Calibration Of Simulation Models Research Articles

Integrated Method for Online Calibration of Real-Time Traffic Network Management Systems

This paper presents an integrated method for online calibration of realtime traffic network simulation models. The method integrates a time-dependent demand adjustment module and a link-based traffic flow propagation model calibration module. These modules use available realtime traffic observations to minimize inconsistency between the model estimation results and real-world observations. The modules are integrated into a real-time traffic network management system that was developed for the US-75 corridor in Dallas, Texas. Results illustrate that the online calibration method is effective in enhancing the model's consistency in the different operational conditions.

W–SPSA in Practice: Approximation of Weight Matrices and Calibration of Traffic Simulation Models

The development and calibration of complex traffic models demands parsimonious techniques, because such models often involve hundreds of thousands of unknown parameters. The Weighted Simultaneous Perturbation Stochastic Approximation (W–SPSA) algorithm has been proven more efficient than its predecessor SPSA (Spall, 1998), particularly in situations where the correlation structure of the variables is not homogeneous. This is crucial in traffic simulation models where effectively some variables (e.g. readings from certain sensors) are strongly correlated, both in time and space, with some other variables (e.g. certain OD flows). In situations with reasonably sized traffic networks, the difference is relevant considering computational constraints. However, W–SPSA relies on determining a proper weight matrix (W) that represents those correlations, and such a process has been so far an open problem, and only heuristic approaches to obtain it have been considered.This paper presents W–SPSA in a formally comprehensive way, where effectively SPSA becomes an instance of W–SPSA, and explores alternative approaches for determining the matrix W. We demonstrate that, relying on a few simplifications that marginally affect the final solution, we can obtain W matrices that considerably outperform SPSA. We analyse the performance of our proposed algorithm in two applications in motorway networks in Singapore and Portugal, using a dynamic traffic assignment model and a microscopic traffic simulator, respectively.

Transport Simulation Model Calibration with Two-Step Cluster Analysis Procedure

The calibration results of transport simulation model depend on selected parameters and their values. The aim of the present paper is to calibrate a transport simulation model by a two-step cluster analysis procedure to improve the reliability of simulation model results. Two global parameters have been considered: headway and simulation step. Normal, uniform and exponential headway generation models have been selected for headway. Application of two-step cluster analysis procedure to the calibration procedure has allowed reducing time needed for simulation step and headway generation model value selection.

Driving Type Categorizing and Microscopic Simulation Model Calibration

Microsimulation programs, before being used for traffic analysis in practice, should be calibrated to reproduce the traffic characteristics and to represent the driver population in reality. A driver behavior questionnaire survey conducted in China revealed that significant differences in driving behavior existed in drivers. Calibration should be done by taking these differences in driving style into account. In this study, 30 drivers in Changsha, China, were invited to participate in in-car tests and complete a questionnaire. Factor analysis revealed the interdependencies between observed acceleration or deceleration characteristics and other variables. Four driving types were classified according to the four factor scores. Further analysis of repeated trips validated the consistency of driver membership within a class. A censoring method was applied to calibrate the desired acceleration functions and desired speed distributions in a simulation model for these four driving types. Distinct differences were found in desired speed and maximum desired acceleration or deceleration of the four driving types. It is expected that the categorization of driving types will be useful in model calibration.

Evaluation of a multi-stage guided search approach for the calibration of building energy simulation models

This paper is focused on increasing the knowledge on methods for calibrating BES models and to get more insights of different approaches for the optimization of the calibration process. The paper will be centred in the evaluation of a multistage guided search approach. It defines an iterative optimization procedure which starts with the assignment of probabilistic density functions to the unknown parameters, followed by a random sampling and running batch of simulations. It then finishes with an iterative uncertainty and sensitivity analysis combined with a re-assignment of the ranges of variation of the strong parameters. The procedure converges when no new influencing parameters are found. This method is applied to a real case study consisting of an unoccupied office building located in Lleida (Spain). The measured indoor temperature has been used to determine the uncertainty and precision of the method. The effect of the size of the sampling, the number of iterations and the parameters of the global sensitivity method are analyzed in detail. The results of this paper exemplify the degree of accuracy of multistage guided search approaches, and illustrate the reasons how these analyses can contribute to the improvement of more refined calibration methods.

Simulation based calibration using extended balanced augmented empirical likelihood

This paper introduces an extension of the balanced augmented empirical likelihood (eBAEL) method for calibrating simulation models. We illustrate the efficiency of our method in two simulation studies, where we calibrate moments of different distributions and parameters of a geometric Brownian motion process, comparing our approach against other simulation based methods. In these benchmark experiments we observe converging mean squared errors of the empirical likelihood approach. In fact, the results demonstrate that the eBAEL approach is able to provide the best mean squared errors for calibration and in particular is the most robust calibration method, particularly in the presence of noise.

