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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 (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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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 (April 22, 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 (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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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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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 (February 5, 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 jointly on the levels of both capital income and labor income.

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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 (October 1, 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 (January 2004): 71–81. http://dx.doi.org/10.3141/1876-08.

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9

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 (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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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 (July 2, 2015): 67–76. http://dx.doi.org/10.13167/2015.10.8.

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

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18

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 (April 28, 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 forecast the effects on safety and mobility of adverse weather conditions; however, there is limited knowledge on how to calibrate the model to reflect different weather conditions. This paper contributes a methodology for calibrating car-following behavior required for successful development of microsimulation models. This research was completed using SHRP2 Naturalistic Driving Study (NDS) data to capture realistic driving behavior in a variety of weather conditions. This study has two primary objectives. First, calibrate the Wiedemann 1999 car-following model for a subset of NDS trips, cluster trips with similar weather conditions, and identify an optimal parameter set to represent that condition. Second, apply the optimal model parameters in a realistic microsimulation network to assess the predicted traffic flow in each weather condition. Findings support the hypothesis that the calibration of driving models for use in microsimulation results in more realistic estimations of traffic flow. Moreover, this research illustrates that the use of high resolution trajectory-level data can successfully capture weather-dependent driving behaviors.

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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 (June 17, 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 using both machine-learning and statistical approaches. A decision-tree model and an ordered probit model are estimated and compared to better comprehend the supplier evaluation process. The results indicate that several factors such as the type of the business, commodity type, number of orders, and the value of orders are significant factors. In addition, the decision-tree model is reliable in forecasting the consumer’s behavior.

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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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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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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 (May 26, 2014): 142–51. http://dx.doi.org/10.1179/1942787514y.0000000021.

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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 (June 2014): 1337–47. http://dx.doi.org/10.1109/tits.2014.2304394.

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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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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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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 (May 24, 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 approach to solving the MILP problem, using the “branch and cut” algorithm, which demonstrates superiority in computation time and production of quality solutions. The optimization model determines 135 marshal points and 12 transit routes to evacuate approximately 8,400 transit-dependent individuals. Transit demand and marshal point locations are found to be concentrated at the core of the peninsula. The microsimulation modeling takes a dynamic traffic assignment-based approach. The simulation model predicts that it takes 22 h to evacuate all auto users but just 7 h for the transit-dependent population. The study reveals that the transit system has excess capacity to assist evacuees who switch from auto and other modes. Local traffic congestion prolongs the evacuation of a few densely-populated zones in the downtown core of the peninsula. The findings of this research help policy-makers understand the impacts of marshal point locations and transit route choice decisions on multimodal evacuation performance, and provide insights into emergency planning of multimodal evacuations under "mode switch" and transit-based evacuation scenarios.

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

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McClelland, Robert, Surachai Khitatrakun, and Chenxi Lu. "Estimating Confidence Intervals in a Tax Microsimulation Model." International Journal of Microsimulation 13, no. 2 (August 31, 2020): 2–20. http://dx.doi.org/10.34196/ijm.00216.

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Cheng, Chung-Tang. "Guy H. Orcutt’s Engineering Microsimulation to Reengineer Society." History of Political Economy 52, S1 (December 1, 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 the University of Wisconsin, his ambitious microsimulation finally succeeded at the Urban Institute, constituted as the Dynamic Simulation of Income Model. Since the late-1970s, microsimulation have been used to understand the economic consequences of welfare and redistributive policies. As a pretrained electrical engineer and physicist, Orcutt viewed the socioeconomic system as an electrical-mechanical network. Microsimulation was an engine designed for not only scrutinizing the system but reengineering the society.

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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 (March 31, 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 vehicle motion, and consequently, minimal computational complexity is a strong requirement. However, traffic-related phenomena, such as the capacity drop are influenced by the free-flow acceleration regime. Furthermore, the acceleration pattern of a vehicle plays an essential role in the estimation of the energy required during its motion, and therefore in the fuel consumption and the CO2 emissions. The present work proposes a lightweight microsimulation free-flow acceleration model (MFC) that is able to capture the vehicle acceleration dynamics accurately and consistently, it provides a link between the model and the driver and can be easily implemented and tested without raising the computational complexity. The proposed model is calibrated, validated, and compared with known car-following models on road data on a fixed route inside the Joint Research Centre of the European Commission. Finally, the MFC is assessed based on 0–100 km/h acceleration specifications of vehicles available in the market. The results prove the robustness and flexibility of the model.

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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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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 (April 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 once-only sigmoidoscopy screening to no-screening. To estimate the natural growth of adenomas to CRC, we used the following microsimulation models: (i) the Microsimulation Screening Analysis; (ii) the CRC Simulated Population model for Incidence and Natural history; (iii) the Simulation Model of Colorectal Cancer; (iv) a model derived by the German Cancer Research Center. We defined overdiagnosed cancers as the difference between the observed number of CRCs in the no-screening arm and the expected number of cancers in screening arm (sum of observed and prevented by adenoma removal). The amount of overdiagnosis is defined as the number of overdiagnosed cancers over the number of cancers observed in the no-screening arm.ResultsOverdiagnosis estimates were highly dependent on model assumptions. For FOBT screening with 2354 cancers observed in control arm, four out of five models predicted overdiagnosis, range 2.0% (2400 cancers expected in screening) to 7.6% (2533 cancers expected in screening). For sigmoidoscopy screening with 452 cancers observed in control arm, all models predicted overdiagnosis, range 25.2% (566 cancers expected in screening) to 128.1% (1031 cancers expected in screening).ConclusionsThe amount of overdiagnosis estimated based on the microsimulation models varied substantially. Microsimulation models may not give reliable estimates of the preventive effect of adenoma removal, and should be used with caution to inform guidelines.

