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Journal articles on the topic 'Microsimulation modelling'

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Published: 4 June 2021

Last updated: 31 January 2023

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1

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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2

Holden, Ken. "Microsimulation modelling of the corporate firm." International Journal of Forecasting 13, no. 1 (March 1997): 143–44. http://dx.doi.org/10.1016/s0169-2070(96)00721-2.

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3

Hasan, Umair, Andrew Whyte, and Hamad AlJassmi. "A Microsimulation Modelling Approach to Quantify Environmental Footprint of Autonomous Buses." Sustainability 14, no. 23 (November 24, 2022): 15657. http://dx.doi.org/10.3390/su142315657.

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In this study a novel microsimulation-based methodology for environmental assessment of urban systems is developed to address the performance of autonomous mass-mobility against conventional approaches. Traffic growth and microsimulation models, calibrated using real data, are utilised to assess four traffic management scenarios: business-as-usual; public bus transport case; public-bus rapid transit (BRT) case; and, a traffic-demand-responsive-autonomous-BRT case, focusing on fuel energy efficiency, headways, fleet control and platooning for lifecycle analysis (2015–2045) of a case study 3.5 km long 5-lane dual-carriageway section. Results showed that both energy consumption and exhaust emission rates depend upon traffic volume and flow rate factors of vehicle speed-time curves; acceleration-deceleration; and braking rate. The results measured over-reliance of private cars utilising fossil fuel that cause congestions and high environmental footprint on urban roads worsen causing excessive travel times. Public transport promotion was found to be an effective and easy-to-implement environmental burden reduction strategy. Results showed significant potential of autonomous mass-mobility systems to reduce environmental footprint of urban traffic, provided adequate mode-shift can be achieved. The study showed utility of microsimulations for energy and emissions assessment, it linked bus network performance assessment with environmental policies and provided empirical models for headway and service frequency comparisons at vehicle levels. The developed traffic fleet operation prediction methodology for long-term policy implications and tracking models for accurate yearly simulation of real-world vehicle operation profiles are applicable for other sustainability-oriented urban traffic management studies.

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Ballas, Dimitris, Richard Kingston, John Stillwell, and Jianhui Jin. "Building a Spatial Microsimulation-Based Planning Support System for Local Policy Making." Environment and Planning A: Economy and Space 39, no. 10 (October 2007): 2482–99. http://dx.doi.org/10.1068/a38441.

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This paper presents a spatial microsimulation modelling and predictive policy analysis system called Micro-MaPPAS, a Planning Support System (PSS) constructed for a local strategic partnership in a large metropolitan area of the UK. The innovative feature of this system is the use of spatial microsimulation techniques for the enhancement of local policy decision making in connection with the neighbourhood renewal strategy. The paper addresses the relevant data issues and technical aspects of the linkage of spatial microsimulation modelling frameworks to PSS and deals with the wider implications that such a linkage may have to local policy and planning procedures. Finally, the paper presents some illustrative examples of the policy relevance and policy analysis potential of the software.

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5

O’Donoghue, Cathal, and Gijs Dekkers. "Increasing the Impact of Dynamic Microsimulation Modelling." International Journal of Microsimulation 11, no. 1 (2017): 61–96. http://dx.doi.org/10.34196/ijm.00174.

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6

Dougherty, Mark, Ken Fox, Michael Cullip, and Marco Boero. "Technological advances that impact on microsimulation modelling." Transport Reviews 20, no. 2 (January 2000): 145–71. http://dx.doi.org/10.1080/014416400295220.

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Siripanich, Amarin, and Taha Hossein Rashidi. "Dymium: A modular microsimulation modelling framework for integrated urban modelling." SoftwareX 12 (July 2020): 100555. http://dx.doi.org/10.1016/j.softx.2020.100555.

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8

Leeuwen, Eveline S. van. "Review of Small Area Estimation and Microsimulation Modelling." International Journal of Microsimulation 10, no. 2 (2016): 208–9. http://dx.doi.org/10.34196/ijm.00162.

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9

Zhang, Jing, Nick Hounsell, and Birendra Shrestha. "Calibration of bus parameters in microsimulation traffic modelling." Transportation Planning and Technology 35, no. 1 (February 2012): 107–20. http://dx.doi.org/10.1080/03081060.2012.635431.

