How e-bike commuters choose trip routes? An exploratory analysis using both theory driven and data driven approaches

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.