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Journal articles on the topic 'Microsimulation model'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

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 (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

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2

O’Donoghue, Cathal. "Simulating migration microsimulation model." International Journal of Microsimulation 3, no. 2 (2009): 65–79. http://dx.doi.org/10.34196/ijm.00039.

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Šurdonja, Sanja, Daniela Nežić, and Aleksandra Deluka-Tibljaš. "The Roundabout Capacity Estimate Microsimulation Model." Journal of Maritime & Transportation Science 49-50, no. 1 (2015): 143–65. http://dx.doi.org/10.18048/2015.49-50.143.

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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 (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, t

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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 (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 paramete

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DeBacker, Jason, Richard W. Evans, and Kerk L. Phillips. "Integrating Microsimulation Models of Tax Policy into a DGE Macroeconomic Model." Public Finance Review 47, no. 2 (2019): 207–75. http://dx.doi.org/10.1177/1091142118816744.

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This article proposes a method for integrating individual effective tax rates and marginal tax rates computed from a microsimulation (partial equilibrium) model of tax policy with a dynamic general equilibrium model of tax policy that can provide macroeconomic analysis or dynamic scores of tax reforms. Our approach captures the rich heterogeneity, realistic demographics, and tax-code detail of the microsimulation model and allows this detail to inform a general equilibrium model with a relatively high degree of heterogeneity. In addition, we propose a functional form in which tax rates depend

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Klazar, Stanislav, and Martin Zelený. "Microsimulation Model for Distributional Analysis of Consumption Taxes." Český finanční a účetní časopis 2008, no. 3 (2008): 56–68. http://dx.doi.org/10.18267/j.cfuc.280.

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Gomes, Gabriel, Adolf May, and Roberto Horowitz. "Congested Freeway Microsimulation Model Using VISSIM." Transportation Research Record: Journal of the Transportation Research Board 1876, no. 1 (2004): 71–81. http://dx.doi.org/10.3141/1876-08.

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Pasra, M., S. Hamid, A. Faisal, and H. Yatmar. "Model Microsimulation Roundabout Utilities in Makassar." IOP Conference Series: Materials Science and Engineering 875 (July 23, 2020): 012024. http://dx.doi.org/10.1088/1757-899x/875/1/012024.

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10

Wolf, Douglas A. "The Role of Microsimulation in Longitudinal Data Analysis." Canadian Studies in Population 28, no. 2 (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

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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 u

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Ištoka Otković, Irena, Damir Varevac, and Matjaž Šraml. "ANALYSIS OF NEURAL NETWORK RESPONSES IN CALIBRATION OF MICROSIMULATION TRAFFIC MODEL." Elektronički časopis građevinskog fakulteta Osijek 6, no. 10 (2015): 67–76. http://dx.doi.org/10.13167/2015.10.8.

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13

Ancora, Vincenzo, Claudio Nelli, and Marco Petrelli. "A Microsimulation Model for BRT Systems Analysis." Procedia - Social and Behavioral Sciences 54 (October 2012): 1250–59. http://dx.doi.org/10.1016/j.sbspro.2012.09.839.

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14

Harmon, Adam, and Eric J. Miller. "Overview of a labour market microsimulation model." Procedia Computer Science 130 (2018): 172–79. http://dx.doi.org/10.1016/j.procs.2018.04.027.

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15

Andreassen, Leif, Dennis Fredriksen, Hege M. Gjefsen, Elin Halvorsen, and Nils M. Stølen. "The dynamic cross-sectional microsimulation model MOSART." International Journal of Microsimulation 13, no. 1 (2020): 92–113. http://dx.doi.org/10.34196/ijm.00214.

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Brownstone, David, Peter Englund, and Mats Persson. "A microsimulation model of Swedish housing demand." Journal of Urban Economics 23, no. 2 (1988): 179–98. http://dx.doi.org/10.1016/0094-1190(88)90013-7.

