E-bicyclist experiences in motor city: A mixed computational and content analysis approach for understanding sustainable micromobility

As cities worldwide face mounting climate challenges, understanding e-bicycle experiences in car-dependent regions is crucial for sustainable transportation planning. This study examines factors that promote or hinder e-bicycle usage in metro-Detroit, Michigan through an innovative methodological approach combining Content Analysis, Text Mining (TF-IDF), and Biterm Topic Modeling (BTM). Our analysis of open-ended survey responses from current e-bicyclists revealed distinct linguistic and thematic patterns: positive experiences centered around terms like “e-bike” “ride,” and “save” corresponding to thematic categories of car substitution (24%), increased riding (21%), and commuting (21%), while negative experiences concentrated around “driver” and “battery,” reflecting driver hostility (36%) and bike performance issues (28%). The BTM uncovered how these elements interact within cohesive experiential themes, where enhanced mobility, health benefits, and sustainable transportation options reinforce positive experiences, while technical limitations interact with infrastructure deficiencies and social barriers to create compound adoption challenges. Our findings illuminate how e-bicycle adoption in car-centric regions requires addressing interconnected technical, social, and infrastructure factors simultaneously rather than as isolated variables, providing crucial insights for policymakers seeking to promote sustainable transportation transitions in North American cities.