Analysis of microscopic behavior models for probabilistic modeling of driver behavior

The accurate simulation and prediction of human behavior is critical in many transportation applications, including safety and energy management systems. Construction of human driving models by hand is time-consuming and error-prone, and small modeling inaccuracies can have a significant impact on the estimated performance of a candidate system. This paper presents a comparative evaluation of several probabilistic microscopic human behavior models from the literature trained on naturalistic data for free-flow, car following, and lane change context-classes on highways. We propose several metrics to quantify model quality and use these metrics to demonstrate that a new class of Bayesian network models outperforms the state of the art.