Global sensitivity analysis techniques to simplify the calibration of traffic simulation models. Methodology and application to the IDM car‐following model

As models are simplifications of reality, the management of the uncertainty arising along the whole modelling process is a crucial and delicate operation, which primarily affects the credibility of model results. In the field of traffic simulation to tackle this issue, it is common practice to include the model uncertainty alongside the uncertainty in the parametric inputs. However, reducing the uncertainty in the modelling process through the indirect estimation of the model parameters is far from being simple. In this picture, a key role can be played by model sensitivity analysis. In the present work, in particular, the role of sensitivity analysis in the management of modelling uncertainties is firstly illustrated. Then, one of the most advanced techniques to perform sensitivity analysis is explained and applied to identify, in the specific context of application, which of the input factors of two car-following models can be fixed without appreciably affecting a specific output of interest. Results confirmed the relevance of sensitivity analysis in driving analysts' activities for models' comprehension, calibration and validation, namely, for their appropriate use.

Impact of bulk traps in GaN buffer on the gate-lag transient characteristics of AlGaN/GaN HEMTs

The impact of bulk traps in GaN buffer on the transient characteristics of AlGaN/GaN HEMTs is studied based on two-dimensional (2-D) numerical simulations. Starting from the calibration of simulation model compared with the experimentally measured data, we carried out both the pulse-on and pulse-off gate-lag transient simulations. The simulation reproduced the experimental result, suggesting that the rising trend of the turn-on pulsed gate-lag transient current in the experiment can be explained with bulk traps. It is proved that the bulk acceptor trap in GaN buffer can play an important role in the gate-lag transient response of GaN-based HEMTs besides the surface and interface traps. Bulk traps which cannot be eliminated by passivation should be taken into account for modeling. The mechanism of bulk trapping effect in buffer is analyzed in this paper, which may benefit the future work for design and model development of these devices. Furthermore, the influence of trap filling time on the drain current collapse is investigated.

Determination of Mini-Roundabout Capacity in the United States

AbstractThe design of mini-roundabouts has been around and practiced in Europe for decades. It has been a successful and low-cost intersection configuration using existing external boundaries of intersections. Nevertheless, accessible traffic capacity models for mini-roundabouts do not exist. This study provides design recommendations and a simulation approach for capacity models of mini-roundabouts from U.S. data. Two geometries are selected that have a 7 or 10 m (24- or 36-ft) approach width typical of conventional intersections. The mini-roundabouts are best defined by the inscribed circle diameter of 15 and 23 m (50 and 75 ft). Field data are collected on drivers’ behavior and decisions for a mini-roundabout design in order to calibrate simulation models. A microscopic traffic simulation software is used to model the selected prototype designs for capacity estimations. The defining feature for mini-roundabouts is the traversable central and splitter islands for large vehicles that make through or left...

A Sensitivity-Analysis-Based Approach for the Calibration of Traffic Simulation Models

In this paper, a multistep sensitivity analysis (SA) approach for model calibration is proposed and applied to a complex traffic simulation model with more than 100 parameters. Throughout this paper, it is argued that the application of SA is crucial for true comprehension and the correct use of traffic simulation models, but it is also acknowledged that the main obstacle toward an extensive use of the most sophisticated techniques is the high number of model runs usually required. For this reason, we have tested the possibility of performing a multistep SA, where, at each step, model parameters are grouped on the basis of possible common features, and a final SA on the parameters pertaining to the most influential groups is then performed. The proposed methodology was applied to an urban motorway case study simulated using MITSIMLab, a complex microscopic traffic simulator. The method allowed the analysis of the role played by all parameters and by the model stochasticity itself, with 80% fewer model evaluations than the standard variance-based approach. Ten model parameters accounted for a big share in the output variance for the specific case study. A Kriging metamodel was then estimated and integrated with the multistep SA results for a global calibration framework in the presence of uncertainty. Results confirm the great potential of this approach and open up to a novel view for the calibration of a traffic simulation model.

Utilizing artificial neural network to predict energy consumption and thermal comfort level: An indoor swimming pool case study

This paper presents an ANN approach to predict energy consumption and thermal comfort level of an indoor swimming pool. In Swimming pool, several environmental and control variables, directly or indirectly, affect energy consumption and thermal comfort, rendering difficult the development of a mathematical relationship amongst input and output variables. Thus, ANN based prediction approach is used to elicit this relationship within reasonable period of time. This forms the basis of an optimization based control system to evaluate the control parameters in the swimming pool. The proposed approach is implemented for a specific HVAC system, based on scenarios developed in close consultation with site engineers and domain experts. Due to lack of meaningful historical monitored data, a calibrated simulation model is used to generate large amount of data sets to train the corresponding ANN prediction engine. The trained ANN was then calibrated in real conditions and used as a cost function in an optimization program to help achieve energy saving targets. Several ANN algorithms have been tested and benchmarked leading to the selection, with further tuning, of the best performing ANN algorithm, namely Levenberg–Marquardt algorithm. The latter was used and achieved good results as demonstrated in the selected case study.