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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 (2008): 440–53. http://dx.doi.org/10.1016/j.compenvurbsys.2008.09.013.

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Hammit, Britton E., Rachel James, Mohamed Ahmed, and Rhonda Young. "Toward the Development of Weather-Dependent Microsimulation Models." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 7 (2019): 143–56. http://dx.doi.org/10.1177/0361198119844743.

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Adverse weather conditions severely affect transportation networks. Decades of research have been dedicated to analyzing these impacts and developing countermeasures to reduce their negative effects on travelers and infrastructure. Recent developments in technology have enabled the introduction of intelligent transportation system applications used for network planning, safety assessments, countermeasure evaluation, and roadway operations. One such application is microsimulation modeling, which is a powerful tool used to emulate traffic flow. Agencies are interested in using microsimulation to

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Mahmoudifard, Seyed Mehdi, Ramin Shabanpour, Nima Golshani, Kiana Mohammadian, and Abolfazl Mohammadian. "Supplier Evaluation Model in Freight Activity Microsimulation Estimator." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 9 (2018): 70–80. http://dx.doi.org/10.1177/0361198118777084.

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The supplier selection process is one of the main components of the Freight Activity Microsimulation Estimator (FAME), which is a disaggregated and comprehensive framework that simulates the freight movements for all industries and all commodities in the U.S. However, the supplier selection and supplier evaluation models in the FAME face computational issues. Using the result of a nationwide establishment survey, this study analyzes the supplier selection problem by evaluating the potential suppliers. The buyer’s behavior on selecting the distance range in which the trade forms is analyzed usi

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Sutherland, Holly, and Francesco Figari. "EUROMOD: the European Union tax-benefit microsimulation model." International Journal of Microsimulation 6, no. 1 (2012): 4–26. http://dx.doi.org/10.34196/ijm.00075.

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21

Astarita, Vittorio, Vincenzo Giofré, Giuseppe Guido, and Alessandro Vitale. "Investigating road safety issues through a microsimulation model." Procedia - Social and Behavioral Sciences 20 (2011): 226–35. http://dx.doi.org/10.1016/j.sbspro.2011.08.028.

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22

Tsavalista Burhani, Jzolanda, Febri Zukhruf, and Russ Bona Frazila. "Port performance evaluation tool based on microsimulation model." MATEC Web of Conferences 101 (2017): 05011. http://dx.doi.org/10.1051/matecconf/201710105011.

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23

Samimi, Amir, Abolfazl Mohammadian, Kazuya Kawamura, and Zahra Pourabdollahi. "An activity-based freight mode choice microsimulation model." Transportation Letters 6, no. 3 (2014): 142–51. http://dx.doi.org/10.1179/1942787514y.0000000021.

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24

Boukouvalas, Alexis, Pete Sykes, Dan Cornford, and Hugo Maruri-Aguilar. "Bayesian Precalibration of a Large Stochastic Microsimulation Model." IEEE Transactions on Intelligent Transportation Systems 15, no. 3 (2014): 1337–47. http://dx.doi.org/10.1109/tits.2014.2304394.

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25

Gomez-Marin, Cristian Giovanny, Conrado Augusto Serna-Uran, Martin Dario Arango-Serna, and Antonio Comi. "Microsimulation-Based Collaboration Model for Urban Freight Transport." IEEE Access 8 (2020): 182853–67. http://dx.doi.org/10.1109/access.2020.3028564.

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26

Ballas, Dimitris, Graham Philip Clarke, and Emily Wiemers. "Building a dynamic spatial microsimulation model for Ireland." Population, Space and Place 11, no. 3 (2005): 157–72. http://dx.doi.org/10.1002/psp.359.

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27

Alam, Jahedul, Muhammad Ahsanul Habib, and Uday Venkatadri. "Development of a Multimodal Microsimulation-Based Evacuation Model." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 10 (2019): 477–88. http://dx.doi.org/10.1177/0361198119848410.

