1,721,174 research outputs found
“No free lunch” Theorems Applied to the Calibration of Traffic Simulation Models
No full textIn 1997, Wolpert and Macready have 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”, that 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 the present 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. 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.JRC.F.8 - Sustainable Transpor
Sensitivity analysis of microscopic traffic flow models: methodology and application
No full textIn this work variance-based techniques for model sensitivity analysis have been discussed and applied to the Intelligent Driver Model (IDM) and the Gipps’ model. Throughout the paper it is argued that the application of such methods is crucial for a true comprehension and the correct use of these models. In particular, concerning the subject of their parameters estimation or calibration.
Important issues arising when setting up a sensitivity analysis have been investigated and commented in the specific application to car-following models. Among these issues, the importance of the characterization of the uncertainty in the inputs, also known as data assimilation, and the influence of the data richness (i.e. the coverage of a wide spectrum of traffic patterns) on the characterization of the parameters significance and on their successive calibration.
The method allowed us to show that both in the case of the IDM and the Gipps’ model, some parameters, which are generally considered fixed in the field literature, account for a high share of the output uncertainty and thus require to be calibrated. Sensitivity indices let us also to evaluate the parsimony of models, intended as the ability to describe reality with a minimum of adjusting parameters
Integration of simulation-based and model-based calibrations of traffic micro-simulation models
No full textHow parameters of microscopic traffic flow models relate to traffic conditions and implications on model calibration
No full textWhile several methodological issues in setting up a calibration process for traffic micro-simulation models are still unresolved, the influence of individual parameters of microscopic models on simulated traffic dynamics is also far from clear. In order to address these issues the paper sets up a methodology based on the sensitivity analysis of traffic flow models (the one here used is AIMSUN). Sensitivity analysis was performed by means of a series of 30 analyses of variance (ANOVA). These were designed to evaluate the effect of parameters on the variance of the simulated outputs, and to draw a general inference about i) the proper interval for the aggregation of measurements, ii) the proper measure of performance (e.g. traffic counts vs. speeds), iii) the proper traffic measurement locations iv) the sub-set of parameters to calibrate. The analysis allowed us to quantify the effect of the single parameters on different traffic phases. For example, it was possible to quantify the extent of the influence of the parameter “reaction time” on simulated outputs in locations where free flow, rather than congested conditions, occurs. The great differences among parameters in affecting the different traffic phases suggested that parameters are likely to be calibrated independently i.e. using data from different locations. The first evidence of the possibility of breaking the calibration problem into two sub-problems is given. This entails great benefits in terms of computational time, given the exponential computational complexity of the calibration problem
Verification of traffic micro-simulation model calibration procedures: analysis of Goodness–of-Fit measures
No full textThe problem of calibrating traffic simulation models comes within the framework of “no free lunch” theorems: solutions to the methodological issues arising when setting up a calibration study cannot be posed independently. In other words, the choice of the algorithm to use, for instance, depends upon the parameters to calibrate, upon the measure of goodness of fit (GoF), and, of course, upon the micro-simulation model applied. This calls for methodologies able to check the robustness of a calibration framework as well as further investigations of the problem, in order to identify possible “classes” of problems to be treated using the same approach. Therefore in the present work, first we describe a general method for verifying a traffic micor-simulation calibration procedure, based on a test with synthetic data. Then we investigate the influence that the choice of a particular GoF measure may have on the results of a calibration exercise. In all, 16 GoF measures are analyzed by using them to create and visualize the objective function of eight different calibration configurations. It was thus also possible i) to test the hypothesis presented in Punzo and Ciuffo (2009) to calibrate different parameters independently, on different time series of measurements and ii) to check the effect on the calibration itself of random errors in traffic measurement. Results show the importance of verifying the calibration procedure with synthetic data before using real measurements. In addition they highlight limitations of some GoF measures as well as give major insights into the topic
Towards global solutions in the calibration of micro-simulation models: a verification approach
No full textIntegration of driving and traffic simulation: issues and first solutions
No full textDriving simulators are very suitable test beds for the evaluation and development of intelligent transportation systems (ITSs). However, the impact of such systems on the behavior of individual drivers can properly be analyzed through driving simulators only if autonomous vehicles in the driving scenario move according to the system under evaluation. This condition means that the simulation of the traffic surrounding the interactive vehicle should already take into account the driver's behavior as affected by the system under analysis. Currently, this loop is not properly tackled, because the effects on individuals and traffic are, in general, separately and, often, independently evaluated. The integration of traffic and driving simulations, instead, may provide a more consistent solution to this challenging evaluation problem. It also opens up new scenarios for enhancing the credibility of both traffic modeling and driving simulation and for their combined development. For instance, because drivers directly interact with driver/traffic models in a driving simulation environment, such models may also be tested against nonnormative behavior, and this case seems the only way to test driver/traffic models for safety applications. Based on this idea, this paper describes the integration of a driving simulation engine known as SCANeR and a traffic-flow microsimulation model known as AIMSUN. Methodological and technical issues of such integration are first presented, and future enhancements for higher consistency of the simulation environments are finally envisaged. © 2006 IEEE.JRC.DDG.H.8 - Sustainability Assessmen
Goodness of fit function in the frequency domain for robust calibration of microscopic traffic flow models
No full textIn the field of traffic simulation, the calibration of uncertain inputs against real data is usually taken to cover both the epistemic uncertainty regarding the un-modeled details of the phenomena and the aleatory not predicted by the models. For this reason, model parameters are usually indirectly estimated within an optimization framework which tries to maximize the fit between real and simulated measures of the traffic system. This is the case, for example, of the calibration of car-following models’ parameters against vehicle trajectory data. Only recently, it has been proven that the capability of the optimization framework to provide the parameters’ values that allow the car-following model reproducing real trajectories at its best is strictly connected to the setting of the optimization framework itself. This, in particular, entails the necessity to carefully choose an appropriate combination of optimization algorithm and measure of goodness of fit (GOF).
