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Journal articles on the topic 'Microsimulation'

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Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 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

Kelly, Terence, and Kai Nagel. "Relaxation Criteria for Iterated Traffic Simulations." International Journal of Modern Physics C 09, no. 01 (February 1998): 113–32. http://dx.doi.org/10.1142/s0129183198000108.

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Iterative transportation microsimulations adjust traveler route plans by iterating between a microsimulation and a route planner. At each iteration, the route planner adjusts individuals' route choices based on the preceding microsimulations. Empirically, this process yields good results, but it is usually unclear when to stop the iterative process when modeling real-world traffic. This paper investigates several criteria to judge relaxation of the iterative process, emphasizing criteria related to traveler decision-making.

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3

Walker, Joan L. "Making Household Microsimulation of Travel and Activities Accessible to Planners." Transportation Research Record: Journal of the Transportation Research Board 1931, no. 1 (January 2005): 38–48. http://dx.doi.org/10.1177/0361198105193100105.

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There is a large gap between the aggregate, trip-based models used by transportation planning agencies and the activity-based, microsimulation methods espoused by those at the forefront of research. The modeling environment presented here is intended to bridge this gap by providing a palatable way for planning agencies to move toward advanced methods. Three components to bridging the gap are emphasized: an incremental approach, a demonstration of clear gains, and a provision of an environment that eases initial implementation and allows for expansion. The modeling environment (called STEP2) is a household microsimulator, developed in TransCAD, that can be used to implement a four-step model as well as models with longer-term behavior and trip chaining. An implementation for southern Nevada is described, and comparisons are made with the region's aggregate four-step model. The models perform similarly in numerous ways. A key advantage to the microsimulator is that it provides impacts by socioeconomic group (essential for equity analysis) and individual trip movements (for use in a vehicle microsimulator). A sensitivity analysis indicates that the microsimulation model has less inelastic cross elasticity of transit demand with respect to auto travel times than the aggregate model (aggregation error). The trade-off is that microsimulators have simulation error; results are presented regarding the severity of this error. This work shows that a shift to microsimulation does not necessarily require substantial investment to achieve many of the benefits. One of the greatest advantages is a flexible environment that can expand to include additional sensitivity to demographics and transportation policy variables.

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Farooq, Bilal, Eric J. Miller, Franco Chingcuanco, and Martin Giroux-Cook. "Microsimulation framework for urban price-taker markets." Journal of Transport and Land Use 6, no. 1 (April 10, 2013): 41–51. http://dx.doi.org/10.5198/jtlu.v6i1.325.

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In the context of integrated transportation and other urban engineering infrastructure systems, there are many examples of markets, where consumers exhibit price-taking behavior. While this behavior is ubiquitous, the underlying mechanism can be captured in a single framework. Here, we present a microsimulation framework of a price-taker market that recognizes this generality and develop efficient algorithms for the associated market-clearing problem. By abstracting the problem as a specific graph theoretic problem (i.e., maximum weighted bipartite graph), we are first able to exploit algorithms that are developed in graph theory. We then explore their appropriateness in terms of large-scale integrated urban microsimulations. Based on this, we further develop a generic and efficient clearing algorithm that takes advantage of the features specific to urban price-taker markets. This clearing solution is then used to operationalize two price-taker markets, from two different contexts, within a microsimulation of urban systems. The initial validation of results against the observed data generally shows a close match.

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Schweizer, Joerg, Cristian Poliziani, Federico Rupi, Davide Morgano, and Mattia Magi. "Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data." ISPRS International Journal of Geo-Information 10, no. 3 (March 14, 2021): 165. http://dx.doi.org/10.3390/ijgi10030165.

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

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6

Raju, Narayana, and Haneen Farah. "Evolution of Traffic Microsimulation and Its Use for Modeling Connected and Automated Vehicles." Journal of Advanced Transportation 2021 (September 24, 2021): 1–29. http://dx.doi.org/10.1155/2021/2444363.

