Development of an enhanced base unit generation framework for predicting demand in free-floating micro-mobility

Accurate demand forecasting has become increasingly necessary in the burgeoning field of free-floating micro-mobility systems. However, for model training, the service area must be divided into specific areal units, which often involves grid-based methods. Although these methods are feasible and provide a uniform area division, they are highly susceptible to the Modifiable Areal Unit Problem (MAUP), which is a critical issue in spatial data analysis. Although MAUP can adversely affect predictive model learning, studies addressing this issue are scarce. Therefore, a novel base areal unit generation algorithm is proposed that employs a clustering approach to enhance the prediction accuracy in free-floating micro-mobility system demand. The method identifies suitable base areal units by merging smaller ones while considering the similarities in temporal usage patterns and distances between different areas, mitigating the impact of MAUP during model learning. The approach was evaluated using shared e-scooter data from two cities, Kansas City and Minneapolis, and it was compared to the traditional grid method. The findings indicate that the proposed framework generally improves prediction performance within the newly defined areal units.