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Studies have shown that vehicle trajectory data are effective for calibrating microsimulation models. Light Detection and Ranging (LiDAR) technology offers high-resolution 3D data, allowing for detailed mapping of the surrounding environment, including road geometry, roadside infrastructures, and moving objects such as vehicles, cyclists, and pedestrians. Unlike other traditional methods of trajectory data collection, LiDAR's high-speed data processing, fine angular resolution, high measurement accuracy, and high performance in adverse weather and low-light conditions make it well suited for applications requiring real-time response, such as autonomous vehicles. This research presents a comprehensive framework for integrating LiDAR sensor data into simulation models and their accurate calibration strategies for proactive safety analysis. Vehicle trajectory data were extracted from LiDAR point clouds collected at six urban signalized intersections in Lubbock, Texas, in the USA. Each study intersection was modeled with PTV VISSIM and calibrated to replicate the observed field scenarios. The Directed Brute Force method was used to calibrate two car-following and two lane-change parameters of the Wiedemann 1999 model in VISSIM, resulting in an average accuracy of 92.7%. Rear-end conflicts extracted from the calibrated models combined with a ten-year historical crash dataset were fitted into a Negative Binomial (NB) model to estimate the model's parameters. In all the six intersections, rear-end conflict count is a statistically significant predictor (p-value < 0.05) of observed rear-end crash frequency. The outcome of this study provides a framework for the combined use of LiDAR-based vehicle trajectory data, microsimulation, and surrogate safety assessment tools to transportation professionals. This integration allows for more accurate and proactive safety evaluations, which are essential for designing safer transportation systems, effective traffic control strategies, and predicting future congestion problems. [ABSTRACT FROM AUTHOR]

Traffic congestion is a phenomenon that has been extensively explored by researchers due to its impact on reliability and safety. This research is focused on proactively detecting and mitigating congestion on freeways by fuzing conventional traffic data obtained from radar and loop detectors with newer sources, such as Bluetooth and connected vehicles (CV). Data-driven and signal-processing techniques are explored to develop algorithms that use near- or real-time traffic measurements to predict the onset and intensity level of traffic congestion. The developed algorithm can be applied to both conventional and low penetration CV-based datasets to identify four types of congestion, that is, normal, recurring, other non-recurring, and incident. This research also demonstrates the advantage of using CV-based travel time estimates to calibrate microsimulation models over fixed point-based derivations of travel time from spot speeds. Finally, a set of mitigation strategies consisting of speed harmonization and dynamic rerouting are implemented in the calibrated simulation network to demonstrate their effectiveness in proactively reducing recurring and non-recurring congestion. The final derived algorithm is effective in proactively predicting the onset of congestion and its intensity level, with an overall mean prediction error of 30.2%. A limitation to the algorithm's methodology is that it cannot disentangle the type of congestion when two or more are occurring simultaneously and only predicts/classifies the anticipated highest level. However, this does not impair the user's ability to readily deploy appropriate mitigation strategies to alleviate the predicted intensity of congestion. [ABSTRACT FROM AUTHOR]

This paper provides a detailed literature review of the environmental implications of vehicle platooning, a topic gaining significant attention in transportation. While previous reviews have focused on the safety, planning, fuel economy, and microsimulation aspects of platooning, this paper delves into environmental aspects. It identifies a lack of research adopting a holistic approach to transport and environmental benefits and emphasizes the need for further research to enhance vehicle efficiency and improve air quality and health conditions. This study traces the historical evolution of platooning, highlighting the shift in research focus over the decades. It advocates for more research on platooning's environmental aspects, particularly pollutant emissions and air quality. The primary contributions of this work are threefold and include the following: firstly, it delineates simulation methodologies for platooning and the associated pollutant emissions; secondly, it offers a critical assessment of the existing literature on vehicle emissions, fuel consumption, and energy savings; and thirdly, it illuminates the prospective research challenges within the specialized domain of vehicle platooning. [ABSTRACT FROM AUTHOR]

The traffic in developing countries presents its own specificity, notably due to the heterogeneous traffic and a weak-lane discipline. This leads to differences in driver behavior between these countries and developed countries. Knowing that the analysis of the drivers from developed countries leads the design of the majority of driver models, it is not surprising that the simulations performed using these models do not match the field data of the developing countries. This article presents a systematic review of the literature on modeling driving behaviors in the context of developing countries. The study focuses on the microsimulation approaches, and specifically on the multiagent paradigm, that are considered suitable for reproducing driving behaviors with accuracy. The major contributions from the recent literature are analyzed. Three major scientific challenges and related minor research directions are described. [ABSTRACT FROM AUTHOR]

One of the main factors affecting the safety of signalised intersections is the stop/go behaviour during the yellow interval. Although previous research has exhaustively examined drivers' stop/go decision-making, the expected autonomous vehicles' (AVs') stop/go behaviour has not yet been thoroughly investigated. Through a series of simulation experiments developed for conventional and autonomous vehicles using different carfollowing, lane-changing, lateral placement and stop/go model parameter values, we examine here whether the default VISSIM stop/go parameter values can adequately replicate the observed drivers' behaviour at the considered intersection and assess the suitability of using the currently available options, albeit referring to human drivers, to simulate the expected stop/go behaviour of AVs. We also propose a policy framework for determining the desired behaviour of AVs in yellow interval, which is integrated into an AVs logic and achieved in the last simulation to explore the effect of automation on the stop/go outcome and, hence, on the safety level of signalised intersections. Several data analysis and modeling techniques were used for the formulation of certain scenarios, including binary choice models. The default stop/go parameter values were found unfit to replicate the observed stop/go behaviour and subjected to calibration. Compared to the currently available options, the proposed AVs logic proved to produce the most accurate results, in terms of the stop/go simulation outcome. Regarding the impact of automation on the stop/go outcome, the simulation experiments showed that AVs preferred a more conservative behaviour in favor of road safety, as indicated by the significant reduction (z15%) in the number of vehicles crossing the stop line during the yellow light and zero instances of red light violation. However, compared to the conservative drivers represented by the default stop/go parameter values, AVs preferred a more rational behaviour in favor of intersection capacity without compromising road safety. [ABSTRACT FROM AUTHOR]

