E-scooter Knowledge Driven AI Data & Code

Recently, geospatial artificial intelligence (GeoAI) has risen as a set of essential technologies for urban mobility pattern mining and understanding. However, traditional deep learning models are constrained by their high data dependency and limited interpretability. This study introduces the Knowledge-Driven Semantic Tree (KD-ST) model, a novel GeoAI framework that integrates structured semantic descriptions with graph-based learning to enhance geospatial modeling on e-scooter ridership classification. By incorporating a street knowledge structure into the GeoAI model architecture, KD-ST bridges the gap between purely data-driven methods and knowledge-informed urban analytics, improving classification performance and model transparency. We conducted case studies in four major U.S. cities, including Austin, Phoenix, Denver, and Washington, D.C., to evaluate the proposed KD-ST model's performance. The proposed model outperformed baseline models by 12.1% to 156.5% as for the F1 score. Moreover, to enhance transparency and reliability, key internal parameters were extracted to visualize and analyze the learned hierarchical knowledge structure. Results indicate that domain knowledge provides useful information for the design of deep learning models and improves model performance. Furthermore, the model achieved higher transferability among cities with more similar urban contexts, which provides valuable insights for e-scooter planners on model choice.