Iterative Calibration Method for Microscopic Road Traffic Simulators

Abstract

Recently, computer simulation aided work has become a standard routine in all engineering fields. Accordingly, simulation plays a fundamental role even in road traffic engineering. A reliable simulator is able to provide effective analysis of a given traffic network if the applied simulation scenarios properly converge to the real-word situation. This requirement can be achieved based on the mixed use of prior real-world traffic measurements and proper simulation settings. The latter one, however, is not straightforward. Accordingly, the paper investigates a potential calibration technique to create realistic simulations. Basically, a tuning method with genetic algorithm is proposed to reproduce true-to-life traffic based on floating car speed data. IntroductionIn our days, the conscious transport planning and decision making is expected (Susniené, 2012;Török et al., 2012;Meszaros and Torok 2014). Growing traffic demands together with the increasing motorization induce the continuous maintenance and development of road traffic control systems. In parallel, microscopic road traffic simulators forms an integral part of practical traffic engineering (Yurshevich and Yatskiv, 2014;Kollár, 2014). Appropriate traffic simulations constitute the preparatory working stage in such engineering tasks. Moreover, the applied ITS (intelligent transportation systems) solutions also require the appropriate traffic simulation based analysis. As a consequence, traffic simulators are more and more expected during both the development process and the validation phase as well.On the other hand, the proper use of simulators is also important in order to avoid false results. Namely, beside the several advantages of computer based traffic modeling, simulators also contain the danger of providing erroneous results in case of inappropriate simulation settings in the scenarios. Accordingly, calibration of simulation parameters is expected. Numerous researches have been conducted in this field providing efficient methods to optimize the most important settings, such as travel times, driving behavior parameters, saturation flow rates, etc. These parameters are mostly microscopic variables which can be successfully tuned based on the existing research results, e.g. Columbia River Crossing (2006); Park and Schneeberger (2003);Cunha et al. (2009).Contrary to the foregoing, in this paper, we focus on the tuning of a macroscopic variable exclusively: the traffic demand which is one of the most important network parameter in the microscopic simulation (represented by the intensity of vehicle sources in the simulator). Based on manual or detector measurement (if they are available), traffic demands can be appropriately estimated and therefore set in the simulation scenarios. However, in our days, the booming penetration of floating car data (FCD) creates a different approach. If real-world FCD is available from the road network to model, the average link speeds can be calculated, thus the traffic demand parameters can be efficiently calibrated.

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