The purpose of this study is to predict the location of new housing supply and compare two different modelling frameworks. Housing supply significantly influences land use simulations in urban microsimulation systems, closely linked with demographic, transportation, and environmental modules. The supply of new dwellings in urban simulation models have evolved from static, exogenous inputs to dynamic, agent-based determinations. This study follows this trend to examine two approaches to modelling the spatial distribution of new housing supply: the first approach models the development choice of each location; the second approach models the location choice of each residential project. Multinomial logit and nested logit models are applied to a Toronto empirical dataset. The results show that although the first approach achieves higher goodness-of-fit and prediction accuracy, the second approach performs better in explaining the locational preference of individual projects. Project characteristics such as structure type and construction cost, as well as location characteristics such as housing price, number of sales, and population density affect the spatial distribution of new housing supply. Both approaches are evaluated regarding estimation, prediction, and microsimulation system integration. The findings enhance housing modelling literature and inform urban microsimulation's housing supply model configuration. [ABSTRACT FROM AUTHOR]

The shock of the COVID-19 pandemic affected the human capital formation of children and youths. As a consequence of this disruption, the pandemic is likely to imply permanent lower levels of human capital. This paper provides new evidence on the impact of COVID-19 and school closures on education in Latin America by exploiting harmonized microdata from a large set of national household surveys carried out in 2020, during the pandemic. In addition, the paper uses microsimulations to assess the potential effect of changes in human capital due to the COVID-19 crisis on future income distributions. The findings show that the pandemic is likely to have significant long-run consequences in terms of incomes and poverty if strong compensatory measures are not taken soon. [ABSTRACT FROM AUTHOR]

This paper provides a first assessment of the fiscal and distributional consequences of the ongoing structural changes in the labour markets of EU member states, mostly driven by technological progress and ageing. The Cedefop 2020 Skills forecast (including the effects of COVID-19), population projections and the forecast on pension expenditures depict a scenario of an ageing population, an inverted U-shaped unemployment trend and potentially polarising labour markets in the EU till 2030, the latter mostly driven by a surge in high-skill occupations. We make use of the microsimulation model EUROMOD and reweighting techniques to analyse the fiscal and distributional impacts of these trends under a no-policy-change assumption. The results suggest that the macro trends will increase pressure on government budgets, however, we also show that the current tax-benefit systems have the capacity to counterbalance the increases in income inequality and poverty risks triggered by the expected future labour markets developments. [ABSTRACT FROM AUTHOR]

Objectives: Few existing economic evaluations of telephone call-back services for smoking cessation (quitlines) include productivity measures. The Economics of Cancer Collaboration Tobacco Control (ECCTC) model was developed by adopting a societal perspective, including productivity impacts. Study type: Economic simulation modelling. Methods: A multi-health state Markov cohort microsimulation model was constructed. The population was the Victorian smoking population in 2018. The effectiveness of the Victorian Quitline was informed by an evaluation and compared with no service. Risks of developing disease for smokers and former smokers were obtained from the literature. The model calculated economic measures, including average and total costs and health effects; incremental cost-effectiveness ratios; and net monetary benefit (NMB) for both the healthcare and societal perspective. An extensive uncertainty analysis was conducted. Results: The Quitline service is cost-effective and dominant from both healthcare and societal perspectives, reducing costs with greater health benefits compared with no service. The expected incremental NMB was $2912 per person from the healthcare perspective and $7398 from the societal perspective. Total cost savings were $869 035 of healthcare costs, $1.1 million for absenteeism, $21.8 million for lost workforce participation, and $8.4 million for premature mortality, with a total reduction in societal costs of $32.2 million, over the 80 year timeframe of the model. Probabilistic sensitivity analysis suggested a high degree of certainty in these results, and overall conclusions were robust to one-way sensitivity and scenario analyses. Conclusions: The Victorian Quitline service is cost-effective and should be retained and expanded where possible. The ECCTC model can be adapted to analyse the cost-effectiveness of other tobacco cessation interventions, populations and contexts. [ABSTRACT FROM AUTHOR]

Spatial microsimulation encompasses a range of alternative methodological approaches for the small area estimation (SAE) of target population parameters from sample survey data down to target small areas in contexts where such data are desired but not otherwise available. Although widely used, an enduring limitation of spatial microsimulation SAE approaches is their current inability to deliver reliable measures of uncertainty—and hence confidence intervals—around the small area estimates produced. In this article, we overcome this key limitation via the development of a measure of uncertainty that takes into account both variance and bias, that is, the mean squared error. This new approach is evaluated via a simulation study and demonstrated in a practical application using European Union Statistics on Income and Living Conditions data to explore income levels across Italian municipalities. Evaluations show that the approach proposed delivers accurate estimates of uncertainty and is robust to nonnormal distributions. The approach provides a significant development to widely used spatial microsimulation SAE techniques. [ABSTRACT FROM AUTHOR]

The rapid growth of population in urban regions is considered a point of interest concerning car ownership. Increasing car ownership leads to traffic congestion and large amounts of emissions in urban networks. The existence of toxic pollutants and particulates in the air is one of the main reasons for diseases in humans and other living beings. The aim of this paper to identify traffic congestion and presenting it using several scales of models to explain the impact of various characteristics (road, traffic, and vehicle) on traffic flow and air pollution. Furthermore, different techniques implemented to mitigate congestion and amounts of emitted pollutants. The adopted models vary from macro-simulation, which estimates the pollutant quantity for each vehicle, to micro-simulation, which depends on several parameters affecting the vehicle and driver behaviour. The overview also includes different methods of emission measurements that are performed in the field or laboratory. Air quality evaluation is presented by a numerical index (i.e., Air Quality Index) which depends on the estimated pollutant. Air Quality Index (AQI) is categorized from good to severe effect. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the viability of microsimulation in estimating critical gaps at unsignalised intersections. Critical gaps are generally estimated by extracting gap acceptance data from traffic videos. Extracting such data is a strenuous task, especially under mixed traffic conditions. In this study, gap acceptance data were generated from simulation models developed in the Vissim software package. Critical gaps of three unsignalised intersections were estimated using field data and simulated data of the intersections. Critical gaps were estimated by the logit method and the clearing time method. The critical gaps estimated using simulated data were reasonably close to those obtained using field data for both methods. The results also showed that the logit method was more robust than the clearing time method in estimating critical gaps under mixed traffic conditions. Moreover, the time needed for generating simulated data was only 3.6% of that required for obtaining field data. The method proposed in this study will help traffic engineers and practitioners in critical gap estimation by saving a considerable amount of time and effort. [ABSTRACT FROM AUTHOR]