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10

Nolan, Patrick, Yvonne Wang, and Meghan Stephens. "Modelling Child Poverty and Wellbeing the Treasury’s TAWA microsimulation model." Policy Quarterly 18, no. 3 (August 15, 2022): 58–63. http://dx.doi.org/10.26686/pq.v18i3.7717.

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Large tax–transfer microsimulation models can play a key role in guiding tax–transfer analysis and reform. This article discusses the Treasury’s microsimulation model of the tax–transfer system (the Tax and Welfare Analysis (TAWA) model), including how it is used and the standard outputs it produces. The article also considers whether these standard outputs continue to be fit for purpose. This includes a discussion of different ways of estimating poverty impacts, the role reporting should give to financial incentives to work, and the opportunities provided by improved data. This final point is particularly important for understanding take-up and the prospect for extending the model to cover non-financial measures.

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Náfrádi, Bálint, Hannah Kiiver, Subas Neupane, Natalie C. Momen, Kai N. Streicher, and Frank Pega. "Estimating the population exposed to a risk factor over a time window: A microsimulation modelling approach from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury." PLOS ONE 17, no. 12 (December 30, 2022): e0278507. http://dx.doi.org/10.1371/journal.pone.0278507.

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Objectives Burden of disease estimation commonly requires estimates of the population exposed to a risk factor over a time window (yeart to yeart+n). We present a microsimulation modelling approach for producing such estimates and apply it to calculate the population exposed to long working hours for one country (Italy). Methods We developed a three-model approach: Model 1, a multilevel model, estimates exposure to the risk factor at the first year of the time window (yeart). Model 2, a regression model, estimates transition probabilities between exposure categories during the time window (yeart to yeart+n). Model 3, a microsimulation model, estimates the exposed population over the time window, using the Monte Carlo method. The microsimulation is carried out in three steps: (a) a representative synthetic population is initiated in the first year of the time window using prevalence estimates from Model 1, (b) the exposed population is simulated over the time window using the transition probabilities from Model 2; and (c) the population is censored for deaths during the time window. Results We estimated the population exposed to long working hours (i.e. 41–48, 49–54 and ≥55 hours/week) over a 10-year time window (2002–11) in Italy. We populated all three models with official data from Labour Force Surveys, United Nations population estimates and World Health Organization life tables. Estimates were produced of populations exposed over the time window, disaggregated by sex and 5-year age group. Conclusions Our modelling approach for estimating the population exposed to a risk factor over a time window is simple, versatile, and flexible. It however requires longitudinal exposure data and Model 3 (the microsimulation model) is stochastic. The approach can improve accuracy and transparency in exposure and burden of disease estimations. To improve the approach, a logical next step is changing Model 3 to a deterministic microsimulation method, such as modelling of microflows.

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Shalaby, Amer, Baher Abdulhai, and Jinwoo Lee. "Assessment of streetcar transit priority options using microsimulation modelling." Canadian Journal of Civil Engineering 30, no. 6 (December 1, 2003): 1000–1009. http://dx.doi.org/10.1139/l03-010.

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The thrust of a recently published transportation vision for Toronto is focused largely on reducing automobile dependence via a number of interacting strategies, including the wide application of transit priority policies to improve transit competitiveness. This paper reports on quantifying the impacts of several transit priority schemes, with the streetcar operation along King Street in the heart of Toronto as a case study. Four scenarios were modelled in a micro simulation framework. They include the status quo (involving unconditional transit signal priority, already in operation), turning off existing transit signal priority, prohibiting all left turns, and finally prohibiting traffic from King Street. To quantify the impacts of any of the above scenarios, a set of common measures of effectiveness was used, including transit travel time and speed, effective headway, service frequency and person throughput, bunching, fleet size implications, and overall traffic and transit average speeds. The results show the relative merits of the four scenarios and two strategies for improving streetcar service along the King route are recommended. The first is to prohibit all left turns along the route, while the second, admittedly more aggressive, is to potentially transform the arterial into a transit mall accessible only to streetcars.Key words: transit signal priority, transit priority, signal control, microsimulation, streetcars.

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13

Pereira Bandeira, Talyson Pereira, Lisel Expósito Martín, and Manoel Mendonça de Castro Neto. "A method for estimating pedestrian critical gap using microsimulation." TRANSPORTES 29, no. 1 (April 30, 2021): 132–47. http://dx.doi.org/10.14295/transportes.v29i1.2240.

