  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.
