State estimation for traffic simulations as coarse grained systems

点击翻译按钮获取中文摘要

Traditional Models and State Estimation Traditional computer based system simulations focus on mathematically well behaved systems that are modeled mainly by (differential) equations and are simulated by well understood numerical methods. Due to the formal nature of such models, sound mathematical techniques for their calibration and tracking have been developed. The most prominent example is arguably the Kaiman filter with its vast number of practical applications particularly in the fields of physics and control engineering. Multi-Agent Simulations Recently, an increasing interest in the modeling of mathematically less well understood rule-based particle systems has emerged and several microscopic computer simulation systems have been developed. This paradigm of simulation represents individual particles and their dynamics as software objects. Currently available state estimation methods lack the capability to deal with such coarse grained processes, which generally are non-differentiable and stochastic. Since micro-simulations will continue to pervade practical sciences, there is considerable need for systematic research on new state estimation methodologies for coarse-grained systems. Application to Traffic Systems Important practical applications of this multi-agent methodology are traffic simulations. The individual representation of travelers as software agents allows for a highly realistic representation of traffic, although again at the cost of mathematical tractability. Since almost all available traffic planning and management tools require current and/or predicted mobility patterns as input data, new methods for state estimation of coarse grained systems have the potential to provide immediate benefits in this field.