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Traffic microsimulation has a functional role in understanding the traffic performance on the road network. This study originated with intent to understand traffic microsimulation and its use in modeling connected and automated vehicles (CAVs). Initially, the paper focuses on understanding the evolution of traffic microsimulation and on examining the various commercial and open-source simulation platforms available and their importance in traffic microsimulation studies. Following this, current autonomous vehicle (AV) microsimulation strategies are reviewed. From the review analysis, it is observed that AVs are modeled in traffic microsimulation with two sets of strategies. In the first set, the inbuilt models are used to replicate the driving behavior of AVs by adapting the models’ parameters. In the second strategy, AV behavior is programmed with the help of externalities (e.g., Application Programming Interface (API)). Studies simulating AVs with inbuilt models used mostly VISSIM compared to other microsimulation platforms. In addition, the studies are heavily focused on AVs’ penetration rate impact on traffic flow characteristics and traffic safety. On the other hand, studies which simulated AVs with externalities focused on the communication aspects for traffic management. Finally, the cosimulation strategies for simulating the CAVs are explored, and the ongoing research attempts are discussed. The present study identifies the limitations of present CAV microsimulation studies and proposes prospects and improvements in modeling AVs in traffic microsimulation.

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7

Martel, Laurent, Éric Caron Malenfant, and Alain Bélanger. "Microsimulation en démographie." Cahiers québécois de démographie 40, no. 2 (2011): 171. http://dx.doi.org/10.7202/1011538ar.

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8

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

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9

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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11

PASTORE Y PIONTTI, ANA, MARCELO FERREIRA DA COSTA GOMES, NICOLE SAMAY, NICOLA PERRA, and ALESSANDRO VESPIGNANI. "The infection tree of global epidemics." Network Science 2, no. 1 (April 2014): 132–37. http://dx.doi.org/10.1017/nws.2014.5.

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The spreading of transmissible infectious diseases is inevitably entangled with the dynamics of human population. Humans are the carrier of the pathogen, and the large-scale travel and commuting patterns that govern the mobility of modern societies are defining how epidemics and pandemics travel across the world. For a long time, the development of quantitative spatially explicit models able to shed light on the global dynamics of pandemic has been limited by the lack of detailed data on human mobility. In the last 10 years, however, these limits have been lifted by the increasing availability of data generated by new information technologies, thus triggering the development of computational (microsimulation) models working at a level of single individuals in spatially extended regions of the world. Microsimulations can provide information at very detailed spatial resolutions and down to the level of single individuals. In addition, computational implementations explicitly account for stochasticity, allowing the study of multiple realizations of epidemics with the same parameters' distribution. While on the one hand these capabilities represent the richness of microsimulation methods, on the other hand they face us with a huge amount of information that requires the use of specific data reduction methods and visual analytics.

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12

Hyun Sub, Kum. "Imputation Techniques in Microsimulation." Korean Journal of Policy Studies 25, no. 3 (December 31, 2010): 69–83. http://dx.doi.org/10.52372/kjps25304.

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Microsimulation has gained attention for its use in analyzing and forecasting the individual impacts of alternative economic and social policy measures. In practice, however, microsimulation cannot be carried out from a single data source, since it requires far more information than any single data source can provide. This paper discusses ways to combine separate data sources when there are no identical key variables, using imputation techniques, to make a large but synthetic data source for microsimulation. A new approach based on propensity score matching is suggested and discussed.

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13

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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14

Decoster, André, Jason Loughrey, Cathal O’Donoghue, and Dirk Verwerft. "Microsimulation of indirect taxes." International Journal of Microsimulation 4, no. 2 (2010): 41–56. http://dx.doi.org/10.34196/ijm.00052.

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15

Duncan, Gordon. "From Microsimulation to Nanosimulation." Transportation Research Record: Journal of the Transportation Research Board 2175, no. 1 (January 2010): 130–37. http://dx.doi.org/10.3141/2175-15.

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16

Kokic, Philip, Ray Chambers, and Steve Beare. "Microsimulation of Business Performance." International Statistical Review / Revue Internationale de Statistique 68, no. 3 (December 2000): 259. http://dx.doi.org/10.2307/1403413.

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Sykes, Pete. "Transport Planning with Microsimulation." Journal of Maps 3, no. 1 (January 2007): 122–34. http://dx.doi.org/10.1080/jom.2007.9710833.

