Street Semantic Tree: A knowledge-driven GeoAI framework for urban e-scooter ridership classification

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.

近年来，地理空间人工智能（GeoAI）已成为城市出行模式挖掘与理解的核心技术体系。然而，传统深度学习模型受限于较高的数据依赖性与有限的可解释性。本研究提出知识驱动语义树（KD-ST）模型，这是一种将结构化语义描述与图学习相结合的新型GeoAI框架，旨在提升电动滑板车骑行客流分类任务中的地理空间建模效果。通过将街道知识结构融入GeoAI模型架构，KD-ST弥合了纯数据驱动方法与知识赋能城市分析之间的鸿沟，同时提升了分类性能与模型透明度。我们在美国四座主要城市——奥斯汀、菲尼克斯、丹佛及华盛顿哥伦比亚特区——开展了案例研究，以评估所提出的KD-ST模型的性能。实验结果显示，该模型的F1分数相较于基线模型提升了12.1%至156.5%。此外，为提升模型的透明度与可靠性，我们提取了关键内部参数，对学习得到的层级知识结构进行可视化与分析。研究结果表明，领域知识可为深度学习模型的设计提供有效信息，并能提升模型性能。进一步而言，该模型在城市背景更为相似的场景中展现出更优的跨城市迁移性，这为电动滑板车规划者的模型选择提供了极具价值的参考依据。