Modification of the SCE-UA to Include Constraints by Embedding an Adaptive Penalty Function and Application: Application Approach

Evolutionary algorithms that are commonly used for automatic calibration of watershed runoff simulation models are unconstrained optimization algorithms. The watershed runoff phenomenon, however, is quite complex, so there are some limitations to the calibration of such models with a single-objective function. The purposes of this study are to improve the shuffled complex evolution-University of Arizona (SCE-UA) to include constraints and to develop an automatic calibration module of the SWMM (storm water management model). An adaptive penalty function was used to impose constraints on the SCE-UA. Two constraints are imposed to diminish errors of peak flow and peak time on a watershed runoff event simulation. We applied the new automatic calibration module to a watershed runoff event simulation for the Milyang Dam Basin in Korea. The automatic calibration results that included the constraints showed improvement in reducing errors of peak flow and peak flow occurrence time. The overall shapes of flood hydrographs were also more similar to observed hydrographs than those of automatic calibration results without the constraints.

“No Free Lunch” Theorems Applied to the Calibration of Traffic Simulation Models

In 1997, Wolpert and Macready derived “No free lunch theorems for optimization.” They basically state that “the expected performance of any pair of optimization algorithms across all possible problems is identical.” This is to say that there is no algorithm that outperforms the others over the entire domain of problems. In other words, the choice of the most appropriate algorithm depends upon the specific problem under investigation, and a certain algorithm, while providing good performance (both in terms of solution quality and convergence speed) on certain problems, may reveal weak on certain others. This apparently straightforward concept is not always acknowledged by optimization practitioners. A typical example, in the field of traffic simulation, concerns the calibration of traffic models. In this paper, a general method for verifying the robustness of a calibration procedure (suitable, in general, for any simulation optimization) is proposed based on a test with synthetic data. The main obstacle to this methodology is the significant computation time required by all the necessary simulations. For this reason, a Kriging approximation of the simulation model is proposed instead. The methodology is tested on a specific case study, where the effect on the optimization problem of different combinations of parameters, optimization algorithms, measures of goodness of fit, and levels of noise in the data is also investigated. Results show the clear dependence between the performance of a calibration procedure and the case study under analysis and ascertain the need for global solutions in simulation optimization with traffic models.

1-D Simulation of a Diesel Engines Injection Timing

Using GT-POWER to simulate a diesel engines injection timing, calibrating simulation model has been done according to experimental data. Analysis of the effect on engine power characteristics and emission characteristics caused by different pre-injection timing and main injection timing is the main work. The simulation results show that, the output torque and NOx increased when the injection timing is advanced. In contrast, the soot emission is decreased when the injection timing is advanced.

Vehicle Tracking System based on Videotaping Data

The relevance of microscopic traffic simulation models for the analysis of traffic operations and safety is based on their ability to reproduce real time traffic operations at a given location. Accordingly, one of the major steps in the development and application of these models is a good calibration of the model input parameters based on a thorough comparison of simulated and observed vehicle trajectories. Videotaping provides a low cost nonintrusive procedure for capturing individual vehicle operations over time, and as such, provides a useful tool for obtaining observational data for calibration and validation of traffic simulation models. Before videotaped data can be used, however, vehicle tracks or trajectories will need to be extracted from a frame by frame analysis of vehicle progression measurements concerning longitudinal and lateral position, speed, and acceleration over time. A system for tracking moving vehicles is presented that overcomes many of the practical limitations of current videotaping applications usually resulting from traffic and site conditions for the road segment being videotaped. The data extraction algorithm proposed in this paper provides a more flexible (less restrictive) method for videotaping of vehicle in the traffic stream, which attempts to overcome many of the limitations imposed by other more restricting taping methods currently in use. However, the accuracy of the vehicle tracking system needs to be assessed. This necessitates a comparison of individual vehicle trajectories as extracted from the video with the corresponding profiles obtained from baseline ground control points (GCP) referenced values for the same trajectories. This study has two objectives: 1) to introduce and describe the video extraction algorithm that allows vehicle segmentation and tracking, and the computation of traffic parameters from the tracking data, 2) to assess the accuracy of this algorithm with respect to changes in the horizontal viewing angle, and the number and placement of GCP along a given road segment. Preliminary results obtained from a case study shown that the number of GCP and the deflection angle from the perpendicular camera sightline to the roadway have a significant effect on the accuracy of the detected vehicle trajectories. Furthermore, the placement of GCP along the road segment has a significant effect on error, especially as it affects the scale of pixels at the edge of the video angle.

