Gaining insights into how cyclists choose their routes is one major key to improving infrastructure and promoting cycling. Particularly commuters and e-bike users have been noted to have preferences unconstrained from shortest path logic. To that end, this paper aims to uncover cyclists' preferences and the affecting nature-related attributes of routes for commuters to high school in Nijmegen, Netherlands. Based on a Dutch dataset the study analyzed 1284 e-bike cycling trips, each with four route alternatives, including the chosen one. The primary objective is to identify the most influential parameters affecting e-bike commuters' route choices and understand their contributions. The approach employed both a simple path size Logit (PSL) and a Pairwise Combinatorial Logit (PCL) model, incorporating nature-related and interaction variables. Additionally, the research compared the predictive performance of Logit-based models with deep learning models. The findings shed light on the factors influencing e-bike commuters' route choices and demonstrate the superior predictive capabilities of deep learning techniques, with a validation accuracy of 80.16%. By adopting a sensivitity analysis approach, we uncovered the key factors that influence our deep learning model in predicting cycling routes, giving a precise interpretation of our results. Our findings show that commuter e-bike cyclists prefer shorter routes with fewer traffic lights and favor routes that have more natural settings. However, their primary concern is efficiency in their commutes.