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This study presents a multimodal evacuation microsimulation modeling framework. The paper first determines optimum marshal point locations and transit routes, then examines network conditions through traffic microsimulation of a mass evacuation of the Halifax Peninsula, Canada. The proposed optimization modeling approach identifies marshal point locations based on transit demand obtained from a Halifax Regional Transport network model. A mixed integer linear programming (MILP) technique is used to formulate the marshal point location and transit route choice problem. The study proposes a novel

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28

Alkubaisi, Mahdi. "Development of Freeway Weaving Areas Microsimulation Model (FWASIM)." Civil Engineering and Architecture 8, no. 5 (2020): 1006–18. http://dx.doi.org/10.13189/cea.2020.080527.

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29

McClelland, Robert, Surachai Khitatrakun, and Chenxi Lu. "Estimating Confidence Intervals in a Tax Microsimulation Model." International Journal of Microsimulation 13, no. 2 (2020): 2–20. http://dx.doi.org/10.34196/ijm.00216.

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30

Cheng, Chung-Tang. "Guy H. Orcutt’s Engineering Microsimulation to Reengineer Society." History of Political Economy 52, S1 (2020): 191–217. http://dx.doi.org/10.1215/00182702-8718000.

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This essay examines how microanalytic simulation (microsimulation) proposed by Guy H. Orcutt emerged as a tool in evaluating public policies. Inspired by the econometric work of Jan Tinbergen, young Orcutt harbored a “Tinbergen dream” in building a model covering the national economy. Early in his career, he had developed an analogue electrical-mechanical “regression analyzer” to calculate statistical estimates. During the mid-1950s, he shifted to micro-level data and the Monte Carlo method, and then created the first microanalytic simulation of demographic variables. After a failed trial at t

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31

Makridis, Michail, Georgios Fontaras, Biagio Ciuffo, and Konstantinos Mattas. "MFC Free-Flow Model: Introducing Vehicle Dynamics in Microsimulation." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (2019): 762–77. http://dx.doi.org/10.1177/0361198119838515.

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Free-flow movement of vehicles in microsimulation software is usually defined by a set of equations with no explicit link to the instantaneous dynamics of the vehicles. In some cases, the car and the driver are modeled in a deterministic way, producing a driving behavior, which does not resemble real measurements of car dynamics or driving style. Depending on the research topic, the interest in microsimulation is to capture traffic dynamics phenomena, such as shockwave propagation or hysterisis. Existing car-following models are designed to simulate more the traffic evolution, rather than the

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32

Li, Jinjing, and Cathal O’Donoghue. "A survey of dynamic microsimulation models: uses, model structure and methodology." International Journal of Microsimulation 6, no. 2 (2012): 3–55. http://dx.doi.org/10.34196/ijm.00082.

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33

Wieszczy, Paulina, Michal F. Kaminski, Magnus Løberg, Marek Bugajski, Michael Bretthauer, and Mette Kalager. "Estimation of overdiagnosis in colorectal cancer screening with sigmoidoscopy and faecal occult blood testing: comparison of simulation models." BMJ Open 11, no. 4 (2021): e042158. http://dx.doi.org/10.1136/bmjopen-2020-042158.

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ObjectiveTo estimate overdiagnosis of colorectal cancer (CRC) for screening with sigmoidoscopy and faecal occult blood testing (FOBT).DesignSimulation study using data from randomised trials.SettingPrimary screening, UK, NorwayParticipants152 850 individuals from the Nottingham trial and 98 678 individuals from the Norwegian Colorectal Cancer Prevention (NORCCAP) trial.InterventionCRC screening.Outcome measureWe estimated overdiagnosis using long-term data from two randomised trials: the Nottingham trial comparing FOBT screening every other year to no-screening, and the NORCCAP trial comparing

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de Carvalho, Tiago M., Eveline A. M. Heijnsdijk, Luc Coffeng, and Harry J. de Koning. "Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators." Medical Decision Making 39, no. 4 (2019): 405–13. http://dx.doi.org/10.1177/0272989x19837631.