Numerical Modeling and Leakage Reduction in the Water Distribution System of Udine

The paper describes the effective achievements in terms of water supply and water resources management obtained by AMGA S.p.A. within the GAP-UK Project (Interreg IV Programme). Two main issues are addressed, namely, the calibration of the numerical simulation model and the water loss reduction which has been obtained after extensive leakage identification and repair campaigns performed all over the network. In particular, the priority of leakage identification and repair has been assigned to those sectors affected by a high rate of water loss, as a result of modeling and monitoring, thus confirming some model prediction. All the identified leakages have been repaired and the effect on the global water balance of the system is evident, characterized by a minimum night flow which passed from 215 L/s down to 165 L/s.

R&D networks and regional knowledge production: an agent-based simulation of the Austrian competence centres programme

fer to long-term scenarios regarding the level and duration of public funding. By addressing the complexities of knowledge interaction in the context of the “local buzz” versus “global pipelines” discussion, the results show the potential of empirically calibrated simulation models for ex-ante impact assessment in R&D policy.

Validation and Calibration of a Computer Simulation Model of Pediatric HIV Infection

BackgroundComputer simulation models can project long-term patient outcomes and inform health policy. We internally validated and then calibrated a model of HIV disease in children before initiation of antiretroviral therapy to provide a framework against which to compare the impact of pediatric HIV treatment strategies.MethodsWe developed a patient-level (Monte Carlo) model of HIV progression among untreated children <5 years of age, using the Cost-Effectiveness of Preventing AIDS Complications model framework: the CEPAC-Pediatric model. We populated the model with data on opportunistic infection and mortality risks from the International Epidemiologic Database to Evaluate AIDS (IeDEA), with mean CD4% at birth (42%) and mean CD4% decline (1.4%/month) from the Women and Infants’ Transmission Study (WITS). We internally validated the model by varying WITS-derived CD4% data, comparing the corresponding model-generated survival curves to empirical survival curves from IeDEA, and identifying best-fitting parameter sets as those with a root-mean square error (RMSE) <0.01. We then calibrated the model to other African settings by systematically varying immunologic and HIV mortality-related input parameters. Model-generated survival curves for children aged 0-60 months were compared, again using RMSE, to UNAIDS data from >1,300 untreated, HIV-infected African children. ResultsIn internal validation analyses, model-generated survival curves fit IeDEA data well; modeled and observed survival at 16 months of age were 91.2% and 91.1%, respectively. RMSE varied widely with variations in CD4% parameters; the best fitting parameter set (RMSE = 0.00423) resulted when CD4% was 45% at birth and declined by 6%/month (ages 0-3 months) and 0.3%/month (ages >3 months). In calibration analyses, increases in IeDEA-derived mortality risks were necessary to fit UNAIDS survival data. ConclusionsThe CEPAC-Pediatric model performed well in internal validation analyses. Increases in modeled mortality risks required to match UNAIDS data highlight the importance of pre-enrollment mortality in many pediatric cohort studies.

Improved calibration of simulation models in railway dynamics: application of a parameter identification process to the multi-body model of a TGV train

This paper aims at estimating the vehicle suspension parameters of a TGV (Train à Grande Vitesse) train from measurement data. A better knowledge of these parameters is required for virtual certification or condition monitoring applications. The estimation of the parameter values is performed by minimising a misfit function describing the distance between the measured and the simulated vehicle response. Due to the unsteady excitation from the real track irregularities and nonlinear effects in the vehicle behaviour, the misfit function is defined in the time domain using a least squares estimation. Then an optimisation algorithm is applied in order to find the best parameter values within the defined constraints. The complexity of the solution surface with many local minima requires the use of global optimisation methods. The results show that the model can be improved by this approach providing a response of the simulation model closer to the measurements.

Development and calibration of a constrained resource health outcomes simulation model of hospital-based glaucoma services

Health service delivery and organization often develops in an ad hoc manner, in response to unplanned changes in referral patterns, technologies and system-level factors. We developed a discrete event simulation model for the analysis of health services in a routine clinical setting, with the objective of predicting the process impact of variations to current forms of service delivery and organization. As a case study, we use glaucoma services provided at the Royal Adelaide Hospital, South Australia. Both clinical and administrative hospital data were used to populate and calibrate the model. A random search strategy of a constrained parameter space identified convergent input parameter sets, representing the current operation of glaucoma services at the hospital. Analyses of alternative service configurations showed that extending the patient booking cycle, and review times for stable patients lowered patient numbers per clinic and decreased delays. Applied health services can be successfully simulated and calibrated, providing a basis for analyses of patient throughput, and potentially the economic evaluation of alternative approaches to organizing such services.