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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 (May 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]) to simulate individual life histories. We analyze the effect of parametric uncertainty on overdiagnosis with probabilistic sensitivity analyses (ProbSAs). To minimize the number of MISCAN runs needed for ProbSAs, we emulate MISCAN, using data pairs of parameter values and outcomes to fit a Gaussian process regression model. We evaluate to what extent the emulator accurately reproduces MISCAN by computing its prediction error. Results. Using an emulator instead of MISCAN, we may reduce the computation time necessary to run a ProbSA by more than 85%. The average relative prediction error of the emulator for overdiagnosis equaled 1.7%. We predicted that 42% of screen-detected men are overdiagnosed, with an associated empirical confidence interval between 38% and 48%. Sensitivity analyses show that the accuracy of the emulator is sensitive to which model parameters are included in the training runs. Conclusions. For a computationally expensive simulation model with a large number of parameters, we show it is possible to conduct a ProbSA, within a reasonable computation time, by using a Gaussian process regression emulator instead of the original simulation model.

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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 (May 8, 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 the natural history of lung cancer and predicts individual trajectories accounting for age, sex, and smoking habits. We apply both methods to calibrate MILC to observed lung cancer incidence rates from the Surveillance, Epidemiology and End Results (SEER) database. We compare the results from the 2 methods in terms of the resulting parameter distributions, model predictions, and efficiency. Although the empirical method proves more practical, producing similar results with smaller computational effort, the Bayesian method resulted in a calibrated model that produced more accurate outputs for rare events and is based on a well-defined theoretical framework for the evaluation and interpretation of the calibration outcomes. A combination of the 2 approaches is an alternative worth considering for calibrating complex predictive models, such as microsimulation models.

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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 (January 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 the San Francisco model, a microsimulation model system used in actual planning applications since 2000. The system contains models of vehicle availability, day pattern choice, tour time-of-day choice, destination choice, and mode choice. To investigate the variability of the forecasts of this system due to random simulation error, the model system was run 100 times, each time changing only the sequence of random numbers used to simulate individual choices from the logit model probabilities. The extent of random variability in the model results is reported as a function of two factors: ( a) the type of model (vehicle availability, tour generation, destination choice, or mode choice); and ( b) the level of geographic detail—transit at the analysis zone level, neighborhood level, or countywide level. For each combination of these factors, it is shown graphically how quickly the mean values of key output variables converge toward a stable value as the number of simulation runs increases.

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Noei, Shirin, Mohammadreza Parvizimosaed, and Mohammadreza Noei. "Longitudinal Control for Connected and Automated Vehicles in Contested Environments." Electronics 10, no. 16 (August 18, 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 simulation is required to reduce operating costs and achieve an acceptable risk level before testing cooperative automated driving systems in laboratory environments, test tracks, or public roads. Cooperative automated driving systems can be simulated using a vehicle dynamics simulation tool (e.g., CarMaker and CarSim) or a traffic microsimulation tool (e.g., Vissim and Aimsun). Vehicle dynamics simulation tools are mainly used for verification and validation purposes on a small scale, while traffic microsimulation tools are mainly used for verification purposes on a large scale. Vehicle dynamics simulation tools can simulate longitudinal, lateral, and vertical dynamics for only a few vehicles in each scenario (e.g., up to ten vehicles in CarMaker and up to twenty vehicles in CarSim). Conventional traffic microsimulation tools can simulate vehicle-following, lane-changing, and gap-acceptance behaviors for many vehicles in each scenario without simulating vehicle powertrain. Vehicle dynamics simulation tools are more compute-intensive but more accurate than traffic microsimulation tools. Due to software architecture or computing power limitations, simplifying assumptions underlying convectional traffic microsimulation tools may have been a necessary compromise long ago. There is, therefore, a need for a simulation tool to optimize computational complexity and accuracy to simulate many vehicles in each scenario with reasonable accuracy. This research proposes a traffic microsimulation tool that employs a simplified vehicle powertrain model and a model-based fault detection method to simulate many vehicles with reasonable accuracy at each simulation time step under noise and unknown inputs. Our traffic microsimulation tool considers driver characteristics, vehicle model, grade, pavement conditions, operating mode, vehicle-to-vehicle communication vulnerabilities, and traffic conditions to estimate longitudinal control variables with reasonable accuracy at each simulation time step for many conventional vehicles, vehicles dedicated to automated driving systems, and vehicles equipped with cooperative automated driving systems. Proposed vehicle-following model and longitudinal control functions are verified for fourteen vehicle models, operating in manual, automated, and cooperative automated modes over two driving schedules under three malicious fault magnitudes on transmitted accelerations.

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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 and modeled by ELM neural networks, on basis of which an overall estimation model is proposed. At last, a set of observed data from Beijing subway is used to illustrate the proposed method and verify its overall performance.

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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 (January 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 on the aggregate results is negligible.

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Burgard, Jan Pablo, Hanna Dieckmann, Joscha Krause, Hariolf Merkle, Ralf Münnich, Kristina M. Neufang, and Simon Schmaus. "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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Rogers, Susan M., James Rineer, Matthew D. Scruggs, William D. Wheaton, Phillip C. Cooley, Douglas J. Roberts, and Diane K. Wagener. "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 (October 2009): A393. http://dx.doi.org/10.1016/s1098-3015(10)74937-8.

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Dragomir, A., JE Tarride, R. Joober, JF Angers, G. Rouleau, P. Drapeau, and S. Perreault. "MC6 SCHIZOPHRENIA MODELING: MARKOV MODEL WITH MONTE-CARLO MICROSIMULATION." Value in Health 12, no. 7 (October 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 (January 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 (March 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 (January 2010): 53–62. http://dx.doi.org/10.3328/tl.2010.02.01.53-62.

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