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Extracted driving behavior of human driven vehicles can benefit the development of various applications like trajectory prediction or planning, abnormal driving detection, driving behavior classification, traffic simulation modeling, etc. In this paper, we focus on modeling human driving behavior in order to find simplifications for trajectory planning. Using a time-discrete kinematic bicycle model with the vehicle’s acceleration and steering rate as inputs, we model the human driven trajectories of an urban intersection drone dataset for different input sampling times. While most planning algorithms are using input sampling times below 0.33 s, we are able to model 98.2 % of the human driven trajectories of the investigated dataset with a sampling time of 0.6 s. Using longer input sampling times can result in smoother trajectories and longer planning horizons, and thus more efficient trajectories. In a next step, we analyze the correlations between the input of our model and the current state/last input. Such a priori knowledge could simplify common planning algorithms like model predictive control or tree-search based planners by limiting the action space of the ego-vehicle. We propose nonlinear transformations for steering rate and steering angle to represent correlations between speed, acceleration, steering angle and steering rate. In the transformed space the statistics are very well modeled by multivariate Gaussian distributions. Using a multivariate Gaussian, a fast usable behavior model is extracted which is independent of the environment.