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Background. Microsimulation models have been extensively used in the field of cancer modeling. However, there is substantial uncertainty regarding estimates from these models, for example, overdiagnosis in prostate cancer. This is usually not thoroughly examined due to the high computational effort required. Objective. To quantify uncertainty in model outcomes due to uncertainty in model parameters, using a computationally efficient emulator (Gaussian process regression) instead of the model. Methods. We use a microsimulation model of prostate cancer (microsimulation screening analysis [MISCAN

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Chrysanthopoulou, Stavroula A., Carolyn M. Rutter, and Constantine A. Gatsonis. "Bayesian versus Empirical Calibration of Microsimulation Models: A Comparative Analysis." Medical Decision Making 41, no. 6 (2021): 714–26. http://dx.doi.org/10.1177/0272989x211009161.

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Calibration of a microsimulation model (MSM) is a challenging but crucial step for the development of a valid model. Numerous calibration methods for MSMs have been suggested in the literature, most of which are usually adjusted to the specific needs of the model and based on subjective criteria for the selection of optimal parameter values. This article compares 2 general approaches for calibrating MSMs used in medical decision making, a Bayesian and an empirical approach. We use as a tool the MIcrosimulation Lung Cancer (MILC) model, a streamlined, continuous-time, dynamic MSM that describes

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36

Castiglione, Joe, Joel Freedman, and Mark Bradley. "Systematic Investigation of Variability due to Random Simulation Error in an Activity-Based Microsimulation Forecasting Model." Transportation Research Record: Journal of the Transportation Research Board 1831, no. 1 (2003): 76–88. http://dx.doi.org/10.3141/1831-09.

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A key difference between stochastic microsimulation models and more traditional forms of travel demand forecasting models is that micro-simulation-based forecasts change each time the sequence of random numbers used to simulate choices is varied. To address practitioners’ concerns about this variation, a common approach is to run the microsimulation model several times and average the results. The question then becomes: What is the minimum number of runs required to reach a true average state for a given set of model results? This issue was investigated by means of a systematic experiment with

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37

Noei, Shirin, Mohammadreza Parvizimosaed, and Mohammadreza Noei. "Longitudinal Control for Connected and Automated Vehicles in Contested Environments." Electronics 10, no. 16 (2021): 1994. http://dx.doi.org/10.3390/electronics10161994.

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The Society of Automotive Engineers (SAE) defines six levels of driving automation, ranging from Level 0 to Level 5. Automated driving systems perform entire dynamic driving tasks for Levels 3–5 automated vehicles. Delegating dynamic driving tasks from driver to automated driving systems can eliminate crashes attributed to driver errors. Sharing status, sharing intent, seeking agreement, or sharing prescriptive information between road users and vehicles dedicated to automated driving systems can further enhance dynamic driving task performance, safety, and traffic operations. Extensive simula

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38

Chu, Wen-jun, Xing-chen Zhang, Jun-hua Chen, and Bin Xu. "An ELM-Based Approach for Estimating Train Dwell Time in Urban Rail Traffic." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/473432.

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Dwell time estimation plays an important role in the operation of urban rail system. On this specific problem, a range of models based on either polynomial regression or microsimulation have been proposed. However, the generalization performance of polynomial regression models is limited and the accuracy of existing microsimulation models is unstable. In this paper, a new dwell time estimation model based on extreme learning machine (ELM) is proposed. The underlying factors that may affect urban rail dwell time are analyzed first. Then, the relationships among different factors are extracted a

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39

Veldhuisen, Jan, Harry Timmermans, and Loek Kapoen. "Microsimulation Model of Activity-Travel Patterns and Traffic Flows: Specification, Validation Tests, and Monte Carlo Error." Transportation Research Record: Journal of the Transportation Research Board 1706, no. 1 (2000): 126–35. http://dx.doi.org/10.3141/1706-15.