MnAl-C is a prominent candidate for the replacement of rare earth magnets with a moderate energy density product. Crystallographic defects have a strong effect on magnetization properties. In this work, we show the influence of twinning defects in the nanometer regime on the quality of the magnet. Standard micromagnetic simulations and computations of the saddle point configuration for magnetization reversal highlight the importance of optimizing the fraction of and reducing the width of crystallographic twin defects. Switching field distributions and the maximum possible coercive field for ideal microstructures without defects are estimated using a reduced order micromagnetic model. [ABSTRACT FROM AUTHOR]

Transportation agencies periodically conduct signal retiming (i.e., optimization) to ensure efficient signal operations. Previous studies introduced the notion of the "resonant cycle length" (RCL), which is based on the premise that a good progression of traffic on the corridor mainline for various volume fluctuations can be achieved with an appropriate value of cycle lengths, where all other signal timing parameters (splits, offsets and phase sequences) remain unaltered. Several follow-up studies brought many inconsistencies in the previously introduced concept. For instance, authors would investigate the existence of the RCL by evaluating the performance of signal timing plans for not only coordinated movements (side streets and coordinated movements together), but would optimize all signal timing parameters (not only cycle lengths) while investigating. This study sheds light on the RCL concept and highlights the importance of all signal timing parameters for signal performance. In addition, we introduce a concept called the resonant signal timing plan (RSTP) as a refinement for the RCL, which represents a combination of signal timing parameters that (unaltered) retain an acceptable performance for a variety of traffic conditions. Results show that different sets of signal timing parameters cause plans to be resonant depending on the evaluation type. [ABSTRACT FROM AUTHOR]

Optimizing signal timing improves sustainability metrics (e.g., fuel consumption or "FC"). Historically, traffic agencies have retimed signal timing to improve mobility measures (e.g., delays). However, optimizing signals to reduce delays does not necessarily mitigate sustainability measures. Hence, this study introduces an approach that integrates a newly derived surrogate measure for FC, traffic microsimulation software, and a stochastic genetic algorithm. This approach optimizes signal timing to reduce the surrogate measure and reduce sustainability metrics. This study also evaluated the impact of heavy vehicles' presence in a fleet on signal timing and FC savings. A 13-intersection arterial on Washington Street in the Chicago metro area served as a case study. Optimized signal timing delivered solutions that balanced both sustainability and mobility. The estimated excess FC savings ranged between 8 and 12% under moderate operating conditions, with no heavy vehicles, compared to the initial signal timing. The savings reached up to ~14% when many heavy vehicles existed on the side streets. Most of the improvements came without worsening traffic-mobility efficiency, which shows the possibility of a fair tradeoff between mobility and sustainability. All optimization scenarios showed that a slightly longer cycle length than the one implemented in the field is required to reduce FC. [ABSTRACT FROM AUTHOR]

Complex magnetic materials hosting topologically non-trivial particle-like objects such as skyrmions are under intensive research and could fundamentally change the way we store and process data. One important class of materials are helimagnetic materials with Dzyaloshinskii-Moriya interaction. Recently, it was demonstrated that thin nanodisks consisting of two layers with opposite chirality can host a single stable Bloch point of two different types at the interface between the layers. Using micromagnetic simulations we show that FeGe nanostrips consisting of two layers with opposite chirality can host multiple coexisting Bloch points in an arbitrary combination of the two different types. We show that the number of Bloch points that can simultaneously coexist depends on the strip geometry and the type of the individual Bloch points. Our simulation results allow us to predict strip geometries suitable for an arbitrary number of Bloch points. We show an example of an 80-Bloch-point configuration verifying the prediction. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused major disruptions to international tourism and has stimulated research interest. This study examines links between COVID-19-induced tourism disruption and poverty in Tanzania. Unlike previous studies linking COVID-19, tourism and poverty, this paper uses a social accounting matrix (SAM) microsimulation analysis that allows decomposition of poverty indices by population subgroup and assessment of the drivers of aggregate poverty. The results of the SAM multiplier analysis indicate that all households will experience reduced incomes, and that this effect will be more pronounced in urban than rural households. The microsimulation results suggest that the COVID-19-induced tourism crisis will exacerbate the poverty headcount, poverty gap and poverty severity, with urban and rural non-farm households being most affected. The results of the poverty decomposition show that the growth effect has a stronger impact than the inequality effect on increased poverty. Poverty increases and inequality decreases simultaneously. The paper suggests several demand- and supply-side policies that may help to build tourism resilience and recovery and alleviate poverty in Tanzania in the post-COVID world. [ABSTRACT FROM AUTHOR]

