Route choice models are difficult to design and to estimate for various reasons. In this paper we focus on issues related to data. Indeed, real data in its original format are not related to the network used by the modeler and do therefore not correspond to path definitions. Typical examples are data collected with the Global Positioning System (GPS) or respondents describing chosen itineraries to interviewers. Data manipulation is then necessary in order to obtain network compliant paths. We argue that such manipulations introduce bias and errors and should be avoided. We propose a general modeling framework that reconcile network-free data with a network based model without data manipulations. The concept that bridges the gap between the data and the model is called Domain of Data Relevance and corresponds to a physical area in the network where a given piece of data is relevant. We illustrate the framework on simple examples for two different types of data (GPS data and reported trips). Moreover, we present estimation results of Path Size Logit and Subnetwork models based on a dataset of reported trips collected in Switzerland. The network is to our knowledge the largest one used in the literature for route choice analysis based on revealed preferences data.