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Discussed are results pertaining to the application of the Ramblas microsimulation model to predict activity-travel patterns and traffic flows. The validity of the model, which is deliberately based on nationally available time use data, is tested using different national, provincial, and regional data sets. In addition, an analysis of Monte Carlo error is performed. The results of the analyses indicate that the microsimulation model is capable of successfully predicting regional aggregate activity patterns on the basis of a simple set of principles and that the influence of Monte Carlo error

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40

Burgard, Jan Pablo, Hanna Dieckmann, Joscha Krause, et al. "A generic business process model for conducting microsimulation studies." Statistics in Transition New Series 21, no. 4 (2020): 191–211. http://dx.doi.org/10.21307/stattrans-2020-038.

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41

Rogers, Susan M., James Rineer, Matthew D. Scruggs, et al. "A Geospatial Dynamic Microsimulation Model for Household Population Projections." International Journal of Microsimulation 7, no. 2 (2013): 119–46. http://dx.doi.org/10.34196/ijm.00102.

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Michelangeli, Valentina, and Mario Pietrunti. "A Microsimulation Model to evaluate Italian Households’ Financial Vulnerability." International Journal of Microsimulation 7, no. 3 (2013): 53–79. http://dx.doi.org/10.34196/ijm.00107.

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Loughrey, Author Jason, Fiona Thorne, and Thia Hennessy. "A Microsimulation Model for Risk in Irish Tillage Farming." International Journal of Microsimulation 9, no. 2 (2015): 41–76. http://dx.doi.org/10.34196/ijm.00135.

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Ryan, Mary, and Cathal O’Donoghue. "Developing a microsimulation model for farm forestry planting decisions." International Journal of Microsimulation 12, no. 2 (2018): 18–36. http://dx.doi.org/10.34196/ijm.00199.

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Gómez-Marín, Cristian Giovanny, Martín Darío Arango-Serna, and Conrado Augusto Serna-Urán. "Agent-based microsimulation conceptual model for urban freight distribution." Transportation Research Procedia 33 (2018): 155–62. http://dx.doi.org/10.1016/j.trpro.2018.10.088.

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Dragomir, A., JF Angers, JE Tarride, G. Rouleau, P. Drapeau, and S. Perreault. "PMC35 SCHIZOPHRENIA MODELING: MARKOV MODEL WITH MONTE-CARLO MICROSIMULATION." Value in Health 12, no. 7 (2009): A393. http://dx.doi.org/10.1016/s1098-3015(10)74937-8.

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Dragomir, A., JE Tarride, R. Joober, et al. "MC6 SCHIZOPHRENIA MODELING: MARKOV MODEL WITH MONTE-CARLO MICROSIMULATION." Value in Health 12, no. 7 (2009): A488—A489. http://dx.doi.org/10.1016/s1098-3015(10)75310-9.

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Castiglione, Joe, Rachel Hiatt, Tilly Chang, and Billy Charlton. "Application of Travel Demand Microsimulation Model for Equity Analysis." Transportation Research Record: Journal of the Transportation Research Board 1977, no. 1 (2006): 35–42. http://dx.doi.org/10.1177/0361198106197700105.

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Al-Obaedi, Jalal, and Saad Yousif. "Microsimulation Model for Motorway Merges With Ramp-Metering Controls." IEEE Transactions on Intelligent Transportation Systems 13, no. 1 (2012): 296–306. http://dx.doi.org/10.1109/tits.2011.2169792.

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Samimi, Amir, Abolfazl Mohammadian, and Kazuya Kawamura. "A behavioral freight movement microsimulation model: method and data." Transportation Letters 2, no. 1 (2010): 53–62. http://dx.doi.org/10.3328/tl.2010.02.01.53-62.

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