Objectives and importance of study: Colorectal cancer (CRC) is Australia's fourth most commonly diagnosed cancer. CRC screening is an effective intervention to reduce this burden. The National Bowel Cancer Screening Program (NBCSP) provides 2-yearly immunochemical faecal occult blood tests (iFOBTs) to Australians aged 50-74 years; a diagnostic colonoscopy is conducted after a positive iFOBT. Clinical guidelines inform colonoscopy usage, and appropriate use of these guidelines is vital to investigate gastrointestinal symptoms, detect bowel abnormalities and CRC, and remove precancerous polyps. Colonoscopy services are under strain, with limited formal strategies to prioritise patients. There are concerns among practitioners and patient advocates that the NBCSP generates additional colonoscopy requests and increases wait times, worsening patient outcomes and prolonging distress. In this research study, we estimate and project colonoscopy use in Australia from 2001 to 2030 and determine the impact of the NBCSP by examining model-estimated NBCSP colonoscopy demand. Methods: Colonoscopy use in Australia was compiled using Medicare Benefits Schedule (MBS) claims for colonoscopies from 2001 to 2019. From these data, projections were made from 2020 to 2030. Policy1-Bowel, a microsimulation model, was used to estimate NBCSP-related colonoscopy demand from screening follow-up and colonoscopic surveillance from 2006 to 2030. Results: MBS-funded colonoscopy use increased from 284 676 in 2001 to 663 213 in 2019. Annual use is projected to be more than 780 000 by 2030. Of these, 10-14% are projected to be generated by the NBCSP. Per-capita MBS-funded colonoscopy utilisation increased 0.2% annually over 2015-2019, a slowing of growth compared to previous trends. Conclusion: The NBCSP accounts for a modest fraction of colonoscopy use in Australia, and a better understanding of colonoscopy use not associated with the NBCSP is needed. Promoting adherence to guidelinerecommended iFOBT and colonoscopy use could ease pressure on services and improve outcomes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The goal of this study is to calibrate a multiscale model of tumor angiogenesis with time-resolved data to allow for systematic testing of mathematical predictions of vascular sprouting. The multi-scale model consists of an agent-based description of tumor and endothelial cell dynamics coupled to a continuum model of vascular endothelial growth factor concentration. First, we calibrate ordinary differential equation models to time-resolved protein concentration data to estimate the rates of secretion and consumption of vascular endothelial growth factor by endothelial and tumor cells, respectively. These parameters are then input into the multiscale tumor angiogenesis model, and the remaining model parameters are then calibrated to time resolved confocal microscopy images obtained within a 3D vascularized microfluidic platform. The microfluidic platform mimics a functional blood vessel with a surrounding collagen matrix seeded with inflammatory breast cancer cells, which induce tumor angiogenesis. Once the multi-scale model is fully parameterized, we forecast the spatiotemporal distribution of vascular sprouts at future time points and directly compare the predictions to experimentally measured data. We assess the ability of our model to globally recapitulate angiogenic vasculature density, resulting in an average relative calibration error of 17.7% ± 6.3% and an average prediction error of 20.2% ± 4% and 21.7% ± 3.6% using one and four calibrated parameters, respectively. We then assess the model's ability to predict local vessel morphology (individualized vessel structure as opposed to global vascular density), initialized with the first time point and calibrated with two intermediate time points. In this study, we have rigorously calibrated a mechanism-based, multiscale, mathematical model of angiogenic sprouting to multimodal experimental data to make specific, testable predictions. Author summary: The integration of experimentally obtained data to complex mathematical models, specifically in relation to tumor angiogenesis, is a rich field that is of great interest to the community. This integration, typically through calibrating model parameters that govern the mathematical model, is paramount to making specific, testable predictions of the system under investigation. In this work, we have used several forms of quantitative experimental data to inform a complex mathematical model to predict tumor angiogenesis. We do this through a multi-step calibration method by separating individual processes described in the model and utilizing data to inform key model parameters involved in that process. At the final step in our calibration method, we use longitudinal studies of tumor sprouting in our 3D microfluidic platform to fully parameterize our multiscale model of tumor angiogenesis. We utilize early time points to inform model parameters and then test our models ability to predict both local vascular features and vascular summary statistics at later time points. We have shown, with uncertainty, the prediction ability of our model and have shown we can reliably predict in vitro experiments of tumor-induced angiogenesis. [ABSTRACT FROM AUTHOR]

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Markov cohort analysis is a popular deterministic method in medical decision making for calculating mean outcomes in a Markov model by following a cohort of individuals through time. At present, obtaining outcome variances requires either forsaking cohort analysis in favor of a Markov decision process model or using Monte Carlo simulation (microsimulation), a more computationally demanding procedure that provides only statistical estimates. Here we derive an augmented version of cohort analysis that allows exact computation (not merely estimation) of (co)variances. In second-order models that incorporate parameter uncertainty, augmented cohort analysis can replace the "inner loop" required in Monte Carlo simulation, resulting in quicker and more accurate estimates. One reason for computing variances is to calculate a measure of the strength of an affirmative cost-effectiveness conclusion. In Markov cost-effectiveness analysis, an equivalent measure of cost-effectiveness is positivity of the expected incremental net monetary benefit. Augmented cohort analysis allows calculation of the number of standard deviations that this quantity falls above zero. As a measure of strength of cost-effectiveness, this quantity increases with cohort size. This means that the common practice of taking cohort size to be one can substantially underestimate the strength of a resulting cost-effectiveness conclusion under realistically large cohorts. Moreover, if realistic cohort size is large, then modelers can avoid microsimulation by using augmented cohort analysis and Chebyshev bounds to guarantee the probability of cost effectiveness is close to one. History: Accepted by J. Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.1234. [ABSTRACT FROM AUTHOR]

This paper presents the validation analysis of an activity-based travel demand modeling system – shorter-term decisions simulator (SDS). It compares microsimulation results of SDS with observed census and travel-activity survey data for the base year 2006, and forecasting years – 2016 and 2018. Base year validation suggests SDS's reasonable capability to replicate observed data as indicated by lower absolute percentage error (APE) measures. SDS exhibits satisfactory performance for the forecasting years as well. For instance, Kolmogorov–Smirnov (KS) tests during the activity start time and duration analysis for year 2018 yield smaller D-values than critical values, which indicates a better fit model. Prediction of tour-level mode choice, shared travel choice, and vehicle allocation also exhibit good accuracy (± 4%). Validation results presented in this study demonstrate SDS's promising forecasting capability so that the model can be utilized efficiently to develop and test alternative land use and travel demand management strategies. [ABSTRACT FROM AUTHOR]