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Estimating critical gap is crucial for modelling pedestrian level of service at crossings. Critical gap modelling becomes more challenging in signalized crossings because the proportion of pedestrians seeking gaps during red is usually unknown. Besides, the willingness – or lack of it – to cross during red signal indication varies by pedestrian and local characteristics, which makes gap acceptance modelling even more challenging. The main objective of this study is to propose a method for estimating pedestrian critical gap at signalized crossings using Vissim. The method considers that all pedestrians seek for gaps on red, and the critical gap parameter is calibrated for each pedestrian type having delay as calibration target. The results showed MAPE values of 2% and 9% for the two studied crossings. This method was compared to three existing methods of critical gap estimation. The results showed that the proposed method yielded the best estimations of delay, followed by the HCM’s.

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OBrien, Eugene J., Cathal Leahy, Bernard Enright, and Colin C. Caprani. "Validation of scenario modelling for bridge loading." Baltic Journal of Road and Bridge Engineering 11, no. 3 (September 30, 2016): 233–41. http://dx.doi.org/10.3846/bjrbe.2016.27.

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Accurate estimates of characteristic bridge load effects are required for efficient design and assessment of bridges, and long-run traffic simulations are an effective method for estimating the effects. For multi-lane same-direction traffic, truck weights and locations on the bridge are correlated and this affects the calculated load effects. Scenario Modelling is a recently developed method, which uses weigh-in-motion data to simulate multi-lane same-direction traffic while maintaining location and weight correlations. It has been unclear however, whether the method may produce unrealistic driver behaviour when extrapolating beyond the weigh-in-motion measuring period. As weighin-motion databases with more than about a year of data are not available, a microsimulation traffic model, which can simulate driver behaviour, is used here to assess the accuracy of extrapolating traffic effects using Scenario Modelling. The microsimulation is used to generate an extended reference dataset against which the results of Scenario Modelling are compared. It is found that the characteristic load effects obtained using Scenario Modelling compare well with the reference dataset. As a result, for the first time researchers and practitioners can model two-lane same-direction traffic loading on bridges while being confident that the approach is generating accurate estimates of characteristic load effects as well as effectively reproducing the complex traffic correlations involved.

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15

Lidbe, Abhay D., Alexander M. Hainen, and Steven L. Jones. "Comparative study of simulated annealing, tabu search, and the genetic algorithm for calibration of the microsimulation model." SIMULATION 93, no. 1 (January 2017): 21–33. http://dx.doi.org/10.1177/0037549716683028.

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Microsimulation modeling is one of the contemporary techniques that has potential to perform complex transportation studies faster, safer, and in a less expensive manner. However, to get accurate and reliable results, the microsimulation models need to be well calibrated. Microsimulation model consists of various sub-models each having many parameters, most of which are user-adjustable and are attuned for calibrating the model. Manual calibration involves an iterative trial-and-error process of using the intuitive discrete values of each parameter and feasible combinations of multiple parameters each time until the desired results are obtained. With this approach, it is possible to easily get caught in a never-ending circular process of fixing one problem only to generate another. This can make manual calibration a time-consuming process and is suggested only when the number of parameters is small. However, when the calibration parameter subset is large, an automated process is suggested in the literature. Amongst the meta-heuristics used for calibrating microsimulation models, the genetic algorithm (GA) has been widely used and simulated annealing (SA) has been used only once in the past. Thus, the question of which meta-heuristics is more suitable for the problem of calibration of the microsimulation model still remains open. Thus, the objective of this paper is to evaluate and compare the manual and three (the GA, SA, and tabu search (TS)) meta-heuristics for calibration of microsimulation models. This paper therefore addresses the need to examine and identify the suitability of a meta-heuristics for calibrating microsimulation models. The results show that the meta-heuristics approach can be relied upon for calibrating simulation models very effectively, as it offers the benefit of automating the cumbersome calibrating process. All three meta-heuristics (the GA, SA, and TS) have the ability to find better calibrating parameters than the manually calibrated parameters. The number of better solutions, the best solution, and convergence to the best solution by TS is better than those by the GA and SA. Significant time can be saved by automating calibration of microsimulation models using meta-heuristics. The approach presented in this research can be used to help engineers and planners achieve better modeled results, as the calibration of microsimulation models is likely to become more complex in the future.