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18

Anderson, Ronald E., and Chantal Hicks. "Highlights of Contemporary Microsimulation." Social Science Computer Review 29, no. 1 (May 10, 2010): 3–8. http://dx.doi.org/10.1177/0894439310370084.

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19

Moekel, Rolf, Klaus Spiekermann, Carsten Schürmann, and Michael Wegener. "Microsimulation of Land Use." International Journal of Urban Sciences 7, no. 1 (April 2003): 14–31. http://dx.doi.org/10.1080/12265934.2003.9693520.

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20

Orcutt, Guy H. "From engineering to microsimulation." Journal of Economic Behavior & Organization 14, no. 1 (September 1990): 5–27. http://dx.doi.org/10.1016/0167-2681(90)90038-f.

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21

Kokic, Philip, Ray Chambers, and Steve Beare. "Microsimulation of Business Performance." International Statistical Review 68, no. 3 (December 2000): 259–75. http://dx.doi.org/10.1111/j.1751-5823.2000.tb00330.x.

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22

Nagel, Kai, and Christopher L. Barrett. "Using Microsimulation Feedback For Trip Adaptation For Realistic Traffic In Dallas." International Journal of Modern Physics C 08, no. 03 (June 1997): 505–25. http://dx.doi.org/10.1142/s0129183197000412.

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This paper presents a day-to-day re-routing relaxation approach for traffic simulations. Starting from an initial planset for the routes, the route- based microsimulation is executed. The result of the microsimulation is fed into a re-router, which re-routes a certain percentage of all trips. This approach makes the traffic patterns in the microsimulation much more reasonable. Further, it is shown that the method described in this paper can lead to strong oscillations in the solutions.

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23

Astarita, Vittorio. "Microsimulation and the Evaluation of Safety Levels in the Presence of Roadside Obstacles." European Transport/Trasporti Europei, no. 77 (May 2020): 1–12. http://dx.doi.org/10.48295/et.2020.77.8.

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Traffic microsimulation has been used for the analysis of road safety. In particular, some studies have confirmed that the reproduction by simulation of user behaviour under different traffic flows and geometry conditions can identify near crashes events that are a good base to estimate real crash risk. According to recent researches it appears that microsimulation tools can evaluate road safety performance and allow engineers to take appropriate countermeasures at specific points of the road network. The results of these approaches have been promising, though, all ongoing research has overlooked one important issue in the estimation of traffic safety levels: single vehicle crashes. According to statistics, collisions with fixed objects result in above 40% of all vehicle fatal crashes. Common used safety indicators are limited in their application since, in fact, they are based on conflicts techniques that do not consider roadside obstacles and barriers. The objective of this paper is to present a specific application of microsimulation software which is able to consider also potential conflicts with roadside objects. A specific microsimulation model add-on has been developed for the estimation of new road safety indicators that considers also potential crashes with roadside objects. First results are very promising and the developed software was applied on Tritone microsimulation package and can be used as an add-on also for other common used microsimulation packages such as Vissim and Aimsun.

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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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Moll, Sara, Griselda López, and Alfredo García. "Analysis of the Influence of Sport Cyclists on Narrow Two-Lane Rural Roads Using Instrumented Bicycles and Microsimulation." Sustainability 13, no. 3 (January 25, 2021): 1235. http://dx.doi.org/10.3390/su13031235.

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It is frequent to see cyclists on Spanish two-lane rural roads, both riding individually and in groups. However, these roads were designed only for motorized vehicles, most of them having a narrow section with a null or impassable shoulder. Currently, drivers and cyclists have to share roads and interact, affecting both safety and traffic operation. The possibility of overtaking offers an improvement in traffic operation, however on narrow roads it can be difficult, meaning a greater invasion of the opposite lane thus creating more dangerous situations and implying a higher overtaking duration. To analyze the phenomenon, field data from instrumented bicycles and naturalistic videos were collected, then some performance measures to characterize safety and traffic operation were obtained. To increase the number of overtaking manoeuvres and performance measures obtained from observations, microsimulation has been used by adapting a model to include cyclists and their interaction with motorized vehicles. The traffic microsimulator was calibrated and validated with field data. The results show that cycle traffic presence decreases motorized vehicle average travel speed and increases percent followers and delays. Microsimulation can be used to study other traffic scenarios and can help road administrations to safely and efficiently integrate cyclists to vehicular traffic on rural roads.