In order to transform cities into more liveable, safe, and sustainable places, we must shift our mobility paradigms. As one auspicious concept amongst novel intelligent transportation systems, personal rapid transit (PRT) disaggregates urban transportation into small, electric vessels that are centrally operated on dedicated infrastructure, yielding the potential to make public transit more convenient, affordable, and sustainable all at once. In light of this, we examined the potential performance of PRT in a medium-sized German city. Utilizing the traffic simulator SUMO, as well as a specifically developed open source mobility scenario consisting of infrastructure and travel demand, we assessed the level of service and efficiency. We found that a fleet of 30 vehicles can serve the mobility demand of the chosen city while passenger waiting times are guaranteed to stay below three minutes. Vehicle occupancies can be doubled when coordinating vehicles between stations instead of letting them idle randomly. Furthermore, our results show that different combinations of system designs and operating strategies succeed in meeting typical performance requirements—for instance, an operating strategy where unoccupied vehicles idle randomly can effectively compensate for a reduced fleetsize. Depending on the preliminaries of specific cities, such as the availability of space, travel behavior, political background, or acceptable investment and operational costs, a matching transportation system can be designed around the quantitative findings obtained in this study. [ABSTRACT FROM AUTHOR]

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

The fabrication of microflow channels with high accuracy in terms of the optimization of the proposed designs, minimization of surface roughness, and flow control of microfluidic parameters is challenging when evaluating the performance of microfluidic systems. The use of conventional input devices, such as peristaltic pumps and digital pressure pumps, to evaluate the flow control of such parameters cannot confirm a wide range of data analysis with higher accuracy because of their operational drawbacks. In this study, we optimized the circular and rectangular-shaped microflow channels of a 100 μm microfluidic chip using a three-dimensional simulation tool, and analyzed concentration profiles of different regions of the microflow channels. Then, we applied a deep learning (DL) algorithm for the dense layers of the rectified linear unit (ReLU), Leaky ReLU, and Swish activation functions to train and test 1600 experimental and interpolation of data samples which obtained from the microfluidic chip. Moreover, using the same DL algorithm, we configured three models for each of these three functions by changing the internal middle layers of these models. As a result, we obtained a total of 9 average accuracy values of ReLU, Leaky ReLU, and Swish functions for a defined threshold value of 6 × 10 − 5 using the trial-and-error method. We applied single-to-five-fold cross-validation technique of deep neural network to avoid overfitting and reduce noises from data-set to evaluate better average accuracy of data of microfluidic parameters. [ABSTRACT FROM AUTHOR]

At the beginning of the COVID-19 pandemic, most scholars focused on how international transportation (such as airlines) spread the virus to different countries. At this point, scholars have begun to pay more attentions on how COVID-19 locally transmission via ground transportation systems. Because many people use these ground services to commute in urban areas, a high passenger volume may lead to a domestic large outbreak. Without detailed disease spreading path, healthcare professionals are still not sure where and how to apply these anti-epidemic measures. Therefore, this study chose the Taipei metro system as our study area to investigate the relationship between metro station passenger volume and COVID-19 transmission. By using the electric metro ticket data, we know the movement of metro passengers in Taipei, and this OD movement dataset was used to estimate the spreading path of the COVID-19. In order to simulate possible Covid-19 spreading cases in the real world, two different methods (the agent-based model (a microlevel simulation) and the effective distance method (a macro-level estimator)) were applied. Then, we compared the COVID-19 arrival order for each station. In our result, the average infectious order of stations of agent based model and shortest path effective distance is similar. Among all stations, Taipei Main Station is the first infectious station, and the top 15 infectious stations are similar according to result of the two method. Our result may help the authority choose proper methods to simulate the epidemic local transmission and then allocate resources effectively in the future. [ABSTRACT FROM AUTHOR]

Overdetection (often referred to as overdiagnosis) of cancer is the detection of disease, such as through a screening program, that would otherwise remain occult through an individual's life. In the context of screening, this could occur for cancers that were slow growing or indolent, or simply because an unscreened individual would have died from some other cause before the cancer had surfaced clinically. The main harm associated with overdetection is the subsequent overdiagnosis and overtreatment of disease. In this article, the phenomenon is reviewed, the methods of estimation of overdetection are discussed and reasons for variability in such estimates are given, with emphasis on an analysis using Canadian data. Microsimulation modeling is used to illustrate the expected time course of cancer detection that gives rise to overdetection. While overdetection exists, the actual amount is likely to be much lower than the estimate used by the Canadian Task Force on Preventive Health Care. Furthermore, the issue is of greater significance in older rather than younger women due to competing causes of death. The particular challenge associated with in situ breast cancer is considered and possible approaches to avoiding overtreatment are suggested. [ABSTRACT FROM AUTHOR]

Size-controllable micro-droplets obtained in microfluidic cross-junctions are significant in microfluidics. Modeling and predictions in microfluidic-based droplet formation characteristics to date using various traditional theoretical or empirical correlations are far from satisfactory. Driven by unprecedented data volumes from microfluidic experiments and simulations, statistical learning can offer a powerful technique to extract data that can be interpreted into underlying fluid physics and modeling. This Letter historically combines the current experimental data and experimental/numerical data from previous publications as a microfluidics-based droplet formation characteristics database. Two supervised statistical learning algorithms, deep neural network and factorization-machine-based neural network (Deep-FM), were established to model and predict the formed droplet size in microfluidic cross-junctions. As a newly developed statistical learning code in 2017, the Deep-FM manifests a better prediction performance, where the average relative error was only 4.09% and nearly 98% of the data points had individual relative errors of 10% or less. Such high accuracy can be attributed to the outstanding interactions between high-order and low-order features of the Deep-FM framework. Another innovation in this Letter lies in the training dataset shrinkage and optimization without sacrificing the prediction accuracy. Such a method pioneers statistical learning algorithms in small-sample modeling problems, which is different from big data modeling and analyses. The improved statistical learning proposed in this Letter provides universal high-accuracy modeling for microfluidic-based droplet characteristics prediction, which can be an influential data-processing framework that can boost and probably transform current lines of microfluidic physics research and industrial applications. [ABSTRACT FROM AUTHOR]