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Rahman, Azizur, Ann Harding, Robert Tanton, and Shuangzhe Liu. "Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation." International Journal of Microsimulation 3, no. 2 (2009): 3–22. http://dx.doi.org/10.34196/ijm.00035.

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17

O’Donoghue, Cathal, Karyn Morrissey, and John Lennon. "Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices." International Journal of Microsimulation 7, no. 1 (2013): 26–75. http://dx.doi.org/10.34196/ijm.00093.

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18

Creedy, John, Guyonne Kalb, and Hsein Kew. "CONFIDENCE INTERVALS FOR POLICY REFORMS IN BEHAVIOURAL TAX MICROSIMULATION MODELLING." Bulletin of Economic Research 59, no. 1 (January 2007): 37–65. http://dx.doi.org/10.1111/j.0307-3378.2007.00250.x.

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19

Petreski, Blagica, and Marjan Petreski. "Dynamic microsimulation modelling of potential pension reforms in North Macedonia." Journal of Pension Economics and Finance 20, no. 1 (January 10, 2020): 49–66. http://dx.doi.org/10.1017/s1474747219000374.

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AbstractDue to structural and policy shifts, pension deficit in North Macedonia doubled over a decade and significantly outpaced the central budget deficit. The objective of the paper is to examine fiscal and development effects of few pension-reform designs. We constructed MK-PENS Dynamic Microsimulation Pension Model and simulated the effects of few reforms affecting one stakeholder and few combined reforms. Results robustly suggest that without reform and assuming only statutory pension adjustment, the deficit will remain as is. Simulated scenarios suggest that proposed pension reforms significantly reduce the pension deficit, with the most favourable results obtained within the combined scenarios of shared burden. Gradual introduction of reform's elements should come into play in case large political cost is envisaged.

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20

Svoray, Tal, and Itzhak Benenson. "Scale and adequacy of environmental microsimulation." Ecological Complexity 6, no. 2 (June 2009): 77–79. http://dx.doi.org/10.1016/j.ecocom.2009.03.011.

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21

Richiardi, Matteo G., and Ross E. Richardson. "JAS-mine: A new platform for microsimulation and agent-based modelling." International Journal of Microsimulation 10, no. 1 (2016): 106–34. http://dx.doi.org/10.34196/ijm.00151.

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Petelin, L., M. Cunich, P. Procopio, C. Nickson, I. Campbell, and A. H. Trainer. "CN3 It's All in the Family: Microsimulation Modelling of Genetic Testing." Value in Health 24 (June 2021): S3. http://dx.doi.org/10.1016/j.jval.2021.04.018.

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23

CAI, LIXIN, JOHN CREEDY, and GUYONNE KALB. "ACCOUNTING FOR POPULATION AGEING IN TAX MICROSIMULATION MODELLING BY SURVEY REWEIGHTING*." Australian Economic Papers 45, no. 1 (March 2006): 18–37. http://dx.doi.org/10.1111/j.1467-8454.2006.00275.x.

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Wood, Gavin, Richard Watson, and Paul Flatau. "Microsimulation Modelling of Tenure Choice and Grants to Promote Home Ownership." Australian Economic Review 39, no. 1 (March 2006): 14–34. http://dx.doi.org/10.1111/j.1467-8462.2006.00393.x.

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Cortés, Cristián E., Vanessa Burgos, and Rodrigo Fernández. "Modelling passengers, buses and stops in traffic microsimulation: review and extensions." Journal of Advanced Transportation 44, no. 2 (April 2010): 72–88. http://dx.doi.org/10.1002/atr.110.

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Courtioux, Pierre, Stéphane Gregoir, and Dede Houeto. "Modelling the distribution of returns on higher education: A microsimulation approach." Economic Modelling 38 (February 2014): 328–40. http://dx.doi.org/10.1016/j.econmod.2014.01.010.

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Cronin, Kathleen A., Julie M. Legler, and Ruth D. Etzioni. "Assessing uncertainty in microsimulation modelling with application to cancer screening interventions." Statistics in Medicine 17, no. 21 (November 15, 1998): 2509–23. http://dx.doi.org/10.1002/(sici)1097-0258(19981115)17:21<2509::aid-sim949>3.0.co;2-v.

