Calibration Of Microscopic Traffic Models Research Articles

A copula-based estimation of distribution algorithm for calibration of microscopic traffic models

The importance of calibration of microscopic traffic models as the main core of traffic simulation software results from the need for more realistic traffic behaviors. The latent essence of several parameters in such models as well as the uncertainties resulting from the noise in the data, make the process of calibration much more complex. Usually, the calibration process is formulated as an optimization problem. Selecting the appropriate solution algorithm due to nonlinear and non-convex nature of the problem is crucial. The importance of the issue is more significant when the matter of calibrating the medium or large-scale simulation model is considered. This is mainly due to the expensive cost that running the simulation models impose. Therefore, applying the current algorithms in which finding the appropriate solutions requires a large number of simulation runs is not deemed suitable. In this paper, an estimation of distribution algorithm based on copula theory has been suggested. In contrast with traditional solution algorithms, in the proposed algorithm complex interaction between parameters of a model has been considered by constructing and sampling from a copula-based probabilistic model. Copulas are functions that describe the dependence structure of a set of random variables and connect multivariate distribution functions to one-dimensional marginal distribution functions. The results indicate that applying an explicit probabilistic model based on copula helps the estimation of distribution algorithm to explore the search space more effectively and efficiently as well as provides the possibility of extracting the knowledge with regard to the structure of the calibration problem through analyzing the probabilistic models that are constructed during the evolution process. Furthermore, this new algorithm has been compared with the genetic algorithm and kernel-based cross-entropy method on synthetic and real trajectory data. The results confirm that the proposed algorithm is more efficient in terms of convergence rate, resource usage and more robust in terms of the probability of finding the global optimal solution.

An efficient soft computing-based calibration method for microscopic simulation models

ABSTRACTIn recent years, vehicular microscopic simulation models have become one of the main tools used by transportation professionals to analyze transportation policies and projects. Effective use of the existing simulation packages is limited by the calibration of specific parameters based on observed real-life conditions. However, because the calibration of the packages is a resource-intensive process, one might resort to using the default parameter values. In this study, a soft-computing based methodology is proposed that considerably reduces the computation time in comparison to other commonly used methods. The proposed methodology is based on a synergistic combination of artificial neural networks (ANN) and genetic algorithms (GA). First, a Latin hypercube sampling method is used to select representative sets of values for the simulation model's calibration parameters. Second, the effect of each set of parameter values on the simulated traffic stream speed is evaluated. Third, an ANN is trained to determine the relationship between the input parameter values and the output vehicular speed. Finally, a genetic algorithm uses the trained ANN to determine the calibration parameters. Applications of the proposed methodology shows that it allows for less time-consuming calibration of microscopic traffic models compared to other commonly used methods.

Capacity Evaluation of Roundabouts in Slovakia

Capacity Evaluation of Roundabouts in Slovakia Two technical regulations for capacity estimation of roundabouts are valid in the Slovak Republic at the present time. Each of these regulations is based on a different theory of capacity estimation which affects the outcome of the capacity of roundabouts and their evaluation. The capacity of various roundabouts was estimated according to both methods in the previous analysis made at our department [3] and in some cases different results of the evaluation were achieved. The question is: which one of the mentioned theories better reflects a real capacity of the roundabout? For this reason some of the existing roundabouts in the town Žilina were selected and they were evaluated according to both methods and a micro simulation using PTV Vissim software. The geometrical parameters of roundabout, real driving behaviour (speed, proportions, acceleration, etc.) and also pedestrian behaviour were taken into account in the microscopic models. The first assumption for capacity evaluation by a microscopic traffic model is a calibration process which is based on traffic surveys results. Thus created models allow several capacity analyses during the peak traffic loading. Then, the results of micro-simulation can be compared with the capacity evaluation according to the technical regulations. The article deals with creation and calibration of microscopic traffic models and with the mentioned comparing of results.