This study proposes a system construction approach under Industry 4.0 infrastructure that is validated by the proposed framework of microservice quality assessment with framework with data modeling and simulation methodology to achieve innovation and value cocreation goals. The framework, which combines a dynamic process flow with service-dominant logic design and reliability assessment using a multilayer perceptron (MLP) prediction model can assist decision makers in optimizing their service innovation and decision-making processes. The service innovation and evaluation approaches have implications for optimizing the corporation cooperation. The corporation can form a much more comprehensive manufacturing infrastructure or system by considering the requirements and assessment results of third parties. To help the corporation redefine its value proposition and system structure, we must examine the system interaction between different hierarchical layers within the Industry 4.0 system infrastructure. This study used a production dataset from the NASDAQ-listed electronics corporation and two top German and Japanese automobile firms. The proposed system framework had already been validated and introduced to improve 12% of service quality. The system integrated with anticipated functions will accelerate service innovation and optimization by combining MLP and Kaplan–Meier estimation methodologies by extracting the characteristics of realistic datasets. [ABSTRACT FROM AUTHOR]

Governments across the world rely on their Customs Administration to provide functions that include border security, intellectual property rights protection, environmental protection, and revenue mobilisation amongst others. Analyzing the trends in revenue being collected from Customs is necessary to direct government policies and decisions. Models that can capture the trends being purported from the nominal (nonreal) tax values with respect to the trade volumes (value) over the period are indispensable. Predominant amongst the existing models are the econometric models (the GDP-based model, the monthly receipts model, and the microsimulation model), which are laborious and sometimes unreliable when studying trends in time series data. In this study, we modelled monthly revenue data obtained from the Ghana Revenue Authority-Customs Division (GRA-CD) for the period January 2010 to December 2019 using two traditional time series models, ARIMA model and ARIMA Error Regression Model (ARIMAX), and two machine learning time series models, Bayesian Structural Time Series (BSTS) model and a Neural Network Autoregression model. The Neural Network Autoregression model of the form NNAR (1, 3) provided the best forecasts with the least Mean Squared Error (MSE) of 53.87 and relatively lower Mean Absolute Percentage Error (MAPE) of 0.08. Generally, the machine learning models (NNAR (1, 3) and BSTS) outperformed the traditional time series models (ARIMA and ARIMAX models). The forecast values from the NNAR (1, 3) indicated a potential decline in revenue and this emphasizes the need for relevant authorities to institute measures to improve revenue generation in the immediate future. [ABSTRACT FROM AUTHOR]

Data contamination in meta-approaches where multiple biological samples are combined considerably affects the results of subsequent downstream analyses, such as differential abundance tests comparing multiple groups at a fixed time point. Little has been thoroughly investigated regarding the impact of the lurking variable of various batch sources, such as different days or different laboratories, in more complicated time series experimental designs, for instance, repeatedly measured longitudinal data and metadata. We highlight that the influence of batch factors is significant on subsequent downstream analyses, including longitudinal differential abundance tests, by performing a case study of microbiome time course data with two treatment groups and a simulation study of mimic microbiome longitudinal counts. [ABSTRACT FROM AUTHOR]

The interest in investigating transportation issues at university campuses has been growing worldwide. Given the university travelers' unique travel patterns and characteristics influenced mainly by students, forecasting the travel demand of university travelers provides a unique opportunity to understand these patterns and seek solutions for transportation problems at such major trip generators. This study aims at developing a travel demand forecasting model (TDFM) for a university campus, which can be vital for testing alternative solutions, strategies, and policies to tackle transportation problems on university campuses, especially those related to sustainable transportation. The proposed methodology was applied to Sharjah University City (SUC) as a case study. Thus, a tour-based TDFM was developed to replicate the patterns of activity-based travels at SUC. The model was established using the state-of-the-art modeling tool PTV VISUM. Subsequently, it was validated for the baseline year and then applied to forecast future traffic demands. With the aim to reduce vehicular traffic volumes, the model was employed to evaluate two scenarios: introducing parking permits and pricing and establishing a park-and-ride facility to serve travelers to campus. These scenarios were compared in terms of several traffic and environmental performance measures. Based on predefined criteria, results highlighted that parking permit enforcement outperformed the park-and-ride facility. The present research may be considered one of the few studies to integrate a tour-based TDFM with a university's entire transportation network, a detailed traffic microsimulation, and an emission evaluation model. Results highlighted the effectiveness of the proposed modeling framework in providing reliable solutions for challenging questions that face urban university campus planners under different scenarios. Other universities can follow the methodology presented in this work to develop similar travel forecasting models for their campuses based on their specific travel characteristics. • Developed a travel demand forecasting model to tackle transportation problems on university campuses. • Used a tour-based approach rather than the traditional four-step modeling approach. • Applied to Sharjah University City, UAE, to evaluate two scenarios to reduce vehicular traffic volumes on campus. [ABSTRACT FROM AUTHOR]

This paper investigates Vācaspati Miśra's remarkably complex argumentative architecture in support of non-difference by means of a microsimulation model, the classical gold-crown case. A full range of positions, including instantaneism, transformative continuum, indeterminate common basis reference, difference and non-difference coordination, etc., is put under the scrutiny of the Vācaspati Miśra's dialectic effort. The possibility of coexistence of multiple properties with a single referent is then formally explored. The analysis is carried out in compliance with the 'Navya-Nyāya Formal Language' extensional set-based approach and its non-predicative, and variables free, relational syntax. Repeatable modules and structures of reasoning are identified and designed in the form of hypothesis frameworks, axioms and theorems to allow more accurate inferences, in particular regarding transformation and permanence, together with possible or impossible plexuses of properties. Identity and difference qua mutual absence are thoroughly defined with the aid of these formal tools, which conjointly might cast new light on the heuristic and expressive power of Navya-Nyāya logic, as well as on the theoretical potentialities of the non-dualistic account. [ABSTRACT FROM AUTHOR]