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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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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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Kim, Kyu-Ok, and L. R. Rilett. "Simplex-Based Calibration of Traffic Microsimulation Models with Intelligent Transportation Systems Data." Transportation Research Record: Journal of the Transportation Research Board 1855, no. 1 (January 2003): 80–89. http://dx.doi.org/10.3141/1855-10.

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In recent years, microsimulation has become increasingly important in transportation system modeling. A potential issue is whether these models adequately represent reality and whether enough data exist with which to calibrate these models. There has been rapid deployment of intelligent transportation system (ITS) technologies in most urban areas of North America in the last 10 years. While ITSs are developed primarily for real-time traffic operations, the data are typically archived and available for traffic microsimulation calibration. A methodology, based on the sequential simplex algorithm, that uses ITS data to calibrate microsimulation models is presented. The test bed is a 23-km section of Interstate 10 in Houston, Texas. Two microsimulation models, CORSIM and TRANSIMS, were calibrated for two different demand matrices and three periods (morning peak, evening peak, and off-peak). It was found for the morning peak that the simplex algorithm had better results then either the default values or a simple, manual calibration. As the level of congestion decreased, the effectiveness of the simplex approach also decreased, as compared with standard techniques.

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Peichl, Andreas. "Linking Microsimulation and CGE models." International Journal of Microsimulation 9, no. 1 (2015): 167–74. http://dx.doi.org/10.34196/ijm.00132.

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Lemp, Jason D., Laura B. McWethy, and Kara M. Kockelman. "From Aggregate Methods to Microsimulation." Transportation Research Record: Journal of the Transportation Research Board 1994, no. 1 (January 2007): 80–88. http://dx.doi.org/10.3141/1994-11.

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31

Heard, Daniel, Gelonia Dent, Tracy Schifeling, and David Banks. "Agent-Based Models and Microsimulation." Annual Review of Statistics and Its Application 2, no. 1 (April 10, 2015): 259–72. http://dx.doi.org/10.1146/annurev-statistics-010814-020218.

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Spielauer, Martin. "What is Social Science Microsimulation?" Social Science Computer Review 29, no. 1 (May 18, 2010): 9–20. http://dx.doi.org/10.1177/0894439310370085.

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Rutter, Carolyn M., Diana L. Miglioretti, and James E. Savarino. "Bayesian Calibration of Microsimulation Models." Journal of the American Statistical Association 104, no. 488 (December 2009): 1338–50. http://dx.doi.org/10.1198/jasa.2009.ap07466.

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Astrom, Magnus, and Coomaren P. Vencatasawmy. "Incorporating Artificial Intelligence in Microsimulation." Geografiska Annaler, Series B: Human Geography 83B, no. 2 (January 2001): 53–65. http://dx.doi.org/10.1111/1468-0467.00094.

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Åström, Magnus, and Coomaren P. Vencatasawmy. "Incorporating artificial intelligence in microsimulation." Geografiska Annaler: Series B, Human Geography 83, no. 2 (August 2001): 53–65. http://dx.doi.org/10.1111/j.0435-3684.2001.00094.x.

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Burden, Sandy, and David Steel. "Constraint Choice for Spatial Microsimulation." Population, Space and Place 22, no. 6 (June 1, 2015): 568–83. http://dx.doi.org/10.1002/psp.1942.

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Pudney, Stephen, and Holly Sutherland. "How reliable are microsimulation results?" Journal of Public Economics 53, no. 3 (March 1994): 327–65. http://dx.doi.org/10.1016/0047-2727(94)90030-2.

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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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Rahimi, Amir Masoud, Maxim A. Dulebenets, and Arash Mazaheri. "Evaluation of Microsimulation Models for Roadway Segments with Different Functional Classifications in Northern Iran." Infrastructures 6, no. 3 (March 15, 2021): 46. http://dx.doi.org/10.3390/infrastructures6030046.

