Modeling the choice of shared micro-mobility services using XGBoost machine learning algorithm

Abstract

Abstract. In recent years, shared micro-mobility services (e.g., bikes, e-bikes, and e-scooters) have been popularized at a rapid pace worldwide, which provide more choices for people’s short and medium-distance travel. Accurately modeling the choice of these shared micro-mobility services is important for their regulation and management. However, little attention has been paid to modeling their choice, especially with machine learning. In this paper, we explore the potential of the XGBoost model to model the three types of shared micro-mobility services, including docked bike, docked e-bike, and dockless e-scooter, in Zurich, Switzerland. The model achieves an accuracy of 72.6%. Moreover, the permutation feature importance is implemented to interpret the model prediction. It is found that trip duration, trip distance, and difference in elevation present higher feature importance in the prediction. The findings are beneficial for urban planners and operators to further improve the shared micro-mobility services toward sustainable urban mobility.