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Arnold, Kellyn F., Wendy J. Harrison, Alison J. Heppenstall, and Mark S. Gilthorpe. "DAG-informed regression modelling, agent-based modelling and microsimulation modelling: a critical comparison of methods for causal inference." International Journal of Epidemiology 48, no. 1 (December 5, 2018): 243–53. http://dx.doi.org/10.1093/ije/dyy260.

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29

Rosser, Julian F., Gavin Long, Sameh Zakhary, Doreen S. Boyd, Yong Mao, and Darren Robinson. "Modelling Urban Housing Stocks for Building Energy Simulation using CityGML EnergyADE." ISPRS International Journal of Geo-Information 8, no. 4 (March 29, 2019): 163. http://dx.doi.org/10.3390/ijgi8040163.

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Understanding the energy demand of a city’s housing stock is an important focus for local and national administrations to identify strategies for reducing carbon emissions. Building energy simulation offers a promising approach to understand energy use and test plans to improve the efficiency of residential properties. As part of this, models of the urban stock must be created that accurately reflect its size, shape and composition. However, substantial effort is required in order to generate detailed urban scenes with the appropriate level of attribution suitable for spatially explicit simulation of large areas. Furthermore, the computational complexity of microsimulation of building energy necessitates consideration of approaches that reduce this processing overhead. We present a workflow to automatically generate 2.5D urban scenes for residential building energy simulation from UK mapping datasets. We describe modelling the geometry, the assignment of energy characteristics based upon a statistical model and adopt the CityGML EnergyADE schema which forms an important new and open standard for defining energy model information at the city-scale. We then demonstrate use of the resulting urban scenes for estimating heating demand using a spatially explicit building energy microsimulation tool, called CitySim+, and evaluate the effects of an off-the-shelf geometric simplification routine to reduce simulation computational complexity.

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Kamo, Ken-Ichi, Keisuke Fukui, Yuri Ito, Tomio Nakayama, and Kota Katanoda. "How much can screening reduce colorectal cancer mortality in Japan? Scenario-based estimation by microsimulation." Japanese Journal of Clinical Oncology 52, no. 3 (December 11, 2021): 221–26. http://dx.doi.org/10.1093/jjco/hyab195.

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Abstract Background Screening is one of the effective interventions for the reduction of colorectal cancer mortality. Though the Japanese government recommends faecal occult blood test and colonoscopy as a follow-up examination following a diagnosis, both participation rates have not been so high and the national mortality rate has not shown a clear decreasing trend. Methods Microsimulation models simulate the life histories of a large population of individuals under various scenarios. In this study, we applied a microsimulation model to estimate the reduction of colorectal cancer mortality based on screening scenarios. Results The effect of reducing the age-standardized mortality rate for colorectal cancer was estimated at 9.4% for men and 6.0% for women under the scenario which calls for 50% participation in faecal occult blood test and 90% participation of follow-up examination. This scenario corresponds to the goal setting for screening in the third-term of the Basic Plan to Promote Cancer Control Programs in Japan. Conclusions Our microsimulation model was found to be useful in estimating the mortality reduction effect of cancer control policy. Such modelling techniques can be utilized to develop effective and optimal cancer control programs.

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Zhu, Sicong, LiSian Tey, and Luis Ferreira. "Genetic Algorithm Based Microscale Vehicle Emissions Modelling." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/178490.

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There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO2are proposed. A genetic algorithm approach is adopted to select the predicting variables for the black box model. The approach is capable of solving combinatorial optimization problems. Overall, the emission prediction results reveal that the proposed new models outperform conventional equations in terms of accuracy and robustness.

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Alam, MD Jahedul, and Muhammad Ahsanul Habib. "A dynamic programming optimization for traffic microsimulation modelling of a mass evacuation." Transportation Research Part D: Transport and Environment 97 (August 2021): 102946. http://dx.doi.org/10.1016/j.trd.2021.102946.

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Nourinejad, Mehdi, Adam Wenneman, Khandker Nurul Habib, and Matthew J. Roorda. "Truck parking in urban areas: Application of choice modelling within traffic microsimulation." Transportation Research Part A: Policy and Practice 64 (June 2014): 54–64. http://dx.doi.org/10.1016/j.tra.2014.03.006.