In this paper, a self-adaptive, two-stage fuzzy controller is established to realize the real-time online optimization of traffic signal timing plan, which takes multimodels of transportation as the research object to analyze the reliability of the control system at the isolated urban intersection. In this system, the first stage calculates traffic urgency degree for all red phases and selects the red phase with maximum traffic urgency degree as the next green phase. The second stage determines whether to extend or terminate the current signal phase. Aiming at the problems of the parameters of membership functions empirical settings and insufficient response to the real-time fluctuation in traffic flow, the controller introduces an improved hybrid genetic algorithm to solve it and enable the controller to self-learn. Finally, a microsimulation platform is constructed based on the VISSIM and Python language to evaluate the efficiency and reliability of the controller under complex actual traffic conditions. Results showed that the average delay time per vehicle is reduced by 14.59%, while the average number of stops per vehicle is reduced by 0.71% compared with the traditional control method. Results indicate that the traffic signal timing plan generated by the controller can efficiently improve the intersection traffic capacity and has good efficiency and reliability under the condition of medium saturation and unsteady flow. [ABSTRACT FROM AUTHOR]

This article presents a microsimulation that explores age, period, and cohort effects in the decline of religiosity in contemporary societies. The model implements a well-known and previously empirically validated theory of secularization that highlights the role of "fuzzy fidelity," i.e., the percentage of a population whose religiosity is moderate (Voas 2009). Validation of the model involved comparing its simulation results to shifts in religiosity over 9 waves of the European Social Survey. Simulation experiments suggest that a cohort effect, based on weakened transmission of religiosity as a function of the social environment, appears to be the best explanation for secularization in the societies studied, both for the population as a whole and for the proportions of religious, fuzzy, and secular people. [ABSTRACT FROM AUTHOR]

Background: Analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: how useful can the microbiome be for complex trait prediction? Are estimates of microbiability reliable? Can the underlying biological links between the host's genome, microbiome, and phenome be recovered? Methods: Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as inputs, and (ii) using variance-component approaches (Bayesian Reproducing Kernel Hilbert Space (RKHS) and Bayesian variable selection methods (Bayes C)) to quantify the proportion of phenotypic variance explained by the genome and the microbiome. The proposed simulation approach can mimic genetic links between the microbiome and genotype data by a permutation procedure that retains the distributional properties of the data. Results: Using real genotype and rumen microbiota abundances from dairy cattle, simulation results suggest that microbiome data can significantly improve the accuracy of phenotype predictions, regardless of whether some microbiota abundances are under direct genetic control by the host or not. This improvement depends logically on the microbiome being stable over time. Overall, random-effects linear methods appear robust for variance components estimation, in spite of the typically highly leptokurtic distribution of microbiota abundances. The predictive performance of Bayes C was higher but more sensitive to the number of causative effects than RKHS. Accuracy with Bayes C depended, in part, on the number of microorganisms' taxa that influence the phenotype. Conclusions: While we conclude that, overall, genome-microbiome-links can be characterized using variance component estimates, we are less optimistic about the possibility of identifying the causative host genetic effects that affect microbiota abundances, which would require much larger sample sizes than are typically available for genome-microbiome-phenome studies. The R code to replicate the analyses is in https://github.com/miguelperezenciso/simubiome. [ABSTRACT FROM AUTHOR]

Motivation Microbiome data have proven extremely useful for understanding microbial communities and their impacts in health and disease. Although microbiome analysis methods and standards are evolving rapidly, obtaining meaningful and interpretable results from microbiome studies still requires careful statistical treatment. In particular, many existing and emerging methods for differential abundance (DA) analysis fail to account for the fact that microbiome data are high-dimensional and sparse, compositional, negatively and positively correlated and phylogenetically structured. To better describe microbiome data and improve the power of DA testing, there is still a great need for the continued development of appropriate statistical methodology. Results In this article, we propose a model-based approach for microbiome data transformation, and a phylogenetically informed procedure for DA testing based on the transformed data. First, we extend the Dirichlet-tree multinomial (DTM) to zero-inflated DTM for multivariate modeling of microbial counts, addressing data sparsity and correlation and phylogeny among bacterial taxa. Then, within this framework and using a Bayesian formulation, we introduce posterior mean transformation to convert raw counts into non-zero relative abundances that sum to one, accounting for the compositionality nature of microbiome data. Second, using the transformed data, we propose adaptive analysis of composition of microbiomes (adaANCOM) for DA testing by constructing log-ratios adaptively on the tree for each taxon, greatly reducing the computational complexity of ANCOM in high dimensions. Finally, we present extensive simulation studies, an analysis of HMP data across 18 body sites and 2 visits, and an application to a gut microbiome and malnutrition study, to investigate the performance of posterior mean transformation and adaANCOM. Comparisons with ANCOM and other DA testing procedures show that adaANCOM controls the false discovery rate well, allows for easy interpretation of the results, and is computationally efficient for high-dimensional problems. Availability and implementation The developed R package is available at https://github.com/ZRChao/adaANCOM. For replicability purposes, scripts for our simulations and data analysis are available at https://github.com/ZRChao/Papers%5fsupplementary. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Microsimulation and synthetic data are often high dimensional, requiring extensive validation and exploration to compare results against certain benchmarks. In both cases, validation is necessary to ensure that the many univariate distributions and multivariate relationships in the new data are similar to the many univariate distributions and multivariate relationships in the underlying data. This article illustrates some data visualization techniques for comparing a generated sample or population against a known reference sample or population. For implementation ease, we also outline an iterative workflow built with R Markdown that can be shared publicly on GitHub or privately with Amazon Web Services S3. The lessons learned from this work apply to any analysis that compares multiple data sets, deals with high‐dimensional data, or involves summarizing iterations of analyses. [ABSTRACT FROM AUTHOR]

