This paper presents our ongoing work towards XAI for Mobility Data Science applications, focusing on explainable models that can learn from dense trajectory data, such as GPS tracks of vehicles and vessels using temporal graph neural networks (GNNs) and counterfactuals. We review the existing GeoXAI studies, argue the need for comprehensible explanations with human-centered approaches, and outline a research path toward XAI for Mobility Data Science.

翻译：本文介绍了我们在移动数据科学应用中实现可解释人工智能（XAI）方面的持续工作，重点关注能够从密集轨迹数据（如利用时序图神经网络（GNNs）和反事实推理的车辆及船舶GPS轨迹）中学习的可解释模型。我们回顾了现有的地理空间可解释人工智能（GeoXAI）研究，论证了采用以人为中心的方法提供可理解解释的必要性，并勾勒出面向移动数据科学的人工智能可解释性研究路径。