro-analytical model. The results proved the suitability as well as advantages and disadvantages of both approaches. The paper contains suggestions for optimal application of selected models regarding different traffic problems.
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Zinn, Sabine, Jutta Gampe, Jan Himmelspach, and Adelinde M. Uhrmacher. "MIC-core: A tool for microsimulation." In 2009 Winter Simulation Conference - (WSC 2009). IEEE, 2009. http://dx.doi.org/10.1109/wsc.2009.5429424.

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Molnar, Istvan, and Imre Sinka. "Toward Agent-Based Microsimulation--Another Approach." In First Asia International Conference on Modelling & Simulation (AMS'07). IEEE, 2007. http://dx.doi.org/10.1109/ams.2007.104.

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Zinn, Sabine, Jutta Gampe, Jan Himmelspach, and Adelinde M. Uhrmacher. "A DEVS model for demographic microsimulation." In the 2010 Spring Simulation Multiconference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1878537.1878689.

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O’Cinneide, D., and D. Connell. "The accuracy of traffic microsimulation modelling." In URBAN TRANSPORT 2006. Southampton, UK: WIT Press, 2006. http://dx.doi.org/10.2495/ut060281.

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Xiao-Yuan Wang and Jia-Sen Liu. "Research of the lane utilization with microsimulation." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527398.

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Huang, Weinan, Jian Sun, Heng Wang, and Keping Li. "Design of IDSS for Traffic Microsimulation Model Calibration." In 2008 Second International Symposium on Intelligent Information Technology Application (IITA). IEEE, 2008. http://dx.doi.org/10.1109/iita.2008.229.

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Reports on the topic "Microsimulation"

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Basu, N., R. J. Pryor, T. Quint, and T. Arnold. Aspen: A microsimulation model of the economy. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/399684.

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Spielauer, Martin. The "LifeCourse" model, a competing risk cohort microsimulation model: source code and basic concepts of the generic microsimulation programming language Modgen. Rostock: Max Planck Institute for Demographic Research, November 2006. http://dx.doi.org/10.4054/mpidr-wp-2006-046.

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Brown, M. J., C. Mueller, B. Bush, and P. Stretz. Exposure estimates using urban plume dispersion and traffic microsimulation models. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/564119.

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Michaud, Darryl. Driver Distraction in Microsimulation of a Mid-Block Pedestrian Crossing. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6438.

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5

Gasior, Katrin, Silvia Navarro, Jukka Pirttilä, and Mari Kangasniemi. Distributional impacts of agricultural policies in Zambia: A microsimulation approach. UNU-WIDER, December 2022. http://dx.doi.org/10.35188/unu-wider/2022/276-8.

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6

McLennan, David. Iterative proportional fitting for reweighting input data in SOUTHMOD microsimulation models. UNU-WIDER, October 2021. http://dx.doi.org/10.35188/unu-wider/wtn/2021-17.

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7

Garcia-Fry, Martin, Osamu Murao, Syamsul Bachri, and Luis Moya. Land-Use Microsimulation Model for Livelihood Diversification After the 2010 Merapi Volcano Eruptions. Peeref, September 2022. http://dx.doi.org/10.54985/peeref.2209p4883110.

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8

Roantree, Barra, Karina Doorley, Theano Kakoulidou, and Seamus O'Malley. Budget 2022. ESRI, December 2021. http://dx.doi.org/10.26504/qec2021win_sa_roantree.

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Abstract:
This Article outlines and assesses changes to the tax and welfare system announced as part of Budget 2022. It first looks at the main taxation measures announced before turning to employment, education and social welfare supports. It then considers the effect of the package of measures as a whole on the incomes of households using representative survey data from the Survey of Incomes and Living Conditions run on SWITCH – the ESRI’s tax and benefit microsimulation model – and ITSim – an indirect tax microsimulation model developed jointly by the ESRI and the Department of Finance. The Article concludes with some brief reflections on inflation forecasts and the policy-making process.
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9

Gasior, Katrin, Helen Barnes, Maria Jouste, Jesse Lastunen, David McLennan, Michael Noble, Rodrigo C. Oliveira, Pia Rattenhuber, and Gemma Wright. Full-year adjustment for modelling COVID-19 policies in SOUTHMOD tax-benefit microsimulation models. UNU-WIDER, October 2021. http://dx.doi.org/10.35188/unu-wider/wtn/2021-18.

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Lastunen, Jesse. On-model adjustment of incomes during COVID-19 in SOUTHMOD tax-benefit microsimulation models. UNU-WIDER, May 2022. http://dx.doi.org/10.35188/unu-wider/wtn/2022-4.

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