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Hanaoka, Kazumasa, and Graham P. Clarke. "Spatial microsimulation modelling for retail market analysis at the small-area level." Computers, Environment and Urban Systems 31, no. 2 (March 2007): 162–87. http://dx.doi.org/10.1016/j.compenvurbsys.2006.06.003.

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Ballas, Dimitris, and Graham P. Clarke. "Modelling the Local Impacts of National Social Policies: A Spatial Microsimulation Approach." Environment and Planning C: Government and Policy 19, no. 4 (August 2001): 587–606. http://dx.doi.org/10.1068/c0003.

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Farrag, Siham G., Moulay Youssef El-Hansali, Ansar-Ul-Haque Yasar, Elhadi M. Shakshuki, and Haroon Malik. "A microsimulation-based analysis for driving behaviour modelling on a congested expressway." Journal of Ambient Intelligence and Humanized Computing 11, no. 12 (May 20, 2020): 5857–74. http://dx.doi.org/10.1007/s12652-020-02098-5.

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Allsop, Richard E. "Transport networks and their use: how real can modelling get?" Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1872 (March 6, 2008): 1879–92. http://dx.doi.org/10.1098/rsta.2008.0013.

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The context for modelling transport systems and their use is set out. Such modelling is based on representing the transport system by a network of nodes and links, and the characteristics of this representation needed to reflect the principal realities of the system are outlined. The characteristics of use of the system that need to be reflected are described. Purposes of the modelling are set out and its evolution is described, starting from the basic traffic assignment model and discussing its generalizations and extensions in the search for greater realism—first in steady-state modelling for fixed demand, and then considering variable demand and time dependence. Further progress towards appropriate realism is seen as requiring communication and cooperation between the modellers and the users of models, helped perhaps by combining the advantages of analytical modelling and microsimulation.

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Shanmugam, Ramalingam. "Small area estimation and microsimulation modeling." Journal of Statistical Computation and Simulation 90, no. 7 (June 13, 2019): 1346–47. http://dx.doi.org/10.1080/00949655.2019.1628900.

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Giofrè, Vincenzo, Michał Maciejewski, Agnieszka Merkisz-Guranowska, Bartłomiej Piątkowski, and Vittorio Astarita. "Real road network application of a new microsimulation tool: TRITONE." Archives of Transport 27-28, no. 3-4 (December 31, 2013): 111–21. http://dx.doi.org/10.5604/01.3001.0004.0114.

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The aim of the paper is to carry out a comparative study of 13 different traffic flow models available in TRITONE, a new road traffic simulator that specializes particularly in quantitative road safety assessment. After a short introduction on a traffic flow modelling, a description of the TRITONE functionality is given and various types of behavioural models available in this tool are presented in brief. Then a part of Poznan (Poland) network that served as the study area, was illustrated. The following section lists all the models used in the research and provides a comparison of the results obtained with these models. The article ends with conclusions on the results’ quality of individual models.

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Wu, B. M., M. H. Birkin, and P. H. Rees. "A spatial microsimulation model with student agents." Computers, Environment and Urban Systems 32, no. 6 (November 2008): 440–53. http://dx.doi.org/10.1016/j.compenvurbsys.2008.09.013.

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Leeuwen, Eveline van, Yoshifumi Ishikawa, and Peter Nijkamp. "Microsimulation and interregional input–output modelling as tools for multi-level policy analysis." Environment and Planning C: Government and Policy 34, no. 1 (November 26, 2015): 135–50. http://dx.doi.org/10.1177/0263774x15614720.

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Creedy, John, Nicolas Hérault, and Guyonne Kalb. "Measuring Welfare Changes in Behavioural Microsimulation Modelling: Accounting for the Random Utility Component." Journal of Applied Economics 14, no. 1 (May 2011): 5–34. http://dx.doi.org/10.1016/s1514-0326(11)60003-2.

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Lin, F., S. Thomas, and M. Baldwin. "Microsimulation or Cohort Modelling? A Case Study in Chronic Obstructive Pulmonary Disease (COPD)." Value in Health 16, no. 7 (November 2013): A586. http://dx.doi.org/10.1016/j.jval.2013.08.1617.

