We present a new approach for modeling and analyzing route choice behavior. It is motivated by the need to reduce the complexity of the state-of-the-art models. It is inspired by the simplifications actually done by the people, using representations of their surrounding space. The proposed framework is based on elements designed to mimic the mental representations used by travelers, denoted as Mental Representation Items (MRIs). This paper describes how operational models based on MRIs can be derived and discusses the applications of these models to traffic assignment and route guidance systems. We report estimation results using revealed preferences data to demonstrate the applicability and validity of the approach.