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Industrialization, urban development, and population growth in the last decades caused a significant increase in congestion of transportation networks across the world. Increasing congestion of transportation networks and limitations of the traditional methods in analyzing and evaluating the congestion mitigation strategies led many transportation professionals to the use of traffic simulation techniques. Nowadays, traffic simulation is heavily used in a variety of applications, including the design of transportation facilities, traffic flow management, and intelligent transportation systems. The literature review, conducted as a part of this study, shows that many different traffic simulation packages with various features have been developed to date. The present study specifically focuses on a comprehensive comparative analysis of the advanced interactive microscopic simulator for urban and non-urban networks (AIMSUN) and SimTraffic microsimulation models, which have been widely used in the literature and practice. The evaluation of microsimulation models is performed for the four roadway sections with different functional classifications, which are located in the northern part of Iran. The SimTraffic and AIMSUN microsimulation models are compared in terms of the major transportation network performance indicators. The results from the conducted analysis indicate that AIMSUN returned smaller errors for the vehicle flow, travel speed, and total travel distance. On the other hand, SimTraffic provided more accurate values of the travel time. Both microsimulation models were able to effectively identify traffic bottlenecks. Findings from this study will be useful for the researchers and practitioners, who heavily rely on microsimulation models in transportation planning.

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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 (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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Leszczylowska, Anna. "Microsimulation as an instrument for tax policy analyses." Business and Economic Horizons 11, no. 1 (March 15, 2015): 14–27. http://dx.doi.org/10.15208/beh.2015.02.

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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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Ištoka Otković, Irena, Tomaž Tollazzi, Matjaž Šraml, and Damir Varevac. "Calibration of the Microsimulation Traffic Model Using Different Neural Network Applications." Future Transportation 3, no. 1 (February 2, 2023): 150–68. http://dx.doi.org/10.3390/futuretransp3010010.

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The efficacy of the application of traffic models depends on a successful process of model calibration. Microsimulation models have a significant number of input parameters that can be optimized in the calibration process. This paper presents the optimization of input parameters that are difficult to measure or unmeasurable in real traffic conditions and includes parameters of the driver’s behavior and parameters of Wiedemann’s psychophysical car-following model. Using neural networks, models were generated for predicting travel time and queue parameters and were used in the model calibration procedure. This paper presents the results of a comparison of five different applications of neural networks in calibrating the microsimulation model. The VISSIM microsimulation traffic model was selected for calibration and field measurements were carried out on two roundabouts in a local urban transport network. The applicability of neural networks in the process of calibrating the microsimulation models was confirmed by comparison of the modelled and measured data of traffic indicators in real traffic conditions. Methods of calibration were validated with two sets of new measured data at the same intersection where the calibration of the model was carried out. The third validation was made at the intersection in a different location. The selection of the optimal calibration methodology is based on the model accuracy between the simulated and measured data of traveling time, as well as queue parameters. The microsimulation model provides access to the raw data of observed traffic parameters for each vehicle in the simulation. The dataset of the calibrated model simulation results of all travel times of the selected traffic flow was compared with the dataset of the measured field data to determine whether the data are statistically significantly different or not.

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Li, Jinjing. "Simulating Histories within Dynamic Microsimulation Models." International Journal of Microsimulation 5, no. 1 (2011): 52–76. http://dx.doi.org/10.34196/ijm.00067.

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Tanton, Robert. "Editorial special issue on Spatial Microsimulation." International Journal of Microsimulation 7, no. 1 (2013): 1–3. http://dx.doi.org/10.34196/ijm.00091.

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Tanton, Robert. "A Review of Spatial Microsimulation Methods." International Journal of Microsimulation 7, no. 1 (2013): 4–25. http://dx.doi.org/10.34196/ijm.00092.

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Husby, Trond. "Review of Spatial Microsimulation with R." International Journal of Microsimulation 10, no. 1 (2016): 201–3. http://dx.doi.org/10.34196/ijm.00154.

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Aaberge, Rolf, and Ugo Colombino. "Structural Labour Supply Models and Microsimulation." International Journal of Microsimulation 11, no. 1 (2017): 162–97. http://dx.doi.org/10.34196/ijm.00177.

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Menneni, Sandeep, Carlos Sun, and Peter Vortisch. "Microsimulation Calibration Using Speed-Flow Relationships." Transportation Research Record: Journal of the Transportation Research Board 2088, no. 1 (January 2008): 1–9. http://dx.doi.org/10.3141/2088-01.

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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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