The human gut microbiota composition plays an important role in human health. Long-term diet intervention may shape human gut microbiome. Therefore, many studies focus on discovering links between long-term diets and gut microbiota composition. This study aimed to incorporate the phylogenetic relationships between the operational taxonomic units (OTUs) into the diet-microbe association analysis, using a Bayesian hierarchical negative binomial (NB) model. We regularized the dispersion parameter of the negative binomial distribution by assuming a mean-dispersion association. A simulation study showed that, if over-dispersion is present in the microbiome data, our approach performed better in terms of mean squared error (MSE) of the slope-estimates compared to the standard NB regression model or a Bayesian hierarchical NB model without including the phylogenetic relationships. Data of the Healthy Life in an Urban Setting (HELIUS) study showed that for some phylogenetic families the (posterior) variances of the slope-estimates were decreasing when including the phylogenetic relationships into the analyses. In contrast, when OTUs of the same family were not similarly affected by the food item, some bias was introduced, leading to larger (posterior) variances of the slope-estimates. Overall, the Bayesian hierarchical NB model, with a dependency between the mean and dispersion parameters, proved to be a robust method for analyzing diet-microbe associations. [ABSTRACT FROM AUTHOR]

Recent efforts to characterize the human microbiome and its relation to chronic diseases have led to a surge in statistical development for compositional data. We develop likelihood-based sufficient dimension reduction methods (SDR) to find linear combinations that contain all the information in the compositional data on an outcome variable, i.e., are sufficient for modeling and prediction of the outcome. We consider several models for the inverse regression of the compositional vector or transformations of it, as a function of outcome. They include normal, multinomial, and Poisson graphical models that allow for complex dependencies among observed counts. These methods yield efficient estimators of the reduction and can be applied to continuous or categorical outcomes. We incorporate variable selection into the estimation via penalties and address important invariance issues arising from the compositional nature of the data. We illustrate and compare our methods and some established methods for analyzing microbiome data in simulations and using data from the Human Microbiome Project. Displaying the data in the coordinate system of the SDR linear combinations allows visual inspection and facilitates comparisons across studies. [ABSTRACT FROM AUTHOR]

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The goal of this paper is to predict, by using microsimulation modelling under alternative market scenarios, housing wealth inequality in Czech society up to the year 2050. These predictions can be useful for assessing the rationale for attempts to use housing assets as a way of supplementing state pensions; and thus add to existing studies on asset-based welfare (ABW) that focused only on the recent and past situation. The models predicted small increase in housing wealth inequality among the future elderly but larger increases in inequality in the society as a whole. Rise in wealth inequality was especially steep when consumption of housing assets by future elderly was accompanied by an interruption of financial transfers to the next generations. Reduction of intergenerational transfers may thus significantly enhance wealth inequality in a society and thus pose a risk to social peace. We showed that similar outcome may appear in broader number of countries, including those with much lower homeownership rate. [ABSTRACT FROM AUTHOR]

• Multiple discrete continuous choice models with bounds on consumption. • Application to time-use analysis with episode-level bounds on time allocation. • Proposed models are easy to estimate and apply and avoid outlier predictions. This paper derives a multiple discrete–continuous (MDC) choice model formulation with constraints that specify upper bounds on consumption. To do so, considering the conventional utility maximization problem of a consumer, the Karush-Kuhn-Tucker (KKT) conditions are laid out for the MDC model with a general set of linear constraints that include inequalities. Subsequently, we derive a model with constraints that accommodate upper bounds on consumptions and an additive utility structure that accommodates lower bounds on consumptions. The likelihood expression for the proposed model takes a closed form. Furthermore, we extend the formulation to impose bounds on an MDC choice model with activity episode-level choice alternatives that accommodates a logical ordering among different episodes of an activity. The proposed models are derived for two different specifications of the outside good utility – (1) nonlinear utility with respect to consumption and (2) linear utility with respect to consumption. The proposed models are applied to an empirical context to analyze activity-level as well as episode-level activity participation and time allocation while considering bounds on time allocations. Empirical results suggest that the models that consider upper bounds on consumption offer a better fit to data, avoid predictions of unrealistically large time allocations, and result in overall better predictions than those from models without bounds. The proposed models are useful in situations, such as microsimulation models of travel demand, where it is crucial to avoid unrealistically large predictions. [ABSTRACT FROM AUTHOR]

Spatial and traffic planning is important in order to achieve a quality, safe, functional, and integrated urban environment. Different tools and expert models were developed that are aimed at a more objective view of the consequences of reconstruction in different spatial and temporal ranges while respecting selection criteria. In this paper we analyze the application of the multi-criteria analysis method when choosing sustainable traffic solutions in the center of a small town, in this case Beliš´ce, Croatia. The goal of this paper is to examine the possibility of improving the methodology for selecting an optimal spatial–traffic solution by combining the quantifiable results of the traffic microsimulation and the method of multi-criteria optimization. Socially sensitive design should include psychological and social evaluation criteria that are included in this paper as qualitative spatial–urban criteria. In the optimization process, different stakeholder groups (experts, students, and citizens) were actively involved in evaluating the importance of selected criteria. The analysis of stakeholders’ survey results showed statistically significant differences in criteria preference among three groups. The AHP (Analytic Hierarchy Process) multi-criteria analysis method was used; a total of five criteria groups (functional, safety, economic, environmental, and spatial–urban) were developed, which contain 21 criteria and 7 sub-criteria; and the weights of criteria groups were varied based on stakeholders’ preferences. The application of the developed methodology enabled the selection of an optimal solution for the improvement of traffic conditions in a small city with the potential to also be applied to other types of traffic–spatial problems and assure sustainable traffic planning. [ABSTRACT FROM AUTHOR]

In France, married couples or couples in civil partnerships must declare their resources jointly and are allocated two tax units. This tax system, referred to as the marital quotient, represents a financial package of around 10 billion euros. Using the Ines microsimulation model, we simulate three reforms of this system: an individualisation of taxation, a reduction of marital quotient to 1.5 tax units while allowing married couples/couples in civil partnerships to opt for individual taxation and, finally, the capping of the marital quotient at the same level as the family quotient. Individualisation results in the highest tax gain (around 7 billion), compared with 3.8 billion when the marital quotient is reduced to 1.5 tax units and 3 billion with the marital quotient cap. With these reforms, 46%, 45% and 7% of couples lose out, respectively. The median losses correspond to 1.5%, 1.3% and 2.6% of the disposable income of the households concerned, respectively. Finally, 60%, 64% and 83% of the losses are in the last three standard of living deciles, respectively. [ABSTRACT FROM AUTHOR]