Abstract
Carsharing is an essential part of the transformation towards sustainable mobility in smaller urban areas. To expand their services and the positive social and environmental benefits, carsharing operators must understand their users' travel behavior. To accelerate this understanding, we analyze usage data of a station-based carsharing service from a small city in Germany with machine learning and explainable artificial intelligence to reveal influencing factors on the trip distance. The resulting four overarching groups are personal characteristics, time-related, car-related, and environmental features. We further analyze the driving distance of several subgroups split by personal and time-related features. Our findings highlight the importance of time-related features for the trip distance of carsharing users in all subgroups. We also discuss the influence of non-time-related features on the user groups. With these results, we derive valuable insights for research and carsharing operators by understanding patterns in individual user behavior in smaller urban areas.
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