In this study, the authors focus attention on this latter issue. Specifically, it is claimed here that the commonly used GOFs are not able to capture the dynamics of the time-series which calibration is performed against. Therefore, a spectral analysis based approach to evaluate the overall performance of the simulation model in the objective function is proposed.
The new measure of goodness of fit is tested in the calibration of the Intelligent Driver Model against synthetic and real trajectory data. Results with synthetic data, in particular, confirm that such a new optimization setting is always able to find the global optimum of the problem.JRC.F.8 - Sustainable Transpor
The Calibration of Traffic Simulation Models : Report on the assessment of different Goodness of Fit measures and Optimization Algorithms MULTITUDE Project – COST Action TU0903
Full text linkIn the last decades, simulation optimization has received considerable attention from both researchers and practitioners. Simulation optimization is the process of finding the best values of some decision variables for a system whose performance is evaluated using the output of a simulation model.
A possible example of simulation optimization is the model calibration. In traffic modelling this topic is particularly relevant since the solutions to the methodological issues arising when setting up a calibration study cannot be posed independently. This calls for methodologies able to check the robustness of a calibration framework as well as further investigations of the issue, in order to identify possible “classes” of problems to be treated in a similar way. Therefore in the present work, first a general method for verifying a traffic micro-simulation calibration procedure (suitable in general for simulation optimization) is described, based on a test with synthetic data. Then it is applied, my means of two different case studies, to draw inferences on the effect that different combinations of parameters to calibrate, optimization algorithm, measures of Goodness of Fit and noise in the data may have on the optimization problem. Results showed the importance of verifying the calibration procedure with synthetic data. In addition they ascertained the need for global optimization solutions, giving new insights into the topic.
Research contained within this paper benefited from the participation in EU COST Action TU0903 MULTITUDEJRC.H.8 - Sustainability Assessmen
Can Results of Car-Following Model Calibration Based on Trajectory Data Be Trusted?
No full textCalibration of car-following models against trajectory data has been widely applied as the basis for studies ranging from model investigation and benchmarking to parameter correlation. Other theoretical issues, such as inter- and intradriver heterogeneity or multianticipative driving behavior, are addressed in such studies. However, few of these studies attempted to analyze and quantify the uncertainty entailed in the calibration process and its impacts on the accuracy and reliability of results. A thorough understanding of the whole calibration problem (against trajectory data), as well as of the mutual effect of the specific problems raised in the field literature, does not yet exist. In this view, a general methodology to assess a calibration procedure was proposed and applied to the calibration of the Gipps car-following model. Compact indicators were proposed to evaluate the capability of a calibration setting to find the known global solution regarding the accuracy and the robustness of the variation of the starting conditions of the optimization algorithm. Then a graphical inspection method, based on cobweb plots, was proposed to explore the existence and nature of the local minima found by the algorithms, as well as to give insights into the measures of performance and the goodness-of-fit functions used in the calibration experiments. The methodology was applied to all calibration settings (i.e., combinations of algorithms, measures of performance, and goodnessof- fit functions) used in the field literature so far. The study allowed the highlighting and motivation, for the model under investigation, of the limits of some of these calibration settings. Research directions for the definition of robust settings for the problem of car-following model calibration based on real trajectory data are outlined.JRC.F.8 - Sustainable Transpor
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