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Carey, Colm, Colin C. Caprani, and Bernard Enright. "A pseudo-microsimulation approach for modelling congested traffic loading on long-span bridges." Structure and Infrastructure Engineering 14, no. 2 (June 2017): 163–76. http://dx.doi.org/10.1080/15732479.2017.1330893.

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Lymer, Sharyn, Michelle Cunich, and Stephen Colagiuri. "Simulated economic impacts of Australian Obesity Management Algorithm implementation: microsimulation modelling to 2030." Obesity Research & Clinical Practice 13, no. 3 (May 2019): 248–49. http://dx.doi.org/10.1016/j.orcp.2018.11.036.

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Tey, Li-Sian, Sicong Zhu, Luis Ferreira, and Guy Wallis. "Microsimulation modelling of driver behaviour towards alternative warning devices at railway level crossings." Accident Analysis & Prevention 71 (October 2014): 177–82. http://dx.doi.org/10.1016/j.aap.2014.05.014.

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Dekkers, Gijs. "New Frontiers in Microsimulation Modelling edited by Asghar Zaidi, Ann Harding, Paul Williamson." International Statistical Review 78, no. 3 (December 2010): 481–82. http://dx.doi.org/10.1111/j.1751-5823.2010.00122_34.x.

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Hynes, Stephen, Niall Farrelly, Eithne Murphy, and Cathal O'Donoghue. "Modelling habitat conservation and participation in agri-environmental schemes: A spatial microsimulation approach." Ecological Economics 66, no. 2-3 (June 2008): 258–69. http://dx.doi.org/10.1016/j.ecolecon.2008.02.006.

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Watanabe, Leandro, Jacob Barhak, and Chris Myers. "Toward reproducible disease models using the Systems Biology Markup Language." SIMULATION 95, no. 10 (September 6, 2018): 895–930. http://dx.doi.org/10.1177/0037549718793214.

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Abstract:

Disease modelers have been modeling progression of diseases for several decades using such tools as Markov models or microsimulation. However, they need to address a serious challenge; many models they create are not reproducible. Moreover, there is no proper practice that ensures reproducible models, since modelers rely on loose guidelines that change periodically, rather than well-defined machine-readable standards. The Systems Biology Markup Language (SBML) is one such standard that allows exchange of models between different software tools. Recently, the SBML Arrays package has been developed, which extends the standard to allow handling simulation of populations. This paper demonstrates through several abstract examples how microsimulation disease models can be encoded using the SBML Arrays package, enabling reproducible disease modeling.

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Jung, Yoon-Sun, Young-Eun Kim, Dun-Sol Go, and Seok-Jun Yoon. "Projecting the prevalence of obesity in South Korea through 2040: a microsimulation modelling approach." BMJ Open 10, no. 12 (December 2020): e037629. http://dx.doi.org/10.1136/bmjopen-2020-037629.

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ObjectiveTo project the prevalence of obesity in 2040 among individuals 19 years and older in South Korea.Design, setting, and participantsUsing the ‘Population Health Model-body mass index’ (BMI) microsimulation model, the prevalence of obesity in Korean adults 19 years and older was projected until 2040. The model integrated individual survey data from the Korea Health Panel Survey of 2011 and 2012, population statistics based on resident registration, population projections and complete life tables categorised by sex and age. Birth rate, life expectancy and international migration were based on a medium growth scenario. The base population of Korean adults in 2012, devised through data aggregation, was 39 842 730. The prediction equations were formulated using BMI as the dependent variable; the individual’s sex, age, smoking status, physical activity and preceding year’s BMI were used as predictive factors.Outcome measureBMI categorised by sex.ResultsThe median BMI for Korean adults in 2040 was expected to be 23.55 kg/m2 (23.97 and 23.17 kg/m2 for men and women, respectively). According to the Korean BMI classification, 70.05% of all adults were expected to be ‘preobese’ (ie, have BMIs 23–24.9 kg/m2) by 2040 (81.23% of men and 59.07% of women) and 24.88% to be ‘normal’.ConclusionsWe explored the possibility of applying and expanding on the concept of microsimulation in the field of healthcare by combining data sources available in Korea and found that more than half of the adults in this study population will be preobese, and the proportions of ‘obesity’ and ‘normal’ will decrease compared with those in 2012. The results of our study will aid in devising healthy strategies and spreading public awareness for preventing this condition.

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